Image Analysis and Classification Techniques using ArcGIS 10

Prepared by Pari Ranade and Ayse Irmak

GIS in Water Resources

Fall 2010



The purpose of this exercise is to perform typical tasks of remote sensing analysis using ArcGIS 10. We will perform georeferencing, image interpretation and image classification. We will use Google captured image as an example for georeferencing. Landsat 5 image for image interpretationa and image classification. With this exercise you will –


1. Capture image from Google for Lincoln are and Georeference it.

2. Perform Image analysis and identify features in the Landsat 5 images of July and Feb for Lancaster County. (display false and true color composites)

3. Compute NDVI

4. Could mask the image

5. Perform supervised and unsupervised classification

6. Compare classified map with existing NASS and CALMIT landuse


Computer and Data Requirements

To carry out this exercise, you need to have a computer, which runs the ArcInfo version of ArcGIS 10. There is no need for remote sensing software. The data are provided in the accompanying zip file,  


Downloading Landsat data

Landsat data can be downloaded for free from or USGS global visualization viewer facilitates viewing and downloading of satellite imagery. USGS provides Landsat 5 TM data in TIFF format. This data can be converted into other formats using ArcGIS or any image processing software (i.e. ERDAS-IMAGINE, ENVI). Landsat satellite passes over earth, the area can be identified by the path and row combination. Our study area is under the path 28 row 32. This is southeastern part of Nebraska and Landsat passes over this area at approximately 10:15 CST. Each Landsat scene has a unique identifier. Our dataset has identifier LT50290322005251EDC00. All the information regarding Landsat data can be found in Header file of Landsat data.  These files will end with _GCP and _MTL. These files also contain the information regarding each band pertinent to that scene. Useful information like center and corner coordinates of Landsat scene is also provided in the header file. A screen shot of sample attributes for this Landsat scene is provided below.


Landsat 5 TM has 7 different bands. These bands are useful for extracting various information related to vegetation, temperature, clouds, soil moisture, biomass, rocks, minerals and so on.  Below are details of band designation for Landsat 5 TM. More detailed information can be found at



Spectral Bands1

Wavelength  (micrometers)

Potential Information Content

Resolution (meters)

Band 1


0.45 - 0.52

Discriminates soil and rock surfaces from vegetation. Provides increased penetration of water bodies


Band 2


0.52 - 0.60

Useful for assessing plant vigor


Band 3


0.63 - 0.69

Discriminates vegetation slopes


Band 4

  Near IR

0.76 - 0.90

Biomass content and shorelines


Band 5

  Mid  IR

1.55 - 1.75

Discriminates moisture content of soil and vegetation; penetrates thin clouds.


Band 6

  Thermal IR

10.40 - 12.50

thermal mapping and estimated soil moisture


Band 7

  Mid  IR

2.08 - 2.35

Mapping hydrothermally altered rocks associated with mineral deposits


1: IR stands for infrared


Various composites can be mapped to facilitate viewing of the raw imagery. A false color composite of near infrared is useful for observing spatial distribution of the vegetation in the Landsat scene by combining bands 2, 3 & 4 (RGB). We are going to generate the true color composite by using spectral bands 2 (green), 3 (red), and 4 (near infrared).


Part I – Georeferencing


Raster data or imagery served by various agencies usually has spatial reference. However sometimes the location information delivered with them is inadequate and the data does not align properly with other data you may have. Sometimes raster data is digitized using scanned paper maps and co-ordinate system needs to be assigned.  Thus, to use some raster datasets in conjunction with your other spatial data, you may need to align, or georeference, to a map coordinate system. Georeferencing raster data allows it to be viewed, queried, and analyzed with other geographic data.

In this exercise, we will use Google map to as a source raw image to be georeferenced. With ArcMap 10 we can use Bing maps directly in ArcMap as basemaps. But google map is just an illustration of how we can use our own map and add spatial reference to it. The mapped could be scanned image of your field map or any other scanned hard copy map.


Go to Search for Lincoln, NE. Click on the Satellite option on upper right corner of google map. On the Northwest side of the map you will see Capitol Beach Lake near I-80. Interstate 80 is a major transcontinental corridor connecting California and New York City.



Go to New! On upper right corner of the webpage. Enable LatLng Marker.  This will enable to mark the longitude and latitudes of the locations of the map. You can also enable LatLng Tooptip as well if you wish. Right click on the point where you need to mark the lat-long select drop latlng option. To zoom in the specific are using right click and select zoom in option. It is handy compared to zoom in bar on the Google.



Raster is georeferenced using existing spatial data (target data), such as a vector feature class, that resides in the desired map coordinate system. The process involves identifying a series of ground control points—known x,y coordinates—that link locations on the raster dataset with locations in the spatially referenced data (target data). Control points are locations that can be accurately identified on the raster dataset and in real-world coordinates. There are many different types of features that can be used as identifiable locations, such as road or stream intersections, the mouth of a stream, rocky outcrops, the end of a jetty of land, the corner of an established field, street corners, or the intersection of two hedgerows.  


Similar to shown in figure, try to identify the control points which are unique and easy to locate on the map.  Try to avoid control points which are too close to each other. Also try to find control points from all regions of the image. Capture the screen using PrintScreen button on keyboard and paste it in Paint and save it as an image. Paint can be opened on windows PC from Start/Programs/Accessories/Paint. Note down the Latitude and longitude information for each control point and save the image as ControlPts.TIF. This will be your reference if you note the control points incorrectly.



Close the LatLng Tooltip and uncheck the Show lables option under satellite.  Again capture the screen, open the paint and paste the image. Crop the image to clean the sides of the image. You have been provided with Lancaster county feature class, NASS and CALMIT land use maps for comparison after we georeference the Google captured image. Entire data is under Lancaster Geodatabse in Ex6 folder. Go to ArcCatalog and under the Ex 6 folder create and new personal geodatabase name it as EX6.   Save the image as TIFF format with name Capitol Beach under EX6 personal geodatabase. Add the Capitol Beach.TIF to ArcMap. Now you will see clear satellite image of Capitol Beach without any labels or location information.




















Open ArcMap and set the coordinate system of the frame for proper alignment. Right click on layers. Go to Properties\coordinate system/import. Select the Lancaster_County polygon in Lancaster geodatabase.



Now we have identified the control points with Lat Long information, we will import them in ArcGIS. Open ArcCatalog. Right click on Ex6 geogatabase, select new/feature class. Name it as Control_Points alias Control_Points . Select point feature type. Select the Spatial reference in Geographic Coordinate System/World/WGS 1984.prj. Leave rest of the options as default. This is the coordinate system that Google uses.



Go to Toolbars/Editor to open editor toolbar.



Start Editing the Control_Points. Open the Capitol Beach.TIF that you have saved under Ex6 geodatabse.  You might receive warning about unknown spatial reference. Click OK.



In create feature window choose create control points layer and under construction tools choose point. Right click on any point within layer and select Absolute X,Y…Type X (lat) and Y (Long) that you have recorded from Google LatLng marker for each control point.



Complete the same procedure for all the control points that you have selected. If you enter the wrong LatLong information, you can delete the point and add it again. Stop editing and save your edits. Now you have completed the control points feature layer. This was to illustrate one way of adding control points. Another simpler way is to add control points using Make XY event layer tools from text or excel table. Tool is located under data management\layers and table views. You will later export this layer as point feature class with the name Control_Points in Ex6 geodatabase.


Now we will start georeferencing the Capitol Beach.TIF. Go to customize/toolbars/georeferencing. Make sure Capitol beach layer is selected for georeferencing. Click on add control points  button.



This button will link our Control_Point features to the same location on the Capitol Beach.TIF, Which means we will kind of assign spatial reference to the control points on Capitol Beach.TIF. ArcGIS then will fit the surface and assign spatial reference to entire image. 





Now let’s start adding the control points . Make sure you Auto Adjust option is on both on Georeferencing toolbar and on Link table. Link table can be opened by clicking on  button on Georeferencing toolbar.


You might not be able to see anything in full extent since, on Capitol Beach.TIF is missing a spatial reference. Right click on Capitol Beach.TIF layer click on Zoom to Layer. Now identify the location of one of control point on the image and click on the location on image. Here you can refer to your ControlPts.TIF image where you have saved the LatLong information of each control point on image.


Now right click on the Control_Point layer and ‘Zoom to Layer’ (your cursor will be showing line, disregards that). Now you will see all the control points. Click on the control point which corresponds to point on the image, now ArcGIS snapping environment will be automatically activated and your control point will be snapped. Notice your both layers are now closer. Continue adding the control points. You might receive the warning that ‘The control points are collinear or not well distributed, this will affect the warp result’ Click OK and see how much of warping (shift) is caused. If you think image is warped too much, it indicates you have misplaced the control point.


If you misplace a control point, simply click on the view link table button   and delete the control point (selected control point will be in highlighted in blue as shown in figure). Notice once you have added the second control point you Capitol Beach.TIF and Control_Point layers will be aligned. This is due to Auto adjust function. Now finish all the control points.




Open the View link table. Make sure Auto adjust option is checked. Compare the transformation options – 1st order polynomial (affine), 2nd order polynomial and adjust. See which image looks most similar to your original image from Google. Select the best fitted transformation and click OK.


Q. Turn in the image with all three Transformation option. Label the image to show which transformation is used.


Click on the Update georeferencing option under georeferencing toolbar. Next click on the Rectify option on georeferencing toolbar. Change the Name of the file to ‘Captiol_Beach_Rect’ and Output location to Ex6 geodatabase. Leave rest of the option to default and click SAVE.



Remove Control_Points layers and Capitol_Beach layer from ArcMap. Open the ArcCatalog. Right click on the Captiol_Beach_Rect image from Ex6 geodatabse that you have just saved. (Note: sometime you might have to reopen the ArcCatalog if it was already open, or refresh it from View/refresh or use F5 key on keyboard). Notice your file now has a spatial reference GCS_WGS_1984 and datum D_WGS_1984, which is same as Google data.


Open Captiol_Beach_Rect image in ArcCatalog from Ex6 geodatabse. Now we need to reproject it in the Nebraska State Plain coordinate system (NAD 1983 StatePlane Nebraska FIPS 2600 Feet) in which all the Lancaster county data is.


Search for Project raster under tools in ArcGIS. It is located under Data management tools/projections and transformations/raster/project raster. Save the output raster data with the name ‘Captiol_Beach_Rect_Proj.img’ under Ex6 geodatabase. Click on the output coordinate system and import from CALMIT_2005 layer from Lancaster geodatabase. Now it should show as ‘NAD_1983_StatePlane_Nebraska_FIPS_2600_Feet’.  Select the Geographic transformation as ‘WGS_1984_(ITRF00)_To_NAD_1983’Leave the rest of the options to default.



Captiol_Beach_Rect_Proj’ layer will be added after the tool has run. Wait till it runs completely and you receive successful message. Remove the ‘Captiol_Beach_Rect’ layer from the ArcMap. (Make sure your the coordinate system of the frame is Nebraska State Plane. Else right click on layers. Go to Properties\coordinate system/import. Select the Lancaster_County polygon in Lancaster geodatabase. )

Add ‘Lancaster_County’, ‘CALMIT_2005’ and ‘NASS_2005’ layers from Lancaster geodatabase. Change symbology ofLancaster_County’ to ESRI hollow. Bring the ‘Captiol_Beach_Rect_Proj’ layer on top. Make sure ‘CALMIT_2005’ layer is just below that. Zoom in to the ‘Captiol_Beach_Rect_Proj’ layer .


Right click on the ‘Captiol_Beach_Rect_Proj’ layer and go to properties/display, set transpaancy to 30%. Observe the symbology of the ‘CALMIT_2005’ layer for ‘roads’ (most probably black). See how Interstate – 80 is aligned with the Roads in ‘CALMIT_2005’ layer. This is indication that you have correctly georeferenced the TIF file.



Q Turn in the image showing overlay of CALMIT landuse and rectified Google image (30% transparent). What land uses are present on the Northwest side of the I-80. Which waterbody is present on the North west side of the I-80? What is another indication of presence of water body (which land use)? 






















Part II – Image interpretation

We can utilize the Image Analysis window to visualize and analyze the imagery. These tools will enhance the imagery in order to better interpret the imagery.  Go to Window/Image Analysis. Now image analysis window will open. Select the layers that you want to work with. Note that layer shown in a blue is the one which you are working currently. Simply checking the box won’t make the tools work on layer.



We can use different band combinations of RGB to interpret the images. We can interpret various features in the imagery such as vegetation, buildings, streets, water bodies, landmarks structures. I want to look at different kinds of vegetation. We can even work with historical imagery-you know temporal information.

Open July and Feb images from the Lancaster Geodatabse.  Change RGB layers to 3, 2, 1 respectively. You need to left mouse click on the rectangle (each for red, green, and blue) and assign the layer to it. This is called a natural-color composite where image features will be visible in natural color. Also open the georectified image of capitol Beach.  On the Northwest side of the georectified image you will see a structure just opposite the road (I-80). This is Lincoln municipal airport. Now we will use Display tools in image analysis window to enhance the image. Select Feb image and change display option – 1. Gamma – 0.50 and check DRA. Leave rest of the options to default. You will notice the now that image features such as capitol beach in blue color, airport buildings in white colors and soil in brownish color. Features are more identifiable after enhancement.  Remember these changes are not permanent and you need to take a screen shot while you are performing the exercise.



Q Turn in the picture of Lincoln Municipal Airport in February before and after enhancement using display tools

Now lets see what these tools are doing. Gamma tool will adjust the mid level values without bleaching out the image. So this leaves the black and the white, more of those values alone will adjust into mid values like you see here. One thing I can do is change the stretching function. So you see there are several I can choose from. DRA is called dynamic range adjustment and it dynamically adjusting the display of the image based on the pixel values that are currently visible. So it only optimizes the values that are currently on your screen.  You can explore other tools by playing around them. It would not make changes to original image.

Now change remove the Feb and July images and add them again to ArcMap. Now change the RGB combination to 4, 3, 2. This is called a false-color composite. It will enhance the vegetation in the image. Try to enhance the image if you need to.

These tool can be used to compare the images - zoom to raster resolution , swipe layer, flicker layer buttons. Explore these buttons. Note swipe layer button brigs special arrow, so hold on left mouse click, drag it across the images.


 Q. What is the prominent difference between July and Feb images of the Lincoln Municipal Airport with false color composite?


Now we will use some of the Processing functions. One of the commonly used functions to assess the presence of live green vegetation is NDVI. NDVI is normalized difference vegetation index. NDVI is computed using below formula:     RED and NIR stand for the spectral reflectance measurements acquired in the red and near-infrared regions of electromagnetic spectrum, respectively. NDVI takes the value from -1 to 1. The higher the NDVI, higher the fraction of live green vegetation present in the scene. Landsat band 4 (0.77-0.90 µm) measures the reflectance in NIR region and Band 3 (0.63-0.69 µm) measures the reflectance in Red region.  The equation ArcGIS uses to generate the output is as follows: NDVI = ((IR - R)/(IR + R)) * 100 + 100. This will result in a value range of 0–200 and fit within an 8-bit structure.


We can set the ‘Image analysis options’ using  button on top left corner of the Image analysis window. Click the button. Under NDVI, make sure Red Band is 3 and Infrared Band is 4. This is true for Landsat Images, you might have other hypersprectral or satellite image where layer numbers will change. Also if you have stacked only two or three bands in your Composite image, Layer number could change accordingly.



Select Feb layer in Image analysis window and click on NDVI button  under processing section. You will see NDVI_feb layer is created in ArcMap. Green color shows presence of vegetation and other colors show absence of green vegetation.


Right click on the NDVI_feb layer and data/export data. Chosse location to EX6 geodatabase and Name as NDVI_feb. Repeat the same process to get the July NDVI and save the data.



Q. What is the prominent difference between July and Feb NDVI for Lancaster County? Zoon in near the airport area and compare your results with false color composite image of same area that you have explored earlier? Do you think NDVI values are affected by the clouds?


Part III Image classification


Often we use readily available data in GIS. This data is GIS ready and is provided by various government agencies or private organization. Let’s see how this data is obtained from imagery. We are going to create our own land use map as an example. You have provided with Landsat 5 imagery for path 28 and row 32. This covers area of Lancaster County, Nebraska where Lincoln is located. We will start working with raw Landsat imagery and build our own land use map.


Landsat 5 has 7 bands as  area of Lancaster county, Nebraska where Lincoln is located. We will start working with raw landsat i explained at the beginning of the exercise. Each band contains reflectance within certain wavelength. After we stack these bands we will have information from all the bands contained within single image for entire area. These multiband images can be used for creating spectral profile of the location in remote sensing analysis.


Search for composite bands tool under tools option. Tool is located under data management\raster\raster processing\composite bands. Select input rasters from LT50280322005196EDC00 folder under you exercise folder. This folder contains TIFF images for 7 landsat bands. Add all 7 bands as input rasters. (Trick is to select first image L5028032_03220050715_B10, hold shift key and select one.) Make sure all bands are added in the same order (from L5028032_03220050715_B10 to L5028032_03220050715_B70).  Save the output file as LandJulyComp.img under Ex6 geodatabase. (Note: if it gives the error, you can save it under the separate folder anywhere you want.)



This was the conventional way of doing composite bands. With Image analysis window you can do it on the fly. Add all the seven landsat bands (from L5028032_03220050715_B10 to L5028032_03220050715_B70) to ArcMap. With this tool you can create a composite band of any layers that you want and perform image classification analysis on that.



Now reproject the LandJulyComp.img to NAD 1983 StatePlane Nebraska FIPS 2600 Feet. Search Project Raster. Select input raster as LandJulyComp.img. Name output raster dataset as ‘LandJulyCompProj.img’ under you exercise folder. Import coordinate system from the layer ‘NASS_2005’. Choose geographic transformation as WGS_1984_ITRF00_To_NAD_1983.



Set RGB combination of LandJulyCompProj.img as Layer4, Layer3, Layer2. This is false color composite which facilitates the green vegetation identification. Red color shows the presence of green vegetation and green color shows the absence of vegetation. Also observe the clouds with the white color which are scattered over the image.


Clouds are masked in remote sensing analysis to avoid error. Right click on Ex6 geodatabase in ArCatalog. Click new/feature class. Name is as Clouds with alias Cloud. Choose type of feature as polygon. On next window import coordinate system from CALMIT_2005 raster in Lancaster geodatabase i.e NAD 1983 StatePlane Nebraska FIPS 2600 Feet. Now your empty polygon feature class is ready and we will digitize the clouds. Add ‘clouds’ polygons to ArcMap.


On the editor toolbar click Editor/start editing.  Choose clouds polygon. You will see white color clouds scattered all over the image. Zoom in any of the cloud and observe you will also see black color shadow of the cloud just on the northwest side of the cloud. This is indication of the direction in which Landsat sensor has acquired the image.


In the create feature window choose cloud layer and in construction tools choose polygon. Now approximately digitize the cloud and cloud shadow or five to six sizable clouds in same area. Go to editor\ Stop editing and save your edits.  Once we have identified the clouds we need to mask it (i.e. fill it with null/-9999 or similar value). In typical remote sensing software (ERDAS Imagine or ENVI) cloud filling is one step operation. In ArcGIS there is no direct way to mask the clouds. However one can use model in model builder with various tools to mask the clouds. One such idea is - Add new field to cloud polygon attribute table with -9999, Convert polygon to raster using  -9999 field, Use conditional statement to get new raster which is cloud masked raster with cloud value -9999, Use set null tool to set remove cloud part. Interested students can follow similar approach, but it is not mandatory for this exercise. ArcGIS has very powerful geoprocessing tools, students are encouraged to explore tools for such analyses purposes.


Q. Turn in the false color composite image with cloud polygon overlain on that. (use thick contract boundaries for polygon with hollow symbology)













Next part is to classify the image to create land use map. Go to Customize/Toolbars/Image Classification.  Make sure Spatial Analyst extension is enabled. 



Turn all the layers off and make only three layers visible - CALMIT_2005, NASS_200, and  LandCompJulyBandProj.img. Set RGB combination of LandJulyCompProj.img as Layer4, Layer3, Layer2. This is false color composite which facilitates the green vegetation identification. Red color shows the presence of green vegetation and green color shows the absence of vegetation.


Observed how urban area in the middle of the image is green and agricultural fields outskirts are red color. The landsat image is obtained in July, which is peak growing season for corn and soybean – major crops in the state. This will give you a feel how land use map of NASS and CALMIT in agreement with actual landsat image. You can also zoom in use swipe layer tool in image analysis window to see the detail of three images.


We are going to perform image classification to make our own land use map and compare it with CALMIT and NASS products.


Image classification refers to the task of extracting information classes from a multiband raster image. Depending on the interaction between the analyst and the computer during classification, there are two types of classification: supervised and unsupervised. The intent of the classification process is to categorize all pixels in a digital image into one of several classes, or "themes". This categorized data may then be used to produce thematic maps. (e.g. land cover)  Normally, multispectral data are used to perform the classification and, indeed, the spectral pattern present within the data for each pixel is used as the numerical basis for categorization (Lillesand and Kiefer, 1994).


Unsupervised classification is a method which examines a large number of unknown pixels and divides into a number of classed based on natural groupings present in the image values. Unlike supervised classification, unsupervised classification does not require analyst-specified training data. The basic premise is that values within a given cover type should be close together in the measurement space (i.e. have similar gray levels), whereas data in different classes should be comparatively well separated (i.e. have very different gray levels) (PCI, 1997; Lillesand and Kiefer, 1994; Eastman, 1995 )

The classes that result from unsupervised classification are spectral classed which based on natural groupings of the image values, the identity of the spectral class will not be initially known, must compare classified data to some form of reference data (such as larger scale imagery, maps, or site visits) to determine the identity and informational values of the spectral classes. Thus, in the supervised approach, to define useful information categories and then examine their spectral separability; in the unsupervised approach the computer determines spectrally separable class, and then define their information value. (PCI, 1997; Lillesand and Kiefer, 1994)


Unsupervised classification is becoming increasingly popular in agencies involved in long term GIS database maintenance. The reason is that there are now systems that use clustering procedures that are extremely fast and require little in the nature of operational parameters. Thus it is becoming possible to train GIS analysis with only a general familiarity with remote sensing to undertake classifications that meet typical map accuracy standards. With suitable ground truth accuracy assessment procedures, this tool can provide a remarkably rapid means of producing quality land cover data on a continuing basis.


On the Image Classification toolbar under classification choose Iso Cluster Unsupervised Classification. Make sure you have  LandCompJulyProj.img layer selected for classification.

. Select number of classes as 20. Name out put classified raster as ISO_Unsupervised. Save output signature file as ISO_Unsupervised.gsg. Keep rest of the options to default.

This is first rough estimate of how your land use map might look like. We can use this map now to see which pixels are grouped in one class and it will serve as guideline for us to choose the training samples for supervised classification. Training samples in supervised classification are input by user which serve as a ground truth. 


Let’s compare the land use map with CALMIT and NASS land use maps for 2005. Use  and  buttons on Image Analysis window to compare the three land use map. Zoon in various areas of the map to investigate in detail. You can simply check and uncheck the layers in frame and compare the display by toggling.


Q. Turn in the output of Iso Cluster Unsupervised Classification with class symbology shown on the side. Comment on the comparison between three land use maps. Give the class numbers (from ISO_Unsupervised layer) of the land use categories (from NASS or CALMIT) that you think are quite well mapped by the unsupervised classification. Which classes do you think are captured most accurately? Do you think clouds will have impact on the classification? If yes, how? Do you think clouds and cloud shadows are capture by the unsupervised classification? If yes, give the class numbers.


Lets try supervised classification. We need to create training dataset where we will mark the areas on the image with known classes. In practice this data is collected using GPS on the ground and actual land use class is assigned to the point. Since we don’t have ground truth data, we can use NASS and CALMIT landuse maps as a ground truth.


On the image classification toolbar make sure LandCompJulyBandProj.img is active. Now click on ‘draw training sample with polygon’ tool.


Now we will draw polygons around some samples of each land use class in CALMIT_2005 map. Let’s start with water. Zoom in near branched oak lake area in the image. If you see CALMIT_2005 map it is a large waterbody in northwest side of the image (most probably shown in blue color).


Figure below shows the illustration of how to draw the polygon. First image shows zoomed area of branched on lake from CALMIT_2005 layer. We are using this map as guideline to draw training sample, however we need to be careful of clouds.


Second image shows LandCompJulyBandProj.img in which cloud/cloud shadow is clearly shown on the lake. We will avoid cloudy area while drawing polygon. Third image shows the sample training polygon only drawn on cloud free lake area. We also need to draw a polygon well within the CALMIT_2005 layer’s lake to avoid spectral mixing or mapping error on the edges of CALMIT land use classes.


Now last image shows results of our ISO unsupervised classification, notice how lake area is covered in bluish grey color and cloud is also captured in black color. You can see lake is incorrectly mapped in unsupervised classification. This is an example of why cloud masking is essential to avoid errors.



Now continue looking for water bodies in CALMIT map and draw polygons within them avoiding clouds. You can toggle between layers by turning layer on and off. Trick is to keep only required layers on and toggling between them. You can also use   and  buttons on Image Analysis window. After you have drawn few polygons for water class, open training sample manager  on image classification window.


As shown below, I have drawn seven polygons for water class. Select all your polygons for water class in training manager using shift key and left mouse button. Click on the merge  button. You just have merged all the polygons into single polygon which all represents water body as your training data (ground truth). Click on the class name ‘class 1’ and rename it as Water, choose sky blue color under color column. You have finished creating training sample for water with appropriate symbology.



If you have incorrectly drawn the polygon, open training sample manager and click on the record, it will highlight the selected polygon, simply delete the polygon using ‘delete’ key or use button. Note you have to do this before merging the polygons. You can also split your merged samples using  button and reinvestigate each polygon.



Above figures indicates the training samples for Irrigated corn, after I merged them I created Irrigated Corn class. Click on the class in training manager and click on histogram tool. Histograms for that class will be plotted (for each band in the layer) as shown in figure above. Notice difference between histograms of each layer. Histogram for layer 4 is normally distributed and have more pixels with high values. This is near infrared (NIR) band in Landsat which we use for vegetation assessment (NDVI).


Repeat the same process for few other classes as shown the in the table below in CALMIT_2005 layer. Urban vegetation is a new class you need to create which is not in CALMIT layer. CALMIT assigned urban class to entire Lincoln area. However there are some trees and grass within the city. Try to find it and assign it as Urban Vegetation class.


Dryland Alfalfa

Irrigated Corn

Riperian Forest and Woodland

Dryland Corn

Range, Pasture, Grass

Irrigated Soybean


Dryland Soybean

Urban Vegetation

Irrigated Alfalfa


Choose as many samples as you can for each class and merge them, rename them, specify appropriate symbology.


Remember it is not important to just have higher number of samples for good classification, but also to have reliable samples. So make sure you don’t have the cloudy area in you sample, make sure you see the false color composite (LandCompJulyBandProj.img) and choose nice consistent area for samples using CALMIT land use map as a guideline. Also modify the symbology of CAMLIT land use map to enhance current land use class that you are sampling (may use dark/fluorescent colors). Use your intuition and knowledge of ArcGIS to get best out of your image. Remember geography is both art and science. 


Once you have finished you samples for all 11 classes, use reset class values button  to arrange the class values. Notice class values are now arranged in contiguous order. Use  button training sample manager window to and save the output feature class as ‘signature’ in you Ex6 Geodatabase. Make sure Save as type is feature class and not shapefile. Otherwise you won’t be able to save it Ex6 geodatabase.


Also save the signature file using  button on training sample manager window, name it as ‘SupervizedSiganture’. This file will have .gsg extension. Note that you can’t really save signature file in Ex6 geodatabase. So you can save it anywhere in your folder.





Select all the 11 classes and click on scatterplots button. Scatterplot of various layers will be displayed.



Close the training sample manager.


On Image Classification window, under classification choose interactive supervised classification. Map layer will be created using training samples that you just created. New layer named Classification_LandCompJulyBandProj.img will be created, which is your land use map.  Note: Do not panic if you see your land use map looks very weird or shows only single value/class for entire map. It is likely if you choose very fewer samples or samples which are wrong or which are under cloud cover. You can again open training sample manager and revise your samples. Focus on the classes which you have created with very few samples (Check the count column in training sample manager)


You just have created your own Landuse map. Although ArcMap shows the legend, it is not present in the attribute table of the layer. Open attribute table of the layer ‘Classification_LandCompJulyBandProj.img’



Notice Class_Name is described as Class_1, Class_2 etc. Let’s save this layer first. Right click on the ‘Classification_LandCompJulyBandProj.img’ layer select Data/Export data. Select Name as  Classification_LandCompJulyBandProj and Location to Ex6 geodatabase. Leave rest of the options to default. After saving add the layer to ArcMap. Remove old temporary file Classification_LandCompJulyBandProj.img’from ArcMap.



We need to make it as actual classes. Open ‘Signature’ feature class from Ex6 geodatabase. Right click on Classification_LandCompJulyBandProj’ layer, click Joins and Relates/Join. Join attribute from a table with ‘value’ and choose table to join this layer as ‘signature’, choose field in the table to base the join on as ‘Classvalue’. Keep all the records.



Open the attribute table of Classification_LandCompJulyBandProj after joining and make sure it is joined correctly. You should now see actual classnames like water, irrigated corn etc.


Right click on the Classification_LandCompJulyBandProj layer and Data/Export Data. Save it under Ex6 folder under the name ‘Classification_LandCompJulyBandProjJoin’. Add Classification_LandCompJulyBandProjJoin’ to the ArcMap. Change the symbololy of the layer. Chose unique values with value field ‘classname’



Q. Turn in the supervised classification map with class names shown in the legend. Visually compare the map with CALMIT 2005 and NASS maps and comment on how accurately you have classified the maps.


Now we will perform maximum likelihood classification. On Image Classification window select Classification/ Maximum Likelihood Classification. Select input raster band as ‘LandCompJulyBandProj.img’ Choose signature file ‘supervizedsiganture.gsg’ that you have saved earlier. Save the data under Ex6 geodatabase with the name ‘MLC_Lancaster’. Leave rest of the options as default. 


Q. Turn in the maximum likelihood classification map with class values shown in the legend. Visually compare the map with CALMIT 2005 and NASS maps and comment on similarity or differences in the map classes.Could you relate to some class values (from MLC_Lancaster) to classnames (in Landuse_2005)



Now we will compute the class probability. On Image Classification window select Classification/ Class Probability. Select input raster band as ‘LandCompJulyBandProj.img’ Choose signature file ‘supervizedsiganture.gsg’ that you have saved earlier. Save the data under Ex6 geodatabase with the name ‘CP_Lancaster’. Leave rest of the options as default.


Output will contain 11 bands. There is one band for each class or cluster in the input signature file. Each band stores the probability that a cell belongs to that class. This can be useful in merging classes after a classification is done.

 Learn more about Class probability by searching in ArcGIS help . 


After you run class probability tool, ‘CP_Lancaster’ layer will be added to the map. This is three band layer. But actually there are 11 bands created, one for each class of signature file. Navigate to ‘CP_Lancaster’ layer in Ex6 geodatabase and double click on the file (do not add to map). Now you will see the 11 layers with names Layer_1, Layer_2 and so on. Open each of the layers in ArcMap and  observed how it probability is high where the class features are present. Open the ‘signature feature class’ from Ex6 geodatabase.




Open attribute table of the layer ‘signature’ Select the attribute ‘water’ and open the layer that corresponds to it (if it is first class in attribute table it will be ‘layer_1’). Observe how probability is 100% where your signature file has ‘water’ class polygon. 




 Q. Turn in the screenshot zoomed in one of your polygon for ‘urban vegetation’ class from signature file with map of class probability for urban vegetation class.

Q. Also turn in the full extent map of ‘urban vegetation’ class probability and compare it with the CALMIT land use map. Comment how your ‘urban vegetation’ class is mapped compared to Landuse map of CALMIT (note CALMIT does not have this class, so see what corresponds in CALMIT map for your ‘urban vegetation’. What do you think are the possible errors in the mapping ‘urban vegetation’? What could be the reasons?


Open the attribte tables of the three layers ISO_Unsupervised, CALMIT_2005, Classification_LandCompJulyBandProjJoin and MLC_Lancaster. Export the attribute tables with corresponding names in your folder. Open it using MS Excel. Create the histogram each land use class/value using count.

Here count is a pixel count in that class. Each pixel is ~98 feet. So are of land use can be computed as pixel count multiplied by 98 * 98. (This is informational you need not compute the area)


Q. Turn in histogram of each land use map. Compare and comment on the histograms. This is sort of comparison for each land use classification we have performed.




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