Advanced Remote Sensing: Lab 4: Unsupervised Classification

Goal and Background

The main goal of lab 4 was an introduction to classification methods. These classification methods are built around extracting information from a remotely sensed image and interpreting the subject well enough for classification. The lab allowed for an understanding of the execution of recoding pixels into classes for land use/ land cover. Land use/land cover (LULC) maps show pixels classified into similar classes relating to what the use of the land is and what type of land is there. For instance in this lab forest, agriculture, water, urban/built up, and bare soil were the classes chosen. Different spectral signature ranges are grouped together to classify images. The classification used in this lab was an unsupervised classification which allows an algorithm in the ERDAS program to group the spectral ranges together and then the user must identify what the pixels should be.

Methodology

The first part of this lab was to run a type of unsupervised classification algorithm. This algorithm is called Iterative self-organizing data analysis technique or ISODATA. To run this algorithm an input raster needs to be in ERDAS. The raster image for this was called eau_chippewa2000.img.

Figure 1: The subsequent image for this lab

To get to the classification tool the steps to follow are Raster, then Unsupervised, and Unsupervised classification. This will open the Unsupervised Panel shown below. Under Clustering Options the Isodata option was checked. Next, an output file was made. The number of classes was set to 10, which means that pixels would be group into 10 classes depending on their pixel value. Clicking Initializing Options was outlined in the process just to make sure that Principal Axis was set (it is the default). After this making sure that the Approximate True Color button was checked under Color Scheme Options. The Maximum Iterations was 250. The output file is named eau_Chipp2000usp.img.

Figure 2: The unsupervised panel which sets all the parameters.

After all these parameters were changed the model was ran. This model created pixel clusters that were classified together. To view the classes selecting the Table tab, then Show Attributes. This shows the 10 classes created in the unsupervised classification. Next was to recode the 10 classes into 5 and to give them proper colors instead of the pink and cyan of the original unsupervised image. The 5 classes were water (blue), forest (dark green), agriculture (pink), urban/built-up (red), and bare soil (sienna). To recognize what was being viewed in the classes it is advisable to connect to Google Earth from ERDAS. The reason for this is for better classification. Incorrectly classifying agriculture as urban/built-up would cause an odd image to appear. Also, a very important step would be to change the Google Earth date to 2005 so that there is a relationship between the image in ERDAS and Google Earth. Once everything was synced then reclassifying could begin. To identify the first class the color was changed to gold to the pixels in that class. Identifying the pixels showed that this particular class was water. The pixel color was changed to water to properly identify it on a LULC map. The class name was changed to water as well. The reclassifying process was done with each class. Each class was changed to gold in a procedural way and the corresponding class color and name was given. To save all this classification process simply closing the table will suffice, but it will ask to save which should be done. The recoded image should be saved as eau_Chipp2000rc.img through the process of File, then Save As, and finally Top Layer As.

Figure 3: 10 class raster attribute table

The second part of the lab was to increase the pixel clusters of the first part of the lab. This time instead of 10 classes 20 classes was the minimum and maximum parameter. The Convergence Threshold was set to 0.92. This output image was named eau_Chipp2000usp2.img. The recoding of this image was done the same as the first image, but this time with 20 classes. This reclassified image is now called eau_Chipp2000rc2.img. To make the image easier to analyze the Raster Attributes were changed. The steps for this were File, View, View Raster attributes. The column properties need to be accessed by clicking on the column icon in Raster Attributes. In column properties the hierarchy of the column should be class names, color, and histogram. Under class_names the display width was set to 15 and the max width to 20.

Figure 4: 20 class raster attribute table

Figure 5: Column Properties window

The third part of the lab was to simplify the 20 classes into 5 classes. The eau_Chipp2000rc2.img is the image that was reclassified. To recode these images Thematic and then Recode were clicked this opened up the recode window. Each class was recode into 1 of 5 classes. Water was recoded as 1, forest as 2, agriculture as 3, urban.built-up as 4, and bare soil as 5. This final image is now saved as eau_Chipp2000rcF.img