Remote Sensing: Module 9 - Unsupervised Image Classification

     This week we delved into digital image classification. Up first is unsupervised classification, the following module (10) covers supervised classification. Unsupervised classification is an iterative (that is, until a threshold is met) procedure involving the clustering of pixels (with similar spectral reflectance) into classes (a parameter determined by the user). Supervised image classification uses a priori knowledge to guide classification (by way of 'training sites').
     In this lab we took a high resolution aerial photograph of the University of West Florida campus and performed an unsupervised classification (using the ISODATA algorithm) in ERDAS Imagine. The result of which is a thematic raster with a lower spectral resolution than the original photograph. This image was classified into fifty categories. It was our job to reclassify and reduce these categories into one of  five choices: trees, grass, buildings/roads, shadows, and mixed (for pixels that fall into more than one category). This was done by selecting pixels and reassigning them into one of the categories and giving them a designated color. Once all fifty classes were reclassified, the Recode tool was used to reduce the number of classes into the final five. Descriptive data (in the form of areas and percentages) was calculated for each category and for permeable/impermeable surfaces. The map deliverable below displays the classified image and accompanying data.
     ArcGIS was used to make the final map product but it can also run an unsupervised classification by using the Iso Cluster tool and the Maximum Likelihood Classification tool. It is  necessary, however, to possess the Spatial Analyst extension.

Map 1 Unsupervised Digital Image Classification of the University of West Florida Campus