Module 9: Image Classification

This week we continued with image classification.  Image Classification is a major application of remotely sensed imagery to provide information on land use and land cover.  In a previous weeks we utilized tone, texture, shape, size, pattern, shadow and surroundings to identify and classify.  Then moved on to looking at different spectral bands.  This week we move toward automated procedures.  Spectral Pattern Recognition is a numerical process whereby elements of multispectral image data sets are categorized into a limited number of spectrally separable, discrete classes.  Unsupervised Classification, the focus this week, uses some form of clustering algorithm to decide which land cover type each pixel most looks like.  Supervised Classification, next week, uses training sites from multiple spectral band data to guide the computer's classification.   

Tools like Feature Space Image allows visualization of 2 bands of image data simultaneously through a 2 band scatterplot, one band plotted on the X and the other on the Y axis.  This tool helps consider covariance, correlation and clustering between bands.  Spectral Distance, is one means of predicting the likelihood that a pixel belongs to one class or another.  Spectral Distance can be measured as simple euclidean distance in unsupervised or a statistical distance in supervised.  These tools help to differentiate spectral classes, clusters of spectrally similar pixels, that can then be used to form information classes, meaningful groups.  

This week in lab we performed unsupervised classification in both ArcMap and ERDAS.  We attempted to accurately classify images of different spatial and spectral resolutions.  And we manually reclassified and recoded images to simplify the data.  We utilized ISODATA (Iterative Self-Organizing Data Analysis Technique) Clustering Algorithm.  

The map this week is of the UWF campus.  The main map was created by utilizing ISODATA classification in ERDAS.  The tool was set to 50 classes with 25 iterations with a convergence threshold of 0.95 and a skip factor of 2 for both X & Y.  Reclassification of the 50 classes to 5:  trees, grass, building&roads, shadows and mix.  The reclassification was then merged into the 5 classes.  The lab instructions were to calculate an estimate of porous and non-porous surfaces in the image.  I decided to run ISODATA with the same specifications and reclass based on porous, non-porous, and mixed.  Both processes proved difficult primarily due to pixels categorized in the same groups of 50 that were different information classes.  Pixels for roads, parking lots and roof tops sharing groupings with grass or bare ground.   With the original 50 classes to ulizmarly get 5 classes for the main map and 3 classes for the insert the error ratio, based only on my opinion of the visual inspection, is still high.