TNT Image Classification Highlights:

• Work with large images (full scenes, scene mosaics) and multiple images from different sensors and/or dates

• Restrict classification to desired areas of any shape using mask

• Automated unsupervised and supervised classification with choice of classifiers for each

• K-Means, ISODATA, Maximum Likelihood, and many other classifiers including neural network methods

• Automatic display of classification result allows comparison with source imagery or any other geospatial data

• Graphical presentation of class statistics (dendrogram, scatterplots, cooccurrence matrix) to aid interpretation and analysis

• Reload classification result at any time for more analysis and modification

• Progressively merge classes with unlimited undos

• Name classes and save interpreted classification results at any time

• Create training sets for supervised classification manually or from attributes of polygon or point data

• Separate statistics compiled for training set and for classification result

• Error matrix shows accuracy of supervised classification

• Use your visual interpretive skills to guide an incremental classification of your image (i.e., Feature Mapping)

• Classify hyperspectral images and perform subpixel spectral identification

• Combine classification results for different dates or conditions into a single image showing all combinations of classes to determine correlations between different spatial conditions or detect change through time