Unsupervised Classification of Aviris-NG Hyperspectral Images

In hyperspectral imaging for remote sensing, learning from unlabeled data by unsupervised methods is very challenging and it is the subject of considerable recent interest since the collection of large datasets by aircraft, UAVs and satellites has become ubiquitous. We experiment with unsupervised endmember extraction and classification of hyperspectral data collected over India by NASA's AVIRIS-NG airborne remote sensor. We have downloaded some of this data from the NASA-JPL portal in Pasadena, CA, for the purpose of studying land cover and land usage, and especially forests, in India. We report on results from our experiments with unsupervised endmember-based methods and clustering methods for classifying images from a mixed forest region that we selected from the Shoolpaneshwar Wildlife Sanctuary in Western In-dia. Randomized numerical methods are used to speed up the large-scale computations.