Independent component analysis for remote sensing study
Chi Hau Chen, Xiaohui Zhang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
Recently there has been much interest in the Independent Component Analysis (ICA) methods for source signal separation. ICA algorithms can be represented by a neural network architecture to decompose a signal or image into components. The potential use of ICA in remote sensing study is examined. For SAR imagery in particular, the use of ICA to enhance the images and to improve the pixel classification is considered. It is shown that ICA processed images generally have lower contrast ratio (standard deviation to mean of an image) which implies a reduced speckle effect. The features extracted by using ICA also are quite effective for pixel classification. There are five pattern classes considered. By using the 9 original SAR images plus all 6 ATM images, the best overall percentage correct is 86.6% which is the same as using 3 ICA and 6 ATM image data. Also ICA is shown to be better than PCA in classification with the same data set. Although the results presented are preliminary, ICA through its de-mixing operations is potentially a useful approach in remote sensing study.