Independent Component Analysis for Classifying Multispectral Images with Dimensionality Limitation

Airborne and spaceborne remote sensors can acquire invaluable information about earth surface, which have many important applications. The acquired information usually is represented as two-dimensional grids, i.e. images. One of techniques to processing such images is Independent Component Analysis (ICA), which is particularly useful for classifying objects with unknown spectral signatures in an unknown image scene, i.e. unsupervised classification. Since the weight matrix in ICA is a square matrix for the purpose of mathematical tractability, the number of objects that can be classified is equal to the data dimensionality, i.e. the number of spectral bands. When the number of sensors (or spectral channels) is very small (e.g. a 3-band CIR photograph and 6-band Landsat image with the thermal band being removed), it is impossible to classify all the different objects present in an image scene using the original data. In order to solve this problem, we present a data dimensionality expansion technique to generate artificial bands. Its basic idea is to use nonlinear functions to capture and highlight the similarity/dissimilarity between original spectral measurements, which can provide more data with additional information for detecting and classifying more objects. The results from such a nonlinear band generation approach are compared with a linear band generation method using cubic spline interpolation of pixel spectral signatures. The experiments demonstrate that nonlinear band generation approach can significantly improve unsupervised classification accuracy, while linear band generation method cannot since no new information can be provided. It is also demonstrated that ICA is more powerful than other frequently used unsupervised classification algorithms such as ISODATA.