A new feature selection algorithm for multispectral and polarimetric vehicle images

Multispectral and polarimetric data have been shown to provide detailed information useful for automatic target recognition applications. A major limitation of using these data in remote sensing is that they often consist of a large number of features with an inadequate number of samples. To reduce the number of features, we thus present a new generalized steepest ascent feature selection technique that selects only a small subset of important features to use for classification. Our proposed algorithm improves upon the prior steepest ascent algorithm by selecting a better starting search point and performing a more thorough search. It is guaranteed to provide solutions that equal or exceed those of the classical sequential forward floating selection algorithm. Initial results for one multispectral and polarimetric data set show that our algorithm yields better classification results than other suboptimal search algorithms.