Improved sequential search algorithms for classification in hyperspectral remote sensing images

Abstract

Two new sequential search algorithms for feature selection in hyperspectral remote sensing images are proposed. Since many wavebands in hyperspectral images are redundant and irrelevant, the use of feature selection to improve classification results is highly needed. First, we present a new generalized steepest ascent (GSA) feature selection technique that 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. However, when the number of available wavebands is large, the computational load required for the GSA algorithm becomes excessive. We thus propose a modification of the improved floating forward selection algorithm which is more computationally efficient. Experimental results for two hyperspectral data sets show that our proposed algorithms yield better classification results than other suboptimal search algorithms.