In this paper, we present an effective pre-processing algorithm for band selection approach which is an essential task in hyperspectral image analysis. The pre-processing algorithm is developed based on the average inter-band block-wise correlation coefficient measure and a simple thresholding strategy. Here, the threshold parameter is found based on the standard deviation of the average inter-band block-wise correlation coefficients. The performance of the proposed algorithm is validated using the standard hyperspectral database created by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the detected bands with ground-truth annotations, we observed that the proposed algorithm identifies the noisy and water absorption bands in the high-dimensional hyperspectral images. The proposed algorithm achieves the classification accuracy of 94.73%.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@25945293 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@2cb9442b Through org.apache.xalan.xsltc.dom.DOMAdapter@372a79fd; Conference Code:89440
B. D Bhushan, Sowmya V., M Manikandan, S., and Soman, K. P., “An effective pre-processing algorithm for detecting noisy spectral bands in hyperspectral imagery”, International Symposium on Ocean Electronics, SYMPOL 2011. IEEE, Kochi, pp. 34 - 39, 2011.