A Combination of Mutual and Neighborhood Information for Band Selection in Hyperspectral Images
Abstract
Classification of hyperspectral images is computationally expensive due to the presence of large number of spectral bands. Therefore, dimensionality reduction using selection of optimal set of bands is an essential task to speed up the subsequent classification process. Bands must be selected in such a way so that they are as much independent as possible without sacrificing classification accuracy. In this context, a supervised band selection approach is proposed combining mutual and neighborhood information of bands. For classification purpose, Support Vector Machine classifier is used. Overall classification accuracy is considered to assess the efficiency of the proposed method. Performance of the proposed technique is compared with several other Mutual Information based methods and the proposed method is found to be better as compared to others.
Authors: Abhishek Dey, Susmita Ghosh, Emmett J. Ientilucci
Published in: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2023)