Abstract
In the last three decade, one of the significant breakthrough in remote sensing is to introduce of hyperspectral sensors, which acquire a set of images from hundreds of narrow and contiguous wavelengths of the electromagnetic spectrum from visible to infrared regions. Images, which are captured by these sensors, have detailed information in the spectral domain to identify and distinguish spectrally unique materials. To recognize the objects present in hyperspectral images, classification/clustering task need to be performed. But due to the presence of huge number of attributes, classification technique becomes more complex. So, before performing the classification task, reduce the number of attributes (denoted by dimensionality of the data) is an important step where the aim is to discard the redundant attributes and make it less time consuming for classification. In this chapter, few supervised feature extraction techniques for hyperspectral images i.e., prototype space feature extraction (PSFE), modified Fisher’s linear discriminant analysis (MFLDA), maximum margin criteria (MMC) based and partitioned MMC based methods are explained. Experiments are conducted over different hyperspectral data set with different quantitative measures to analyze the performance of these feature extraction methods.
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Datta, A., Ghosh, S., Ghosh, A. (2019). Hyperspectral Remote Sensing Images and Supervised Feature Extraction. In: Das, H., Barik, R., Dubey, H., Roy, D. (eds) Cloud Computing for Geospatial Big Data Analytics. Studies in Big Data, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-03359-0_13
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