Conference Papers

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    Mineral identification using unsupervised classification from hyperspectral data
    (Springer, 2020) Gupta, P.; Venkatesan, M.
    Hyperspectral imagery is one of the research areas in the field of remote sensing. Hyperspectral sensors record reflectance of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of remote sensing of Hyperspectral data. Challenges with the hyperspectral data include high dimensionality and size of the hyperspectral data. Principle component analysis (PCA) is used to reduce the dimension of data by band selection approach. Unsupervised classification technique is one of the hot research topics. Due to the unavailability of ground truth data, unsupervised algorithm is used to classify the minerals present in the remotely sensed hyperspectral data. K-means is unsupervised clustering algorithm used to classify the mineral and then further SVM is used to check the classification accuracy. K-means is applied to end member data only. SVM used k-means result as a labelled data and classify another set of dataset. © Springer Nature Singapore Pte Ltd 2020.
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    Unsupervised learning method for mineral identification from hyperspectral data
    (Springer, 2021) Prabhavathy, P.; Tripathy, B.K.; Venkatesan, M.
    Hyperspectral imagery is one of the research area in the field of Remote sensing. Hyperspectral sensors record reflectance (also called spectra signature) of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of Remote sensing of Hyperspectral data. EO-1 hyperion dataset is unlabeled data. Various types of clustering algorithms are proposed to identify minerals. In this work principal component analysis is used to reduced it’s dimension by reducing bands. Hard-clustering and soft-clustering algorithms are applied on given data to classify the minerals into classes. K-means is hard type of clustering which classify only non-overlapping cluster however, PFCM is soft type of clustering which allow a data points to belongs more than one cluster. Further, results are compared using cluster validity index using DBI value. Both clustering algorithms are experiments on original HSI image and reduced bands. Result shows that PFCM is perform better than K-means for the both type of images. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.