A Unified Framework for Dimensionality Reduction and Classification of Hyperspectral Data
Other literature type
(issn: 2194-9034, eissn: 2194-9034)
The processing of hyperspectral remote sensing data, for information retrieval, is challenging due to its higher dimensionality.
Machine learning based algorithms such as Support Vector Machine (SVM) is preferably applied to perform classification of high
dimensionality data. A single-step unified framework is required which could decide the intrinsic dimensionality of data and achieve
higher classification accuracy using SVM. This work present development of a SVM-based dimensionality reduction and
classification (SVMDRC) framework for hyperspectral data. The proposed unified framework was tested at Los Tollos in
Rodalquilar district of Spain, which have predominance of alunite, kaolinite, and illite minerals with sparse vegetation cover.
Summer season image was utilized for implementing the proposed method. Modified broken stick rule (MBSR) was used to
calculate the intrinsic dimensionality of HyMap data which automatically reduce the number of bands. Comparison of SVMDRC
with SVM clearly suggests that SVM alone is inadequate in yielding better classification accuracies for minerals from hyperspectral
data rather requires dimensionality reduction. Incorporation of modified broken stick method in SVMDRC framework positively
influenced the feature separability and provided better classification accuracy. The mineral distribution map produced for the study
area would be useful for refining the areas for mineral exploration.