A Research: Hyperspectral Image Processing Techniques
Kiran Gowda C1, S Usha2, C J Jagadeesha3
1Kiran Gowda C, Research Scholar, CSE, RRCE, VTU, Karnataka, India.
2S Usha, RRCE, VTU, Bangalore, Karnataka, India.
3C J Jagadeesha, RRSSC(ISRO), FIE, IEI-KSC, Bangalore, Karnataka, India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 577-580 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11200789S219/19©BEIESP DOI: 10.35940/ijitee.I1120.0789S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Hyperspectral image contains more information which are gathered from numerous narrow wavebands from one or more regions, and large amount of data are huddled. An basic problems in hyperspectral image processing are dimension reduction, target detection, target identification, and target classification. In this document, we reviewed the latest activities of target classification, most frequently used techniques for dimension reduction, target detection. Hyperspectral image processing is a complicated process which rely on mixed agents. Here we also recognized and reviewed problems faced by some methods and to overcome the problems, current techniques are discussed and highlighted good methods. To improving correctness, genuine classification techniques and Detection Techniques analysis are recommended.
Keywords: Dimensionality Reduction(DR), Hyperspectral Image(HSI), Independent Component Analysis(ICA), Principal Component Analysis(PCA), Projection Pursuit(PP), Target Detection(TD),
Scope of the Article: Image Processing and Pattern Recognition