Dimensionality Reduction Techniques For Hyperspectral Image using Deep Learning
K. Sudha Rani1, T. Gowri2, T. Madhulatha3
1K. Sudha Rani, Department of ECE, TKR College of Engineering & Technology, Hyderabad (Telangana), India.
2T. Gowri, Senior Member, IEEE, Department of ECE, GITAM University Vishakapatanam (Andhra Pradesh), India.
3T. Madhulatha, Department of ECE, Malla Reddy Engineering College for Women, Hyderabad (Telangana), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 12 December 2019 | Manuscript Published on 30 December 2019 | PP: 364-370 | Volume-9 Issue-2S3 December 2019 | Retrieval Number: B10331292S319/2019©BEIESP | DOI: 10.35940/ijitee.B1033.1292S319
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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: This Research proposal addresses the issues of dimension reduction algorithms in Deep Learning(DL) for Hyperspectral Imaging (HSI) classification, to reduce the size of training dataset and for feature extraction ICA(Independent Component Analysis) are adopted. The proposed algorithm evaluated uses real HSI data set. It shows that ICA gives the most optimistic presentation it shrinks off the feature occupying a small portion of all pixels distinguished from the noisy bands based on non Gaussian assumption of independent sources. In turn, finding the independent components to address the challenge. A new approach DL based method is adopted, that has greater attention in the research field of HSI. DL based method is evaluated by a sequence prediction architecture that includes a recurrent neural network the LSTM architecture. It includes CNN layers for feature extraction of input datasets that have better accuracy with minimum computational cost.
Keywords: Hyperspectral Imaging (HSI), Dimensionality Reduction (DR), Deep Learning (DL), Independent Component Analysis (ICA), Principal Component Analysis (PCA), Minimum Noise Fraction (MNF).
Scope of the Article: Deep Learning