Publication Type : Conference Proceedings
Publisher : AISC Springer Series
Source : Advances in Intelligent Systems and Computing, Second International Conference on Computer and Communication Technologies (IC3T -2015), AISC Springer Series(SCOPUS, ISI proceedings), Volume 380, CMR Technical Campus, Hyderabad, p.557-565 (2016)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945909794&partnerID=40&md5=9dc5a2ff515e4e70eef9effd7d719235
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)
Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication, Communication
Year : 2016
Abstract : Dimensionality reduction techniques have been immensely used in hyperspectral image classification tasks and is still a topic of great interest. Feature extraction based on image fusion and recursive filtering (IFRF) is a recent work which provides a framework for classification and produces good classification accuracy. In this paper, we propose an alternative approach to this technique by employing an efficient preprocessing technique based on average interband blockwise correlation coefficient followed by a stage of dimensionality reduction. The final stages involve recursive filtering and support vector machine (SVM) classifier. Our method highlights the utilization of an automated procedure for the removal of noisy and water absorption bands. Results obtained using experimentation of the proposed method on Aviris Indian Pines database indicate that a very low number of feature dimensions provide overall accuracy around 98%. Four different dimensionality reduction techniques (LDA, PCA, SVD, wavelet) have been employed and notable results have been obtained, especially in the case of SVD (OA = 98.81) and wavelet-based approaches (OA = 98.87). © Springer India 2016.
Cite this Research Publication : L. S. Kiran, Sowmya, and Dr. Soman K. P., “Dimensionality reduced recursive filter features for hyperspectral image classification”, Advances in Intelligent Systems and Computing, Second International Conference on Computer and Communication Technologies (IC3T -2015), vol. 380. AISC Springer Series(SCOPUS, ISI proceedings), CMR Technical Campus, Hyderabad, pp. 557-565, 2016.