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Proceedings Paper

Hyperspectral anomaly detection method based on auto-encoder
Author(s): Emrecan Bati; Akın Çalışkan; Alper Koz; A. Aydin Alatan
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Paper Abstract

A major drawback of most of the existing hyperspectral anomaly detection methods is the lack of an efficient background representation, which can successfully adapt to the varying complexity of hyperspectral images. In this paper, we propose a novel anomaly detection method which represents the hyperspectral scenes of different complexity with the state-of-the-art representation learning method, namely auto-encoder. The proposed method first encodes the spectral image into a sparse code, then decodes the coded image, and finally, assesses the coding error at each pixel as a measure of anomaly. Predictive Sparse Decomposition Auto-encoder is utilized in the proposed anomaly method due to its efficient joint learning for the encoding and decoding functions. The performance of the proposed anomaly detection method is both tested on visible-near infrared (VNIR) and long wave infrared (LWIR) hyperspectral images and compared with the conventional anomaly detection method, namely Reed-Xiaoli (RX) detector.1 The experiments has verified the superiority of the proposed anomaly detection method in terms of receiver operating characteristics (ROC) performance.

Paper Details

Date Published: 15 October 2015
PDF: 7 pages
Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430N (15 October 2015); doi: 10.1117/12.2195180
Show Author Affiliations
Emrecan Bati, Middle East Technical Univ. (Turkey)
Akın Çalışkan, Middle East Technical Univ. (Turkey)
Alper Koz, Middle East Technical Univ. (Turkey)
A. Aydin Alatan, Middle East Technical Univ. (Turkey)


Published in SPIE Proceedings Vol. 9643:
Image and Signal Processing for Remote Sensing XXI
Lorenzo Bruzzone, Editor(s)

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