Change detection method for remotely sensed images based on multivariate analysis method and statistical test

Hiroshi Okumura; Ryohei Yamasaki; Tatsunori Goto; Kohei Arai

doi:10.1117/12.830329

28 September 2009 Change detection method for remotely sensed images based on multivariate analysis method and statistical test

Hiroshi Okumura, Ryohei Yamasaki, Tatsunori Goto, Kohei Arai

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Proceedings Volume 7477, Image and Signal Processing for Remote Sensing XV; 747717 (2009) https://doi.org/10.1117/12.830329
Event: SPIE Remote Sensing, 2009, Berlin, Germany

Abstract

A new change detection method for remotely sensed images is proposed. This method can be applied to two images which have different number of spectral bands and/or have different spectral ranges. The proposed method converts two multi-spectral-multi-temporal images into two sets of canonical variate images which have limited correlation called the canonical correlation. Then, one or more canonical variate images which are the most suitable for change detection are selected and change detection regions in the original images are extracted by using statistical modeling and statistical test. In this paper, the detail of the proposed method is described. Some experiments using simulated multi-spectral-multi-temporal images based on spectral profiles in ASTER Spectral Library are conducted to confirm change detection accuracy. The experimental results show reasonable changed regions and their change quantities.

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Hiroshi Okumura, Ryohei Yamasaki, Tatsunori Goto, and Kohei Arai "Change detection method for remotely sensed images based on multivariate analysis method and statistical test", Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 747717 (28 September 2009); https://doi.org/10.1117/12.830329

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KEYWORDS

Statistical analysis

Sensors

Earth observing sensors

Buildings

Current controlled current source

Image segmentation

Landsat

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