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Proceedings Paper

Multiclass change detection for multidimensional images in the presence of noise
Author(s): Javier López-Fandiño; Dora B. Heras; Francisco Argüello
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Paper Abstract

Change Detection (CD) techniques applied over multitemporal multispectral or hyperspectral remote sensing images allow monitoring changes in the land use or catastrophe tracking, among other applications. A multiclass CD technique for multidimensional images that is robust in the presence of noise is presented in this paper. The technique combines fusion at feature level to perform a first change/no change labeling (binary CD) and a later stage with fusion at decision level that performs a supervised multidate classification of the changed pixels (multiclass CD) obtaining the final from-to change map. The acquisition of multidimensional images usually corrupts the original signal by adding noise. This noise can be related with natural random processes or it can be produced during the sensor operation. Additive White Gaussian Noise (AWGN) and speckle noise simulate these effects. In this paper the robustness of the proposed CD technique in noisy scenarios for these two types of noise of varying intensity is evaluated. The experimental results show that the proposed technique is more robust than other alternatives, achieving accuracies close to those obtained in the absence of noise. The proposed technique is designed to be efficiently computed in GPU, thus dealing with the high computational cost of the processing of multidimensional images.

Paper Details

Date Published: 9 October 2018
PDF: 12 pages
Proc. SPIE 10792, High-Performance Computing in Geoscience and Remote Sensing VIII, 1079204 (9 October 2018); doi: 10.1117/12.2325363
Show Author Affiliations
Javier López-Fandiño, Univ. de Santiago de Compostela (Spain)
Dora B. Heras, Univ. de Santiago de Compostela (Spain)
Francisco Argüello, Univ. de Santiago de Compostela (Spain)

Published in SPIE Proceedings Vol. 10792:
High-Performance Computing in Geoscience and Remote Sensing VIII
Bormin Huang; Sebastián López; Zhensen Wu, Editor(s)

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