A Decision Fusion Framework for Hyperspectral Subpixel Target Detection

作者：Gholizadeh Hamm^*; Zoej Mohammad Javad Valadan; Mojaradi Barat

来源：Photogrammetrie Fernerkundung Geoinformation, 2012, (3): 267-280.

DOI：10.1127/1432-8364/2012/0116

• Target detection
• decision fusion
• hyperspectral data
• remote sensing

Target detection is one of the most challenging issues of remotely sensed data. Due to high spectral resolution of the hyperspectral images and their limited ground sampling distance, targets of interest occur at subpixel level. In such cases, spatial characteristics of targets are hard to acquire and the only way to overcome such problem is to take advantage of the spectral information. Based on the spectral characteristics of background and the targets to be detected, several methods have been proposed. Some of these methods assume a physics-based approach, while the other may be purely statistical. So, all of these methods are based on some assumptions each of which can be challenged in one way or another. One possible way to take advantage of these differences to improve the final results is the fusion of the detectors' outputs. In this paper, eight subpixel target detectors are employed as the ensemble detectors. It is also worth mentioning that the detectors should be different from each other; otherwise the overall decision will not be better than the individual detectors. So, we suggest using the genetic algorithms to select the most suitable detectors for a given decision fusion rule. Experimental results on a real world hyperspectral data as well as a synthetic dataset show the efficiency of the proposed method to improve the detection performance.

• 出版日期2012