An Improved Method to Detect Remote Sensing Image Targets Captured by Sensor Network  

作　　者：SHEN Yingchun JIN Hai DU Bo 

机构地区：[1]School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China [2]Wuhan Digital Engineering Institute, Wuhan 430074, Hubei,China [3]School of Computer, Wuhan University, Wuhan 430072,Hubei, China

出　　处：《Wuhan University Journal of Natural Sciences》2011年第4期301-307,共7页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Basic Research Program of China (973 Program) (2006CB303000)

摘　　要：In order to detect targets from the hyper-spectral images captured by unmanned aerial vehicles, the images are moved into a new characteristic space with greater divisibility by making use of the manifold learning theory. On this basis, a furation impulse response （FIR） filter is developed. The output energy can be minimized after images passing through a FIR filter. The target pixel and the background pixel are distinguished according to the restrained conditions. This method can effectively suppress noises and detect sub-pixel targets in the hyper-spectral remote sensing image of unknown background spectrum.In order to detect targets from the hyper-spectral images captured by unmanned aerial vehicles, the images are moved into a new characteristic space with greater divisibility by making use of the manifold learning theory. On this basis, a furation impulse response （FIR） filter is developed. The output energy can be minimized after images passing through a FIR filter. The target pixel and the background pixel are distinguished according to the restrained conditions. This method can effectively suppress noises and detect sub-pixel targets in the hyper-spectral remote sensing image of unknown background spectrum.

关 键 词：wireless sensor network target detection manifold learning theory constrained energy minimization 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]