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 the...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.展开更多
基金Supported by the National Basic Research Program of China (973 Program) (2006CB303000)
文摘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.
