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中文摘要：
高光谱遥感图像（简称高光谱图像）的空间分辨率通常较低,混合像元现象严重。为了提高图像的分类精度,必须计算出混合像元内每种纯地物所占的比例（丰度）。然而,受实际地物间复杂关系和大气散射的影响,高光谱图像像元内的光谱混合都是非线性的,这就使得传统的基于线性光谱混合模型的解混精度难以满足要求。为此,定义了广义的非线性混合模型,提出了一种基于二次散射的非线性混合模型——二次散射模型（secondary scat-tering model,SSM）。通过对模拟数据和AVIRIS实际数据的解混实验表明,相对于传统的线性光谱解混,基于该模型进行光谱解混得到了更精确的分类结果。
英文摘要：
As the linear mixture model cannot well characterize the resultant mixed spectra due to the complicated relations between different ground objects and the effect of atmospheric scattering, a nonlinear spectral mixture model- secondary scattering model is proposed in this paper. Computer simulated images and AVIRIS hyperspectral images of Cuprite district in America were tested, and the experimental results show that the decompostion result of the proposed model are much more precise than that of the traditional linear model. spectral mixture
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Detection of Regions of Interest in a High Spatial Resolution Remote Sensing Image Based on an Adapt
Remote Sensing Image Fusion Based on Integer Wavelet Transformation and Ordered Nonnegative Independ
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