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In this paper, a new supervised classifica- tion method, combining spectral and spatial information, is proposed. The method is based on the two following facts. First, a hyperspectral pixel can be sparsely repre- sented by a linear combination of the dictionary consists of a few labeled samples. If any unknown hyperspectral pixel lies in the subspace spanned by some labeled-class samples, it will be classified to this labeled-class. And this is to solve a fully constrained sparse unmixing problem with the 12 regularization and the criterion of classification is relaxed to be determined by the largest value of sparse vector whose nonzero entries correspond to the weights of the labeled samples. Second, since the nearest neighbors probably belong to the same class, a spatial constraint is introduced. Alternating direction method of multipliers (ADMM) and the graph cut based method are then used to solve the spectral-spatial model. Finally, two real hy- perspectral data sets are used to validate our proposed method. Experimental results show that the proposed method outperforms many of the state-of-the-art methods.  相似文献
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为了实现复杂背景下的红外小目标检测, 提出了一种基于协作稀疏编码(CSC)的红外小目标检测算法。首先通过滑动窗口法提取待测 试图像的图像块,并将 其转化为列向量作为超完备字典;然后采用CSC模型计算每一个图像块在超完 备字典中的系数矩 阵以及误差矩阵,其中系数矩阵的L2,1范数代表图像的背景信息,而误 差矩阵的L1,2范数代表红外小目标信 息;进而利用ADMM(alternating directional method of multiplier)算法解 算,得到系数矩阵和误差矩阵;最后通 过误差矩阵重建,得到红外小目标的位置。仿真及公开数据实验结果,证实了本文方法的有 效性。  相似文献
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