【摘要】 为解决高光谱图像中存在噪声、空间结构复杂和光谱信息复杂等问题，提高分类算法的噪音处理与空间识别能力，提出应用特征感知与协同表示的高光谱图像分类方法。首先运用自适应加权方式对图像进行重建；然后通过计算空间偏置矩阵，对空间特征进行感知，通过计算光谱偏置矩阵对光谱特征进行感知；最后根据误差最小原则确定测试样本的类别信息。在标准数据集Pavia University和Salinas上的实验结果表明，该方法总体准确率达到98.96%和99.63%。与先进的分类算法相比，该算法在平滑噪声、感知特征和空间识别等方面效果更佳，具有较好的分类性能。更多还原

【Abstract】 In order to solve the problems of noise,complex spatial structure and complex spectral information in hyperspectral images,and improve the ability of noise processing and spatial recognition of the classification algorithm,a hyperspectral image classification method based on feature perception and collaborative representation was proposed. Firstly,the algorithm uses adaptive weighting to reconstruct the image. Then,the spatial features are perceived by calculating the spatial bias matrix,and the spectral features are perceived by calculating the spectral bias matrix.Finally,according to the principle of minimum error,the category information of the test samples is determined. The experimental results on the standard data sets Pavia University and Salinas show that the overall accuracy on the two data sets reaches 98.96% and 99.63%. Compared with the advanced classification algorithms,the algorithm has better effects in terms of smoothing noise,perceptual features and spatial recognition,which has better classification performance.更多还原