基于空间相关性约束稀疏表示的高光谱图像分类
doi: 10.3724/SP.J.1146.2012.00577
Spatial Correlation Constrained Sparse Representation for Hyperspectral Image Classification
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摘要: 该文提出一种新的基于稀疏表示的高光谱图像分类方法。首先利用训练数据构造结构化字典，建立基于稀疏表示的高光谱图像分类模型；然后添加空间相关性约束项和训练数据的空间信息，提高稀疏表示模型分类的准确性；最后采用快速的交替方向乘子法求解模型。实验结果表明：该文方法能够有效提高分类精度，且分类结果稳定。Abstract: A novel classification method of hyperspectral image based on sparse representation is proposed. First, the training data is used to design a structured dictionary, and a classification model of hyperspectral image is built based on sparse representation; Then the spatial correlation and the spatial information of training data are added to improve the accuracy of this model; Finally it is solved by the rapid alternating direction method of multipliers. The experimental results show that the proposed method can improve the classification results, and the results are stable.
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Key words:
- Hyperspectral image /
- Sparse representation /
- Classification /
- Spatial correlation
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