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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Fast Low-Rank Decomposition Model-Based Hyperspectral Image Classification Method
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Fast Low-Rank Decomposition Model-Based Hyperspectral Image Classification Method

机译：基于快速低秩分解模型的高光谱图像分类方法

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摘要

In hyperspectral image classification, jointly using the pixels in an image patch can generally improve the performance. Recently, a new hyperspectral image classification method, which is based on low-rank decomposition model, was proposed by Chen et al. Although this algorithm can achieve state-of-the-art performance and outperform many contemporary classification techniques by jointly classifying both the central pixel and its similar neighboring pixels in the image patch, its computational load is heavy due to its pixelwise processing scheme, which limits its possible applications in practice. In this letter, we proposed a fast groupwise version of this method. In this improved method, the low-rank decomposition model is first used to partition the hyperspectral scene into many groups of similar pixels. Then, the pixels in each similar group would be classified jointly and assigned the same class label. The proposed groupwise classification method can achieve similar performance with the approach described by Chen et al. but with much reduced computational load. Our results demonstrate that when more observations are simultaneously used, the performance of the jointly sparse regression model would increase, and when the number of observations reaches a certain amount, the performance would no longer increase significantly. Our experimental results also demonstrate that, when the jointly sparse regression model is used, the quality is preferred to the quantity of the observations after the number of observations has reached a certain amount.
机译：在高光谱图像分类中，共同使用图像块中的像素通常可以提高性能。最近，Chen等人提出了一种基于低秩分解模型的高光谱图像分类新方法。尽管此算法可以通过对图像补丁中的中心像素及其类似的相邻像素进行共同分类来实现最新的性能并优于许多现代分类技术，但由于其像素级处理方案，其计算量很大，这限制了在实践中可能的应用。在这封信中，我们提出了该方法的快速逐组版本。在这种改进的方法中，首先使用低秩分解模型将高光谱场景划分为许多相似像素组。然后，每个相似组中的像素将被共同分类并分配相同的类别标签。提出的分组分类方法可以达到与Chen等人描述的方法相似的性能。但大大减少了计算量。我们的结果表明，当同时使用更多观测值时，联合稀疏回归模型的性能将提高，而当观测值数量达到一定数量时，性能将不再显着提高。我们的实验结果还表明，当使用联合稀疏回归模型时，在观察数量达到一定数量后，质量优于观察数量。

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