Abstract
Joint sparse representation has been widely used for hyperspectral image classification in recent years, however, the equal weight assigned to each neighbouring pixel is less realistic, especially for the edge areas, and one fixed scale is not appropriate for the entire image extent. To overcome these problems, we propose an adaptive local neighbour selection strategy suitable for hyperspectral image classification. We also introduce a multi-level joint sparse model based on the proposed adaptive local neighbour selection strategy. This method can generate multiple joint sparse matrices on different levels based on the selected parameters, and the multi-level joint sparse optimization can be performed efficiently by a simultaneous orthogonal matching pursuit algorithm. Tests on three benchmark datasets show that the proposed method is superior to the conventional sparsity representation methods and the popular support vector machines.
Highlights
In recent years, remote sensing images have played an important role in many areas, such as surveillance, land-use classification, forest disturbance, and urban planning [1]
Since pixels with similar distances can be simultaneously sparsely represented by the features in the same subspace, and pixels from multiple levels may share different sparsity patterns, MLSR is designed to learn the dictionary for each joint sparse model separately; and (3) a simultaneous orthogonal matching pursuit (SOMP) algorithm is employed to learn the multi-level classification task
The sparsity representation classification (SRC) has been successfully used for Hyperspectral images (HSI) classification, we extend it to a multi-level version for the classification task
Summary
Remote sensing images have played an important role in many areas, such as surveillance, land-use classification, forest disturbance, and urban planning [1]. We propose a novel classification method with a name ‘multi-level joint sparse representation model’ (MLSR), in order to take advantage of the correlations among neighbouring pixels in a region. Since pixels with similar distances can be simultaneously sparsely represented by the features in the same subspace, and pixels from multiple levels may share different sparsity patterns, MLSR is designed to learn the dictionary for each joint sparse model separately; and (3) a simultaneous orthogonal matching pursuit (SOMP) algorithm is employed to learn the multi-level classification task. To sum up the main advantage of the proposed multi-level method, various parameter values can generate multiple sparse models to represent the different inner contextual structures among pixels, thereby improving the HSI classification accuracy.
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