Abstract
This paper proposes a novel method of segment-tree filtering to improve the classification accuracy of hyperspectral image (HSI). Segment-tree filtering is a versatile method that incorporates spatial information and has been widely applied in image preprocessing. However, to use this powerful framework in hyperspectral image classification, we must reduce the original feature dimensionality to avoid the Hughes problem; otherwise, the computational costs are high and the classification accuracy by original bands in the HSI is unsatisfactory. Therefore, feature extraction is adopted to produce new salient features. In this paper, the Semi-supervised Local Fisher (SELF) method of discriminant analysis is used to reduce HSI dimensionality. Then, a tree-structure filter that adaptively incorporates contextual information is constructed. Additionally, an initial classification map is generated using multi-class support vector machines (SVMs), and segment-tree filtering is conducted using this map. Finally, a simple Winner-Take-All (WTA) rule is applied to determine the class of each pixel in an HSI based on the maximum probability. The experimental results demonstrate that the proposed method can improve HSI classification accuracy significantly. Furthermore, a comparison between the proposed method and the current state-of-the-art methods, such as Extended Morphological Profiles (EMPs), Guided Filtering (GF), and Markov Random Fields (MRFs), suggests that our method is both competitive and robust.
Highlights
Hyperspectral image (HSI) classification is important for urban land use monitoring, crop growth monitoring, environmental assessment, etc
We found that when the training sample size increases, the classification accuracy alsoalso increases
A novel and efficient approach based on a Segment-Tree Filter has been proposed for hyperspectral image
Summary
Hyperspectral image (HSI) classification is important for urban land use monitoring, crop growth monitoring, environmental assessment, etc. Spatial information was represented using Markov Random Fields (MRFs) in [5,6], and classification has been performed using α–Expansion [7] and Belief Propagation [8], which are commonly used max-flow/min-cut algorithms in MRF optimization As morphological operations such as opening and closing involve neighboring pixel calculations, contextual information is naturally utilized in this manner. It is difficult to establish a proper window size for bilateral and guided filters [17], largely because objects of interest display the most prominent features at different scales Another merit of this scheme is that the segment-tree filter is more efficient than other EAFs because of its tree structure [24].
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