Abstract
Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without considering the pixel-based Hyperspectral Image (HSI) classification, homogeneous regions may become unsmooth due to the noise to be introduced. To tackle this problem, we propose the weighted EMAPs (WEMAPs) to reduce the noise and smoothen the homogeneous regions based on weighted mean filter (WMF). Then, we construct multiscale WEMAPs to product multiscale feature in order to extract different spatial structures of the HSI and produce better classification results. Finally, a new joint decision fusion and feature fusion (JDFFF) framework is proposed based on the decision fusion (DF) and the multiscale WEMAPs (MWEMAPs) based on extreme learning machine (ELM) classifier. That is, the classification results from various scales are combined into a final one with ELM to perform the HSI classification. Experiment results show that the proposed algorithm significantly outperforms many state-of-the-art HSI classification algorithms.
Highlights
Hyperspectral images (HSIs) have been successfully applied in a wide range of applications, such as land and ocean mapping, geological analysis, brain cancer detection, mining, and precision agriculture [1,2,3]
In order to capture different spatial structures for more effectively modeling the discriminant information of the HSIs, a novel multiscale feature-level fusion (FF) framework namely the joint FF and decision fusion (DF) (JFFDF) based on both the weighted mean filter (WMF) and the proposed weighted EMAPs (WEMAPs) is proposed in this paper for HSI classification
We apply the proposed FF and JDFF to KSVM in order to show the good performance of proposed frameworks
Summary
Hyperspectral images (HSIs) have been successfully applied in a wide range of applications, such as land and ocean mapping, geological analysis, brain cancer detection, mining, and precision agriculture [1,2,3]. By combining different EAPs, the extended multi-attribute profiles (EMAPs) were introduced [22], which can better model the spatial information of the HSI. In order to capture different spatial structures for more effectively modeling the discriminant information of the HSIs, a novel multiscale FF framework namely the joint FF and DF (JFFDF) based on both the WMF and the proposed WEMAPs is proposed in this paper for HSI classification. Both the SVM and ELM have been widely used for HSI classification because of their abilities in handing the Hughes phenomenon [31].
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