A Novel Band Selection and Spatial Noise Reduction Method for Hyperspectral Image Classification

Fu, Hang; Zhang, Aizhu; Sun, Guifan; Ren, Jinchang; Jia, Xiuping; Pan, Zhaojie; Ma, Hongliang

doi:10.1109/tgrs.2022.3189015

Cited by 42 publications

(14 citation statements)

References 56 publications

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“…In addition to qualitative analysis, three popular quantitative analysis standards, overall accuracy (OA), average accuracy (AA), and Kappa coefficient (Kappa), are also used as experimental evaluation indicators. To illustrate the advance of the proposed algorithm, it compared with state-of-the-art band selection methods such as LvaHAI [39], EGCSR_BS [40], IBRA-GSS [41], and NGNMF-E2DSSA [42]. Furthermore, comparative experiments of all bands are added to intuitively analyze the performance.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, the experimental results can verify the aforementioned Hughes phenomenon that the classification accuracy does not always increase with the increase of the number of bands. For example, both BSNet-Conv [27] and NGNMF-E2DSSA [42] have a clear downward trend when the number of bands exceeds 15. This phenomenon occurs earlier in SpaBS [12], and when the band is greater than 10, there is a downward trend.…”

Section: ) Classification Performance With Different Numbers Ofmentioning

confidence: 99%

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Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

Wang

Liu

Cui

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Feature selection is an important way to achieve high precision and efficient classification of hyperspectral remote sensing images. However, most existing FS methods use a fixed scale to extract features and the relationship between spatial and spectral dimensions is ignored. In fact, this correlation is useful for classification. In this paper, a multi-scale feature fusion network (MSFGW) is proposed in which a global weighting mechanism is explored to catch spatial-spectral information at multiple scales. First, the multi-scale feature extraction module composed of group convolution and dilated convolution is utilized to extract the multiscale features. With the increase of the dilation rate, the module takes the spatial differences at varying scales. Secondly, a 3D weighting mechanism is used to combine the spatial and spectral correlated information for reducing the interference of homologous and heterologous and boosting the feature discrimination ability. Then, multi-scale weighted features are fused to integrate the internal information of all bands at different scales. Finally, the band reconstruction network is used to select representative bands according to their entropy. The experimental results with the state-of-the-art feature selection algorithms on three widely hyperspectral datasets demonstrate that the features selected by MSFGW have obvious advantages in classification with only a few training samples.

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Section: Resultsmentioning

confidence: 99%

Section: ) Classification Performance With Different Numbers Ofmentioning

confidence: 99%

Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

Wang

Liu

Cui

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Hyperspectral rich band information often contains richer features. We can select the band by the sensitivity of different ground objects to different bands to highlight certain objects [99].…”

Section: A the Data Types Of Remote Sensingmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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“…Due to the high spectral dimensionality of HSI, it is still difficult to obtain a large number of training samples based on existing techniques, leading to the Hughes phenomenon in HSI. Due to the robustness of SVM to Hughes phenomenon [43], in the classification stage, SVM with a quintuple cross-validated Gaussian kernel is selected for the final implementation of classification.…”

Section: Classifiermentioning

confidence: 99%

Fast and Accurate Hyperspectral Image Classification with Window Shape Adaptive Singular Spectrum Analysis

Bai,

Qi,

Jin

et al. 2023

Remote Sensing

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Hyperspectral classification is a task of significant importance in the field of remote sensing image processing, with attaining high precision and rapid classification increasingly becoming a research focus. The classification accuracy depends on the degree of raw HSI feature extraction, and the use of endless classification methods has led to an increase in computational complexity. To achieve high accuracy and fast classification, this study analyzes the inherent features of HSI and proposes a novel spectral–spatial feature extraction method called window shape adaptive singular spectrum analysis (WSA-SSA) to reduce the computational complexity of feature extraction. This method combines similar pixels in the neighborhood to reconstruct every pixel in the window, and the main steps are as follows: rearranging the spectral vectors in the irregularly shaped region, constructing an extended trajectory matrix, and extracting the local spatial and spectral information while removing the noise. The results indicate that, given the small sample sizes in the Indian Pines dataset, the Pavia University dataset, and the Salinas dataset, the proposed algorithm achieves classification accuracies of 97.56%, 98.34%, and 99.77%, respectively. The classification speed is more than ten times better than that of other methods, and a classification time of only about 1–2 s is needed.

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A Novel Band Selection and Spatial Noise Reduction Method for Hyperspectral Image Classification

Cited by 42 publications

References 56 publications

Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Fast and Accurate Hyperspectral Image Classification with Window Shape Adaptive Singular Spectrum Analysis

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