EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification

Zhang, Hongyan; Zou, Jiaqi; Zhang, Liangpei

doi:10.1109/tgrs.2022.3163326

Cited by 36 publications

(7 citation statements)

References 51 publications

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“…Consequently, researchers have turned to semi-supervised learning methods to improve the classification performance of these models by utilizing both labeled and unlabeled samples. Commonly employed semi-supervised learning methods in HSI classification encompass self-training models [28], generative models [29], and graph models [30][31][32][33].…”

Section: Semi-supervised Learning Methodsmentioning

confidence: 99%

Enhancing Semi-Supervised Few-Shot Hyperspectral Image Classification via Progressive Sample Selection

Zhao,

Zhang,

Huang

et al. 2024

Remote Sensing

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Hyperspectral images (HSIs) provide valuable spatial–spectral information for ground analysis. However, in few-shot (FS) scenarios, the limited availability of training samples poses significant challenges in capturing the sample distribution under diverse environmental conditions. Semi-supervised learning has shown promise in exploring the distribution of unlabeled samples through pseudo-labels. Nonetheless, FS HSI classification encounters the issue of high intra-class spectral variability and inter-class spectral similarity, which often lead to the diffusion of unreliable pseudo-labels during the iterative process. In this paper, we propose a simple yet effective progressive pseudo-label selection strategy that leverages the spatial–spectral consistency of HSI pixel samples. By leveraging spatially aligned ground materials as connected regions with the same semantic and similar spectrum, pseudo-labeled samples were selected based on round-wise confidence scores. Samples within both spatially and semantically connected regions of FS samples were assigned pseudo-labels and joined subsequent training rounds. Moreover, considering the spatial positions of FS samples that may appear in diverse patterns, to fully utilize unlabeled samples that fall outside the neighborhood of FS samples but still belong to certain connected regions, we designed a matching active learning approach for expert annotation based on the temporal confidence difference. We identified samples with the highest training value in specific regions, utilizing the consistency between predictive labels and expert labels to decide whether to include the region or the sample itself in the subsequent semi-supervised iteration. Experiments on both classic and more recent HSI datasets demonstrated that the proposed base model achieved SOTA performance even with extremely rare labeled samples. Moreover, the extended version with active learning further enhances performance by involving limited additional annotation.

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Section: Semi-supervised Learning Methodsmentioning

confidence: 99%

Enhancing Semi-Supervised Few-Shot Hyperspectral Image Classification via Progressive Sample Selection

Zhao,

Zhang,

Huang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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“…For the supervised segmentation loss, we employ the conventional cross-entropy loss function [30], [31] to minimize the disparity between the segmentation results and their corresponding reference labels, i.e.,…”

Section: Loss Functionmentioning

confidence: 99%

Cross-City Semantic Segmentation (C2Seg) in Multimodal Remote Sensing: Outcome of the 2023 IEEE WHISPERS C2Seg Challenge

Liu,

Wang,

Zhang

et al. 2024

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

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Given the ever-growing availability of remote sensing data (e.g., Gaofen in China, Sentinel in the EU, and Landsat in the USA), multimodal remote sensing techniques have been garnering increasing attention and have made extraordinary progress in various Earth observation (EO)-related tasks. The data acquired by different platforms can provide diverse and complementary information. The joint exploitation of multimodal remote sensing has been proven effective in improving the existing methods of land-use/land-cover segmentation in urban environments. To boost technical breakthroughs and accelerate the development of EO applications across cities and regions, one important task is to build novel cross-city semantic segmentation models based on modern artificial intelligence technologies and emerging multimodal remote sensing data. This leads to the development of better semantic segmentation models with high transferability among different cities and regions. The Cross-City Semantic Segmentation contest is organized in conjunction with the 13th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

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“…Additionally, they employed a sparsification strategy to remove task-irrelevant edges and utilized LP as a regularization method to learn appropriate edge weights, promoting the separation of node classes [16]. In order to capture the global and local information between superpixels at the same time, Zhang et al proposed a new superpixel classification method to refine the superpixel boundary, and adopted a new MixHop model for superpixels to capture both local information within superpixels and remote information between superpixels [17].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification

Wu,

Zhan,

et al. 2024

Remote Sensing

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With the development of hyperspectral sensors, the availability of hyperspectral images (HSIs) has increased significantly, prompting advancements in deep learning-based hyperspectral image classification (HSIC) methods. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains, and have been used for HSIC. The superpixel segmentation should be implemented first in the GCN-based methods, however, it is difficult to manually select the optimal superpixel segmentation sizes to obtain the useful information for classification. To solve this problem, we constructed a HSIC model based on a multiscale feature search-based graph convolutional network (MFSGCN) in this study. Firstly, pixel-level features of HSIs are extracted sequentially using 3D asymmetric decomposition convolution and 2D convolution. Then, superpixel-level features at different scales are extracted using multilayer GCNs. Finally, the neural architecture search (NAS) method is used to automatically assign different weights to different scales of superpixel features. Thus, a more discriminative feature map is obtained for classification. Compared with other GCN-based networks, the MFSGCN network can automatically capture features and obtain higher classification accuracy. The proposed MFSGCN model was implemented on three commonly used HSI datasets and compared to some state-of-the-art methods. The results confirm that MFSGCN effectively improves accuracy.

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EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification

Cited by 36 publications

References 51 publications

Enhancing Semi-Supervised Few-Shot Hyperspectral Image Classification via Progressive Sample Selection

Enhancing Semi-Supervised Few-Shot Hyperspectral Image Classification via Progressive Sample Selection

Cross-City Semantic Segmentation (C2Seg) in Multimodal Remote Sensing: Outcome of the 2023 IEEE WHISPERS C2Seg Challenge

Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification

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