ALPN: Active-Learning-Based Prototypical Network for Few-Shot Hyperspectral Imagery Classification

Li, Xiaorun; Cao, Zeyu; Zhao, Liaoying; Jiang, Jianfeng

doi:10.1109/lgrs.2021.3101495

Cited by 20 publications

(7 citation statements)

References 15 publications

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“…To further verify the effectiveness of the proposed AL-MRIS method, several state-of-the-art classification methods, including DRIN [29], 3DCSN [20], S3Net [23], ALPN [26], FAAL [27], CFSL [16] and Gia-CFSL [17], were used for comparison. The corresponding classification maps are shown in Figures 9-12.…”

Section: Comparison Of Different Classification Methodsmentioning

confidence: 99%

“…Ma et al jointly used iterative training sampling and AL to iteratively update and enhance the initial training sample set to improve the HSI classification accuracy with small training samples [25]. Li et al combined semisupervised clustering technology and the AL strategy to develop an efficient prototype network-based framework that can extract representative features from few-shot samples to enhance representation ability [26]. Wang et al developed an adversarial AL strategy that captures variability HSI features and used advanced features to obtain heuristics through adversarial learning [27].…”

Section: Introductionmentioning

confidence: 99%

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AL-MRIS: An Active Learning-Based Multipath Residual Involution Siamese Network for Few-Shot Hyperspectral Image Classification

Yang,

Qin,

Qian

et al. 2024

Remote Sensing

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In hyperspectral image (HSI) classification scenarios, deep learning-based methods have achieved excellent classification performance, but often rely on large-scale training datasets to ensure accuracy. However, in practical applications, the acquisition of hyperspectral labeled samples is time consuming, labor intensive and costly, which leads to a scarcity of obtained labeled samples. Suffering from insufficient training samples, few-shot sample conditions limit model training and ultimately affect HSI classification performance. To solve the above issues, an active learning (AL)-based multipath residual involution Siamese network for few-shot HSI classification (AL-MRIS) is proposed. First, an AL-based Siamese network framework is constructed. The Siamese network, which has relatively low demand for sample data, is adopted for classification, and the AL strategy is integrated to select more representative samples to improve the model’s discriminative ability and reduce the costs of labeling samples in practice. Then, the multipath residual involution (MRIN) module is designed for the Siamese subnetwork to obtain the comprehensive features of the HSI. The involution operation was used to capture the fine-grained features and effectively aggregate the contextual semantic information of the HSI through dynamic weights. The MRIN module comprehensively considers the local features, dynamic features and global features through multipath residual connections, which improves the representation ability of HSIs. Moreover, a cosine distance-based contrastive loss is proposed for the Siamese network. By utilizing the directional similarity of high-dimensional HSI data, the discriminability of the Siamese classification network is improved. A large number of experimental results show that the proposed AL-MRIS method can achieve excellent classification performance with few-shot training samples, and compared with several state-of-the-art classification methods, the AL-MRIS method obtains the highest classification accuracy.

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Section: Comparison Of Different Classification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AL-MRIS: An Active Learning-Based Multipath Residual Involution Siamese Network for Few-Shot Hyperspectral Image Classification

Yang,

Qin,

Qian

et al. 2024

Remote Sensing

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“…Active Learning (AL) has been considered an effective method to reduce the labeling cost as well as acquire a large number of labeled training samples [66]. AL is based on three main aspects; 1): The availability of initial training set X train , 2): The availability of pool set (validation set in this work) X val , 3): Query function e.g., informative sample selection or acquisition function.…”

Section: B Active Learning (Al)mentioning

confidence: 99%

A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification

Ghous

Hong

Khan

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Convolutional Neural Networks (CNNs) have been extensively studied for Hyperspectral Image Classification (HSIC). However, CNNs are critically attributed to a large number of labeled training samples, which outlays high costs in terms of time and resources. Moreover, CNNs are trained on some samples and have been tested on the entire HSI. Perhaps, the entire HSI is taken into account at test time to appropriately generate the ground truth maps. In order to obtain a higher accuracy while considering the limited availability of training samples and disjoint validation and test samples, this work proposes a fast and compact 3D CNN-based Active Learning (AL) for HSIC that integrates both deep transfer learning and AL into a unified framework. In the proposed methodology, a 3D CNN model is trained with very few training samples (i.e., 5%, only) and in the next phase, the most informative and heterogeneous samples are queried from the validation set (candidate set) based on the fuzziness, mutual information and breaking ties of the trained model. The 3D CNN model is later fine-tuned (rather retraining from scratch) with the new training samples (i.e., 200 samples are selected in each iteration) to reduce the computational cost. The proposed method has been compared with the state-of-the-art traditional and deep models proposed for HSIC. Experimental results proved the superiority of our proposed method on several benchmark HSI datasets with significantly fewer labeled samples.Matlab demo can be accessed on GitHub: github.com/mahmad00

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“…Apart from pre-processing, existing works also focus on addressing FS classification issues through the design of training strategies, which can be roughly categorized as strict Few-Shot Learning (FSL) [10,11], semi-supervised learning (SSL) [12,13], and active learning (AL) [14,15]. FSL models often rely on meta-learning training strategies that utilize a limited number of labeled samples and an auxiliary set to construct multiple meta-learning tasks [16], enabling the acquisition of an initialization model with strong generalization capabilities.…”

Section: Introductionmentioning

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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ALPN: Active-Learning-Based Prototypical Network for Few-Shot Hyperspectral Imagery Classification

Cited by 20 publications

References 15 publications

AL-MRIS: An Active Learning-Based Multipath Residual Involution Siamese Network for Few-Shot Hyperspectral Image Classification

AL-MRIS: An Active Learning-Based Multipath Residual Involution Siamese Network for Few-Shot Hyperspectral Image Classification

A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification

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

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