Graph Signal Sampling-Based Active Learning for Hyperspectral Image Classification

Few-shot learning (FSL) uses prior knowledge and supervised experience to effectively classify hyperspectral images (HSIs), thereby reducing the cost of large numbers of labeled samples. However, existing few-shot methods ignore the correlation between cross-domain feature channels, and the feature representation ability is insufficient. To address above issue, this paper proposes a novel Residual Channel Attention Based Sample Adaptation Few-Shot Learning for Hyperspectral Image Classification(RCASA-FSL) for hyperspectral image classification (HSIC), which can capture and enhance cross-domain dependencies through multi-layer residual connection and random-based feature recalibration. Specifically, a Deep Residual Feature Channel Attention Mechanism (DRFCAM) is designed to obtain cross-domain dependencies by residual concatenation, and further the residual structure is stacked for mining depth discrimination information. Furthermore, a new Random-based Feature Recalibration Module (RFRM) is proposed to reassign the feature weights via random matrix, which fully explore feature weight relationships to guide the sample adaptation process. Besides, we design a joint loss function with combining the FSL loss and domain adaptive loss for further optimization model. Experiments conducted on several standard hyperspectral datasets demonstrate that the proposed RCASA-FSL is superior to other FSL techniques in both quantitative and qualitative aspects.

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework that divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.

The vision transformer (ViT) has become a hot topic in image processing due to its global feature extraction capabilities. However, the ViT suffers from over-smoothing in feature extraction and over-fitting in the training procedure, so it is hard to achieve satisfactory performance in hyperspectral image (HSI) classification. To address these issues, we propose a ViT with contrastive learning (CViT). The network architecture includes a patch embedding module, transformer blocks, and a classifier. The training of CViT can be considered as an optimization problem with a supervised contrastive loss, an unsupervised contrastive loss, and an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularizer with respect to linear self-attention weights. Specifically, the supervised contrastive loss is proposed to alleviate the negative effects of HSI features’ spectral variability and spatial diversity by increasing intra-class consistency. On the other hand, the unsupervised contrastive loss is exploited to reduce redundancy by reconstructing global structural information. In particular, regularized linear self-attention weights reduce the over-smoothing issue. Extensive experimental results on three HSI datasets demonstrate that the proposed CViT achieves competitive performance.

In Hyperspectral Image (HSI) classification, acquiring large quantities of high-quality labeled samples is typically costly and impractical. Traditional deep learning methods are limited in such scenarios due to their dependence on sample quantities. To address this challenge, researchers have turned to Few-Shot Learning (FSL). Although existing FSL methods improve classification performance by enhancing domain invariance through domain adaptation, they often overlook the critical issue of high inter-class similarity and large intra-class variability. Moreover, during domain alignment, features of different categories within the same domain may become confused. To address these issues, this paper proposes a novel Domain-Invariant Few-Shot Contrastive Learning (DIFSCL) method, which combines domain adaptation and contrastive learning strategies to not only learn domain-invariant features but also significantly enhance inter-class discriminability. Based on this, we further design a multi-scale adaptive attention mechanism for a hyperspectral feature extraction network to more effectively extract and optimize generalized features within the DIFSCL framework, significantly improving intra-class consistency and inter-class discriminative capability of features. Experimental results on three widely used HSI datasets demonstrate that our method significantly outperforms existing techniques in few-shot classification tasks.

Hyperspectral image classification is an important topic in the field of remote sensing. However, the high dimensionality and high spatial-spectral correlation of hyperspectral image will easily lead to poor classification performance due to the Hughes phenomenon. In this paper, we proposed a adaptive group local Riemannian embedding, called AGLRE, to extract the spatial and spectral features from hyperspectral image. It firstly mapped original data into a Riemannian manifold by constructing region covariance matrices for each pixel of hyperspectral image. And the multiple local tangent space on Riemannian manifold were learned by adaptive neighbourhood strategy. Lastly, the local linear embedding was applied to reduce the dimensionality and merge multiple tangent space into a global coordinate. Experimental results on public hyperspectral data set showed that the proposed method can achieve higher classification performance than other competing algorithms.

Class incremental learning with analytic learning for hyperspectral image classification

Active learning can effectively reduce labelling effort for remote sensing image classification. In this paper, we propose a new active learning method for hyperspectral image classification. We consider batch mode active learning and relatively large amount of data which can be a problem when using current state of the art algorithm based on kernel machines. The active learning procedure is based on the uncertainty sampling strategy and a deep neural network. Stacked autoencoders trained on redundant spatial and spectral features and a few labeled training samples are used to initialize a deep neural network. Uncertainty for a given sample is measured by the difference between the largest two class outputs of the neural network. The less difference there is, the more uncertainty the sample has. Batch of samples with most uncertainty will be selected after label query and added into the training set. Then the neural network is retrained. And such active batch selection will iterate until the budget (the upper limit of label queries) is reached. Experimental results on Pavia university dataset showed that our method outperforms the current support vector machines (SVMs) based multiclass/level uncertainty (MCLU) method both in classification accuracy and generalization capability.

Active learning can effectively reduce labelling effort for remote sensing image classification. In this paper, we propose a new active learning method for hyperspectral image classification. We consider batch mode active learning and relatively large amount of data which can be a problem when using current state of the art algorithm based on kernel machines. The active learning procedure is based on the uncertainty sampling strategy and a deep neural network. Stacked autoencoders trained on redundant spatial and spectral features and a few labeled training samples are used to initialize a deep neural network. Uncertainty for a given sample is measured by the difference between the largest two class outputs of the neural network. The less difference there is, the more uncertainty the sample has. Batch of samples with most uncertainty will be selected after label query and added into the training set. Then the neural network is retrained. And such active batch selection will iterate until the budget (the upper limit of label queries) is reached. Experimental results on Pavia university dataset showed that our method outperforms the current support vector machines (SVMs) based multiclass/level uncertainty (MCLU) method both in classification accuracy and generalization capability.

In this work, we propose a new semisupervised active learning approach for hyperspectral image classification. The proposed method aims at improving machine generalization by using pseudolabeled samples, both confident and informative, which are automatically and actively selected, via semisupervised learning. The learning is performed under two assumptions: a local one for the labeling via a superpixel-based constraint dedicated to the spatial homogeneity and adaptivity into the pseudolabels, and a global one modeling the data density by a multinomial logistic regressor with a Markov random field regularizer. Furthermore, we propose a density-peak-based augmentation strategy for pseudolabels, due to the fact that the samples without manual labels in their superpixel neighborhoods are out of reach for the automatic sampling. Three real hyperspectral datasets were used in our experiments to evaluate the effectiveness of the proposed superpixel-based semisupervised learning approach. The obtained results indicate that the proposed approach can greatly improve the potential for semisupervised learning in hyperspectral image classification.

Variations in atmosphere, lighting, and imaging systems result in diverse category distributions in hyperspectral imagery, impacting the accuracy of cross-domain hyperspectral image classification (HSIC). Unsupervised domain adaptation (UDA) aims to address this issue by learning a model that generalizes effectively across domains, leveraging labels only from source domain (SD). Most existing UDA methods focus on aligning distributions between domains without fully considering the valuable information within individual domains. To fill this gap, this letter proposes a domain-collaborative contrastive learning (DCCL) method. DCCL integrates a novel pseudo-labeling strategy with a cross-domain contrastive learning (CL) framework. Specifically, in the pseudo-labeling phase, the confident examples in target domain (TD) are collaboratively labeled according to the labeled examples in SD and the class centers in TD. Then, the CL phase simultaneously minimizes in-domain and cross-domain contrastive loss to promote the aggregation of examples from the same category in both domains. Experimental results demonstrate that the DCCL achieves the accuracy rates of 93.47% and 54.59% on Pavia and Indiana datasets, respectively, surpassing the performance of other state-of-the-art UDA methods. Our source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Leap-luohaiyang/DCCL-2024</uri>.

Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions

Despite the rapid development of deep learning in hyperspectral image classification (HSIC), most models require a large amount of labeled data, which are both time-consuming and laborious to obtain. However, contrastive learning can extract spatial–spectral features from samples without labels, which helps to solve the above problem. Our focus is on optimizing the contrastive learning process and improving feature extraction from all samples. In this study, we propose the Unlocking-the-Potential-of-Data-Augmentation (UPDA) strategy, which involves adding superior data augmentation methods to enhance the representation of features extracted by contrastive learning. Specifically, we introduce three augmentation methods—band erasure, gradient mask, and random occlusion—to the Bootstrap-Your-Own-Latent (BYOL) structure. Our experimental results demonstrate that our method can effectively improve feature representation and thus improve classification accuracy. Additionally, we conduct ablation experiments to explore the effectiveness of different data augmentation methods.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Few-shot learning (FSL) has achieved promising performance in hyperspectral image classification (HSIC) with few labeled samples by designing a proper embedding feature extractor. However, the performance of embedding feature extractors relies on the design of efficient deep convolutional neural network architectures, which heavily depends on the expertise knowledge. Particularly, FSL requires extracting discriminative features effectively across different domains, which makes the construction even more challenging. In this paper, we propose a novel neural architecture search-based FSL model for HSI classification, called HCFSL-NAS. Three novel strategies are proposed in this work. First, a neural architecture search-based embedding feature extractor is developed to the FSL in HSIC, whose search space includes a group of proposed multi-scale convolutions with channel attention. Second, a multi-source learning framework is employed to aggregate abundant heterogeneous and homogeneous source data, which enables the powerful generalization of network to the HSIC with only few labeled samples. Finally, the pointwise-based cross-entropy loss and the pairwise-based adaptive sparse loss are jointly optimized to maximize inter-class distance and minimize the distance within a class simultaneously. Experimental results on four publicly hyperspectral data sets demonstrate that HCFSL-NAS outperforms both the exiting FSL methods and supervised learning methods for HSI classification with only few labeled samples. Code is available at: https://github.com/xh-captain/HCFSL-NAS.

Graph knowledge distillation-guided few-shot learning for hyperspectral image classification