MSSPG-AL: Few-Shot Hyperspectral Image Classification with Active Learning Updated Multi-Scale Superpixel Graph Fusion

In recent years, deep neural networks have been widely used for hyperspectral image (HSI) classification and have shown excellent performance using numerous labeled samples. The acquisition of HSI labels is usually based on the field investigation, which is expensive and time consuming. Hence, the available labels are usually limited, which affects the efficiency of deep HSI classification methods. To improve the classification performance while reducing the labeling cost, this article proposes a semisupervised deep learning (DL) method for HSI classification, named pyramidal dilation attention convolutional network with active and self-paced learning (PDAC-ASPL), which integrates active learning (AL), self-paced learning (SPL), and DL into a unified framework. First, a densely connected pyramidal dilation attention convolutional network is trained with a limited number of labeled samples. Then, the most informative samples from the unlabeled set are selected by AL and queried real labels, and the highest confidence samples with corresponding pseudo labels are extracted by SPL. Finally, the samples from AL and SPL are added to the training set to retrain the network. Compared with some DL- and AL-based HSI classification methods, our PDAC-ASPL achieves better performance on four HSI datasets.

Convolutional Neural Networks (CNNs) have been extensively applied to hyperspectral (HS) image classification tasks and achieved promising performance. However, for CNN based HS image classification methods, it is hard to depict the dependencies among HS image pixels in long-range distanced positions and bands. Moreover, the limited receptive field of the convolutional layers extremely hinders the development of the CNN structure. To tackle these problems, in this paper, the novel Bottleneck Spatial-Spectral Transformer (BS2T) is proposed to depict the long-range global dependencies of HS image pixels, which can be regarded as a feature extraction module for HS image classification networks. More specifically, inspired by Bottleneck Transformer in computer vision, for HS image feature extraction, the proposed BS2T is incorporated with a feature contraction module, a multi-head spatial-spectral self-attention (MHS2A) module and a feature expansion module. In this way, convolutional operations are replaced by the MHS2A to capture the long-range dependency of HS pixels regardless of their spatial position and distance. Meanwhile, in the MHS2A module, to highlight the spectral features of HS images, we introduce the spectral information and content spatial positional information to classical multi-head self-attentions to make the attentions more positional aware and spectral aware. On this basis, a dual-branch HS image classification framework based on 3D CNN and BS2T is defined for jointly extracting the local-global features of HS images. Experimental results on three public HS image classification datasets show that the proposed classification framework achieves a significant improvement when comparing with the state-of-the-art methods. The source code of the proposed framework can be downloaded from https://github.com/srxlnnu/BS2T.

Deep neural network has been extensively applied to hyperspectral image (HSI) classification recently. However, its success is greatly attributed to numerous labeled samples, whose acquisition costs a large amount of time and money. In order to improve the classification performance while reducing the labeling cost, this article presents an active deep learning approach for HSI classification, which integrates both active learning and deep learning into a unified framework. First, we train a convolutional neural network (CNN) with a limited number of labeled pixels. Next, we actively select the most informative pixels from the candidate pool for labeling. Then, the CNN is fine-tuned with the new training set constructed by incorporating the newly labeled pixels. This step together with the previous step is iteratively conducted. Finally, Markov random field (MRF) is utilized to enforce class label smoothness to further boost the classification performance. Compared with the other state-of-the-art traditional and deep learning-based HSI classification methods, our proposed approach achieves better performance on three benchmark HSI data sets with significantly fewer labeled samples.

In Hyperspectral image (HSI) classification, combining spectral information with spatial information has become an efficient measure to obtain good classification results, where spatial information is generally introduced in an unsupervised way or some complicated way. We introduce spatial coordinates as the spatial information in a simple supervised way and propose two HSI classification algorithms, where spatial coordinates of samples are regarded as the spatial features of samples. A spectral-spatial classification algorithm is proposed, named as HSI Classification Based on Spectral-Spatial Feature Fusion using Spatial Coordinates (SSFFSC). The HSI is divided into multiple small images in spatial dimension, and samples in each small image are randomly selected as training samples. Support vector machine (SVM) is used to classify the samples to obtain the probability of samples belonging to each class according to the spatial coordinate features and spectral features respectively. The probability features are further classified by SVM to achieve the final classification result. Considering that the performance of SSFFSC relies on the partition of HSI, SSFFSC is further combined with active learning (AL) as a new method named as HSI Classification Based on Active Learning and SSFFSC (SSFFSC-AL). Partition of HSI is omitted and the training samples are selected adaptively by AL’s sampling scheme. We find spatial coordinates are useful spatial information. SSFFSC and SSFFSC-AL run fast and improve the classification accuracy effectively by using the spatial coordinates as the spatial features. Experiments demonstrate that comparing with other algorithms, SSFFSC and SSFFSC-AL can obtain higher classification accuracy in less time.

The classification of hyperspectral images plays a critical role in the maintenance of remote image analysis, which has attracted a lot of research interest. Despite the fact that numerous methodologies, including unsupervised and supervised methods, have been presented, achieving an acceptable classification result remains a challenging task. Deep learning-based hyperspectral image (HSI) classification is gaining popularity, because of its efficient classification capabilities. When compared to traditional convolutional neural networks, graph-based deep learning provides the benefits of exhibiting class boundaries and modelling feature relationships. In hyperspectral image (HSI) classification, the most important problem is how to transform hyperspectral data into irregular domains from regular grids. This study describes a method for image classification that employs graph neural network (GNN) models. The input images are converted into region adjacency graphs (RAGs), where regions are super pixels and edges link nearby super pixels.

Graph convolution network (GCN) has been extensively applied to the area of hyperspectral image (HSI) classification. However, the graph can not effectively describe the complex relationships between HSI pixels and the GCN still faces the challenge of insufficient labeled pixels. In order to alleviate the above two issues faced by the GCN in HSI classification, we propose a novel framework that integrates the active learning and the hypergraph neural network. First, we construct a hypergraph that can reveal the complex non-pairwise relationships embedded in the hyperspectral images. Next, we train a semi-supervised hypergraph neural network (GNN) with the fewer labeled training set. Then, exploiting the local structural properties of the hypergraph, the most useful HSI pixels are actively selected for labeling. Finally, we fine-tune the GNN with original training set along with the newly labeled pixels. And the last three steps are iteratively carried on. Compared with the other traditional and active learning approaches of HSI classification, the proposed active hypergraph neural network (ACGNN) can achieve better performance on the three HSI datasets.

Graph neural networks (GNNs) have recently garnered significant attention due to their exceptional performance across various applications, including hyperspectral (HS) image classification. However, most existing GNN-based models for HS image classification are limited depth models and often suffer from performance degradation as model depth increases. This study introduces HyperGCN, an exclusive GNN-based model designed with multiple graph convolutional layers to exploit the rich spectral information inherent in HS images, thereby enhancing classification performance. To address performance degradation, HyperGCN incorporates techniques resistant to oversmoothing into its architecture. Additionally, multiple-side exit branches are integrated into the intermediate layers of HyperGCN, enabling dynamic management of the complexity of HS images. Less complex HS images are processed by fewer layers, exiting early via attached branches, while more complex images traverse multiple layers until reaching the final output layer. Extensive experiments on four benchmark HS datasets (Indian Pines, Pavia University, Salinas, and Botswana) demonstrate HyperGCN’s superior performance over basic GNN-based models. Notably, HyperGCN outperforms or performs comparably to the CNN-GNN combined model in classifying HS images. Furthermore, the superior performance of multi-exit HyperGCN over its single-exit counterpart emphasizes the effectiveness of incorporating side exit branches in GNN-based HS image classification. Compared to state-of-the-art models, multi-exit HyperGCN demonstrates competitive performance, highlighting its effectiveness in handling complex spectral information in HS images while maintaining an acceptable balance between accuracy and computational efficiency.

Hyperspectral image (HSI) classification is a challenging problem due to the high dimensional features, high intra-class variance, and limited prior information, and the classification is the basis for HSI applications. Active learning (AL) and semisupervised learning (SSL) are two promising approaches in the HSI classification. In AL, the traditional entropy query-by-bagging (EQB) algorithm only pays attention on uncertainty and ignore the diversity among the samples. Therefore, we propose averaged normalized entropy query-by-bagging (anEQB) algorithm. Meanwhile, the collaborative active learning and semisupervised learning framework (CASSL) may invoke many wrong pseudolabels and deteriorate the classification performance. To make up for the deficiency of CASSL, we complement different AL algorithms to constitute a multiple filtering mode semisupervised learning framework (MFMSLF). To further study, we introduce syncretic secondary filtering mode into multiple verification semisupervised framework and thus constitute a multiple secondary filtering mode semisupervised verification framework (MSFMSVF). We evaluate the performance of anEQB, MFMSLF, and MSFMSVF on different hyperspectral data sets and compare them with other state-of-the-art HSI classification methods. Numerical experimental results reveal the superior classification performance of anEQB, MFMSLF, and MSFMSVF, respectively. Experimental results also demonstrate that exploring the information and diversity of the samples from different criterion can improve the classification performance of the collaborative framework.

Hyperspectral image (HSI) classification has recently been successfully explored by using deep learning (DL) methods. However, DL models rely heavily on a large number of labeled samples, which are laborious to obtain. Therefore, finding a way to efficiently embed DL models in limited labeled samples is a hot topic in the field of HSI classification. In this paper, an active learning-based siamese network (ALSN) is proposed to solve the limited labeled samples problem in HSI classification. First, we designed a dual learning-based siamese network (DLSN), which consists of a contrastive learning module and a classification module. Secondly, in view of the problem that active learning is difficult to effectively sample under the extremely limited labeling cost, we proposed an adversarial uncertainty-based active learning (AUAL) method to query valuable samples, and to promote DLSN to learn a more complete feature distribution by fine-tuning. Finally, an active learning architecture, based on inter-class uncertainty (ICUAL), is proposed to construct a lightweight sample pair training set, fully extracting the inter-class information of sample pairs and improving classification accuracy. Experiments on three generic HSI datasets strongly demonstrated the effectiveness of ALSN for HSI classification, with performance improvements over other related DL methods.

Hyperspectral image(HSI) Classification is one of the most prevalent issue in remote sensing area. Recently, application of deep learning in HSI classification has emerged. However, merging spatial features with spectral properties in deep learning is a pervasive problem. This paper presents, a discriminative spatial updated deep belief network (SDBN) which effectively utilizes spatial information within spectrally identical contiguous pixels for HSI classification. In the proposed approach, HSI is first segmented into adaptive boundary adjustment based spatially similar regions with similar spectral features, following which an object-level feature extraction and classification is undertaken using deep belief network (DBN) based decision fusion approach that incorporate spatial-segmented contextual and spectral information into a DBN framework for effective spectral-spatial HSI classification. Moreover, for improved accuracy, band preference/correlation based feature selection approach is used to select the most informative bands without compromising the original content in HSI. Usage of local contextual features and spectral similarity from adaptive boundary adjustment based approach, and integration of spatial and spectral features into DBN results into improved accuracy of the final HSI classification. Experimental results on well known hyperspectral data indicates the classification accuracy of the proposed method over several existing techniques.

Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers.

Hyperspectral image (HSI) classification is an essental task of HSI analysis, which aims to assign each pixel a pre-defined class label. Though deep learning based methods dominate the HSI classification methods to date, the existing methods seldom consider how to directly model the uncertainty broadly exists in the HSI applications, which impedes their usage in real applications. To address this problem, we propose to directly model the uncertainty into the deep learning based HSI classification model and construct a specific network based on stochastic differential equation (SDE). The constructed network consists two subnets, in which one is utilized to well fit the HSI classification task and one is exploited to capture the uncertainty within the HSI classification. The constructed network can better depict the uncertainty, and thus result in better HSI classification performance. Experimental results demonstrate the effectiveness of the constructed model for HSI classification.

Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionary learning is proposed for such applications. The proposed algorithm selects training samples that maximize two selection criteria, namely representative and uncertainty. This algorithm trains a deep network efficiently by actively selecting training samples at each iteration. The proposed algorithm is applied for the classification of hyperspectral images, and compared with other classification algorithms employing active learning. It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.

State space models meet transformers for hyperspectral image classification

Over the past few decades, hyperspectral image (HSI) classification has garnered increasing attention from the remote sensing research community. The largest challenge faced by HSI classification is the high feature dimensions represented by the different HSI bands given the limited number of labeled samples. Deep learning and convolutional neural networks (CNNs), in particular, have been shown to be highly effective in several computer vision problems such as object detection and image classification. In terms of accuracy and computational cost, one of the best CNN architectures is the Inception model i.e., the winner of the ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2014 challenge. Another architecture that has significantly improved image recognition performance is the Residual Network (ResNet) architecture i.e., the winner of the ILSVRC 2015 challenge. Inspired by the incredible performance introduced by the Inception and ResNet architectures, we investigate the possibility of combining the core ideas of these two models into a hybrid architecture to improve the HSI classification performance. We tested this combined model on four standard HSI datasets, and it shows competitive results compared with other existing HSI classification methods. Our hybrid deep ResNet-Inception architecture obtained accuracies of 95.31% on the Pavia University dataset, 99.02% on the Pavia Centre scenes dataset, 95.33% on the Salinas dataset and 90.57% on the Indian Pines dataset.