Network For Hyperspectral Image Classification Research Articles

Attention 3D central difference convolutional dense network for hyperspectral image classification.

Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

Multi-task multi-objective evolutionary network for hyperspectral image classification and pansharpening

Multi-task learning has commonly been used and performed well at joint visual perception tasks. Hyperspectral pansharpening (HP) and hyperspectral classification (HC) tasks extract high-frequency information to enhance edges and classify samples, offering potential for performance improvements in multi-task learning. However, differences between tasks can make it challenging to balance their performances. To address this challenge, this paper proposes a multi-task multi-objective evolutionary network (DMOEAD) for joint learning of HC and HP. A multi-task sufficiency-and-diversity sampling method is designed to unify the heterogeneity of sample construction between two types of tasks. Two types of task-specific networks are constructed to decompose high-frequency information. Further, a collaborative learning module is designed to dynamically learn complementary high-frequency information from another task in different layers. To be compatible with the optimization direction of two types of tasks, multi-task optimization is realized using a deep multi-objective evolutionary algorithm (DMEO). In the DMEO, the set of parameters of the DMOEAD is regarded as an individual. A deep mutation operator is designed and used for network optimization, which accelerates large-scale network parameter searching. The DMEO can coordinate the differences between multiple tasks and provide a set of Pareto network parameter solutions. Finally, the experimental results demonstrate that the proposed method can significantly enhance the performance of both pansharpening and classification tasks.

A frequency and topology interaction network for hyperspectral image classification

Most existing Convolutional Neural Networks (CNNs), Transformers, and their variants have limitations in capturing relationships between hyperspectral image (HSI) data, leading to unclear descriptions of region boundaries and limited generalization abilities. While semi-supervised Graph Neural Networks (GNNs) come with higher computational costs. Therefore, this paper proposes a method interacting the frequency and topology information for HSI Classification to address the aforementioned shortcomings, which combines convolution and self-attention to capture both local and global contextual information, thereby enhancing feature representation. Additionally, this method focuses on exploring spectral and topological structure features and enhancing the information exchange and interaction to improve performance. Experimental results demonstrate that this method gains a competitive advantage in HSI classification by proving highly effective in handling spectral ambiguity and material heterogeneity. It also exhibits lower computational costs, making it more feasible and practical compared to most benchmark methods. Our code is available at https://github.com/youngboy03/FTINet.

SFFNet: Staged Feature Fusion Network of Connecting Convolutional Neural Networks and Graph Convolutional Neural Networks for Hyperspectral Image Classification

The immense representation power of deep learning frameworks has kept them in the spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks (GCNs) can be used to compensate for the lack of spatial information in Convolutional Neural Networks (CNNs). However, most GCNs construct graph data structures based on pixel points, which requires the construction of neighborhood matrices on all data. Meanwhile, the setting of GCNs to construct similarity relations based on spatial structure is not fully applicable to HSIs. To make the network more compatible with HSIs, we propose a staged feature fusion model called SFFNet, a neural network framework connecting CNN and GCN models. The CNN performs the first stage of feature extraction, assisted by adding neighboring features and overcoming the defects of local convolution; then, the GCN performs the second stage for classification, and the graph data structure is constructed based on spectral similarity, optimizing the original connectivity relationships. In addition, the framework enables the batch training of the GCN by using the extracted spectral features as nodes, which greatly reduces the hardware requirements. The experimental results on three publicly available benchmark hyperspectral datasets show that our proposed framework outperforms other relevant deep learning models, with an overall classification accuracy of over 97%.

Center-similarity spectral-spatial attention network for hyperspectral image classification

Center-similarity spectral-spatial attention network for hyperspectral image classification

A two-branch multiscale spectral-spatial feature extraction network for hyperspectral image classification

In the field of hyperspectral image (HSI) classification in remote sensing, the combination of spectral and spatial features has gained considerable attention. In addition, the multiscale feature extraction approach is very effective at improving the classification accuracy for HSIs, capable of capturing a large amount of intrinsic information. However, some existing methods for extracting spectral and spatial features can only generate low-level features and consider limited scales, leading to low classification results, and dense-connection based methods enhance the feature propagation at the cost of high model complexity. This paper presents a two-branch multiscale spectral-spatial feature extraction network (TBMSSN) for HSI classification. We design the multiscale spectral feature extraction (MSEFE) and multiscale spatial feature extraction (MSAFE) modules to improve the feature representation, and a spatial attention mechanism is applied in the MSAFE module to reduce redundant information and enhance the representation of spatial features at multiscale. Then we densely connect series of MSEFE or MSAFE modules respectively in a two-branch framework to balance efficiency and effectiveness, alleviate the vanishing-gradient problem and strengthen the feature propagation. To evaluate the effectiveness of the proposed method, the experimental results were carried out on bench mark HSI datasets, demonstrating that TBMSSN obtained higher classification accuracy compared with several state-of-the-art methods.

Robust Self-Ensembling Network for Hyperspectral Image Classification.

Recent research has shown the great potential of deep learning algorithms in the hyperspectral image (HSI) classification task. Nevertheless, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSI is laborious and time-consuming, developing algorithms that can yield good performance in the small sample size situation is of great significance. In this study, we propose a robust self-ensembling network (RSEN) to address this problem. The proposed RSEN consists of two subnetworks including a base network and an ensemble network. With the constraint of both the supervised loss from the labeled data and the unsupervised loss from the unlabeled data, the base network and the ensemble network can learn from each other, achieving the self-ensembling mechanism. To the best of our knowledge, the proposed method is the first attempt to introduce the self-ensembling technique into the HSI classification task, which provides a different view on how to utilize the unlabeled data in HSI to assist the network training. We further propose a novel consistency filter to increase the robustness of self-ensembling learning. Extensive experiments on three benchmark HSI datasets demonstrate that the proposed algorithm can yield competitive performance compared with the state-of-the-art methods.

Hyperspectral image classification based on convolutional neural network

Hyperspectral remote sensing is the acquisition of spatial and spectral data of objects by detecting the electromagnetic waves reflected from them using a hyperspectral sensor. It enables the evaluation and identification of material composition, morphology, structure, and other aspects. Currently, hyperspectral imaging is widely applied in fields such as agriculture, environmental monitoring, forestry, medicine, and geology. However, traditional hyperspectral image classification encounters the problem of high complexity and overfitting when dealing with small sample sizes, requiring the use of advanced convolutional neural network models to address this issue. This paper primarily employs literature analysis, review, and comparative analysis methods to summarize the main challenges encountered when using convolutional neural networks for hyperspectral image classification. It also selects popular models in recent years to introduce, namely 3D-CNN model, hyperspectral pyramidal ResNet model and HybridSN model. This article focuses on whether these three models can solve problems that traditional classification models cannot solve, and what room for improvement there is, which will provide a reference for future research.

Hybrid 2D–3D convolution and pre-activated residual networks for hyperspectral image classification

Hybrid 2D–3D convolution and pre-activated residual networks for hyperspectral image classification

Multi-Level Feature Extraction Networks for Hyperspectral Image Classification

Hyperspectral image (HSI) classification plays a key role in the field of earth observation missions. Recently, transformer-based approaches have been widely used for HSI classification due to their ability to model long-range sequences. However, these methods face two main challenges. First, they treat HSI as linear vectors, disregarding their 3D attributes and spatial structure. Second, the repeated concatenation of encoders leads to information loss and gradient vanishing. To overcome these challenges, we propose a new solution called the multi-level feature extraction network (MLFEN). MLFEN consists of two sub-networks: the hybrid convolutional attention module (HCAM) and the enhanced dense vision transformer (EDVT). HCAM incorporates a band shift strategy to eliminate the edge effect of convolution and utilizes hybrid convolutional blocks to capture the 3D properties and spatial structure of HSI. Additionally, an attention module is introduced to identify strongly discriminative features. EDVT reconfigures the organization of original encoders by incorporating dense connections and adaptive feature fusion components, enabling faster propagation of information and mitigating the problem of gradient vanishing. Furthermore, we propose a novel sparse loss function to better fit the data distribution. Extensive experiments conducted on three public datasets demonstrate the significant advancements achieved by MLFEN.

Dual-input ultralight multi-head self-attention learning network for hyperspectral image classification

ABSTRACT In the hyperspectral image (HSI) classification tasks, various deep learning models have achieved remarkable success. However, most deep learning models are compute-intensive, requiring significant computing power, time, and other resources. It becomes a challenge to pursue better results while saving computational resources. Therefore, a novel dual-input ultralight multi-head self-attention learning network (DUMS-LN) is proposed for HSI classification. The proposed DUMS-LN consists of three main core modules, namely the high-dimensional reduced module (HDRM), lightweight multi-head self-attention (LMHSA) module, and linearized hierarchical conversion module (LHCM). HDRM is used as a pre-processing module with efficient data compression and combines spatial and spectral information extraction from the raw data to provide cleaner and more comprehensive feature data for subsequent processing. In addition, the core computational module of DUMS-LN is the LMHSA module, which is lightweight but possesses better data processing capability than the traditional multi-head self-attention module. Finally, the LHCM divides the model into two phases, reducing the dimensionality of the feature data phase by phase so that the LMHSA module can perform feature extraction at different levels. Experiments on four benchmark HSI datasets show that the proposed DUMS-LN outperforms the comparison HSI classification algorithms regarding speed and classification accuracy.

Synergistic spectral and spatial feature analysis with transformer and convolution networks for hyperspectral image classification

Synergistic spectral and spatial feature analysis with transformer and convolution networks for hyperspectral image classification

Multi-scale feature learning via residual dynamic graph convolutional network for hyperspectral image classification

ABSTRACT Thanks to the relevant spectral-spatial information, hyperspectral images (HSIs) have been widely exploited in Earth observation. Recently, graph convolutional networks (GCNs) have attracted increasing attention in HSI classification due to their advantages in processing non-Euclidean structure data. Unlike convolutional neural networks (CNNs), which perform convolution operations on regular square regions, GCNs can directly work on graph structure data to extract the relationships among adjacent land covers. However, extracting meaningful and deep discriminative spectral-spatial features from HSIs is still a challenging task. In this article, a novel multi-scale feature learning via residual dynamic graph convolutional network is designed for HSI classification, which can extract large-scale contextual spatial structures at superpixel-level graph and local spectral-spatial information at pixel-level, significantly improving the performance of HSI classification. Unlike from the existing GCN-based methods that operate on a graph with a fixed neighbourhood size, multiple graphs with diverse neighbourhood scales are built to comprehensively leverage spectral-spatial information and relationship at multiple scales, and these graphs are dynamically updated to generate more discriminative features (via dynamic GCN) during the convolution process. Moreover, to fully use the multi-scale features extracted from HSIs, a multi-scale feature fusion module is developed to emphasize important features and suppress irrelevant ones. Extensive experiments carried on three benchmark data sets demonstrate the superiority of the proposed approach over other state-of-the-art methods.

Retracted: Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Retracted: Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Multiscale graph convolution residual network for hyperspectral image classification

Multiscale graph convolution residual network for hyperspectral image classification

SSC-SFN: spectral-spatial non-local segment federated network for hyperspectral image classification with limited labeled samples

ABSTRACT Hyperspectral image (HSI) classification methods based on deep learning (DL) have performed well in numerous investigations. Although many modified superpixel-wise neural networks are utilized to enhance spatial information, their ability to mine spectral information in graph structures is insufficient. Moreover, single classifier approaches are unable to extract adequate spatial and spectral information simultaneously. For the classification of large-scale research areas, many works have relied on the use of a large number of labeled samples, leading to low efficiency and weak generalization. To address these issues, an effective spectral-spatial HSI classification approach based on spectral-spatial non-local segment federated network (SSC-SFN) was developed in this study. In this framework, deconvolution is employed to recover the data size, while the lost spatial information is replaced by up-pooling. The spectral dimensional features are updated through the generation of non-Euclidean graph structures and the non-local segment smoothing technique. The convolutional neural network and graph convolutional network techniques are coupled to exploit the available spectral and spatial structure information fully. Extensive experimental results obtained using four public benchmark datasets show that the classification accuracy of SSC-SFN can exceed 90% for large-scale HSIs with limited samples.

Multi-Scale Spatial–Spectral Residual Attention Network for Hyperspectral Image Classification

Hyperspectral images (HSIs) encapsulate a vast amount of information due to their expansive size and high number of channel dimensions. However, they are insufficiently utilized for ineffective feature extraction, particularly for regions with few samples and predominant edges. To fully leverage the spatial–spectral features of HSIs, a dual-branch multi-scale spatial–spectral residual attention network (MSRAN) that integrates multi-scale feature extraction with residual attention mechanisms is proposed. MSRAN independently extracts spatial and spectral features through dual branches, minimizing the interference between these features and enhancing the focus on feature extraction in different dimensions. Specifically, in the spectral feature extraction branch, diverse-scale 3D convolution kernels capture extended spectral sequence characteristics and neighborhood spectral features. The convolution fusion emphasizes the weight of the central pixel to be classified, followed by the use of spectral residual attention mechanisms to extract enhanced central-pixel spectral features. In the spatial feature extraction branch, multi-level receptive fields are utilized to extract various fine-grained spatial contours, edges, and local detailed features, which are further processed through spatial residual attention to effectively extract spatial composite features. Finally, the convolution fusion module adaptively integrates the center-enhanced spectral features with multi-level fine-grained spatial features for classification. Extensive comparative experiments and ablation studies demonstrate that MSRAN achieves highly competitive results on two classic datasets from Pavia University and Salinas as well as on a novel dataset of WHU-Hi-LongKou.

Two-Stream spectral-spatial convolutional capsule network for Hyperspectral image classification

Recently, the capsule network and its enhanced version named convolutional capsule network (Conv-CapsNet) were applied to hyperspectral image (HSI) classification and delivered outstanding performance, attributing to the enriched capabilities of capsules for feature expression and complex spatial knowledge excavation. However, restricted by the locality of convolutions in primary feature extraction, the rich spectral-spatial diagnostic information found in HSIs were insufficiently utilized, which degrades classification performance to a certain degree. In addition, most capsule networks involve a complex structure with many parameters, which are haunted by a common bottleneck of limited available training samples. To tackle these challenges, this paper presents a novel two-stream spectral-spatial convolutional capsule network (TSCCN) for HSIs to boost accurate classification with small samples. Based on the advanced convolutional capsule concept, a two-stream Conv-CapsNet architecture is developed that reduces trainable parameters and mitigates overfitting when samples are insufficient. Specifically, a 1D Conv-CapsNet and a 2D Conv-CapsNet sharing similar structures are developed as accompanying streams to fully excavate favorable features from the continuous spectra and raw spectral-spatial cubes, with the separate features fused to generate joint deep features for classification. To overcome the locality of primary convolutions, an efficient structural information mining module (SIM) is designed for each stream, to learn complementary cross-channel and cross-kernel inter-dependencies for subsequent capsule learning and further improve classification performance. Experiments on three well-known HSI datasets demonstrate the superiority of the proposed algorithm over nine representative state-of-the-art deep classifiers and its efficacy under small sample conditions, where all the methods were implemented in the Tensor-Flow framework and repeated for 10 times.

Channel-Layer-Oriented Lightweight Spectral–Spatial Network for Hyperspectral Image Classification

Channel-Layer-Oriented Lightweight Spectral–Spatial Network for Hyperspectral Image Classification

Spectral–Spatial Large Kernel Attention Network for Hyperspectral Image Classification

Spectral–Spatial Large Kernel Attention Network for Hyperspectral Image Classification