Hyperspectral image classification (HSIC) methods usually require more training samples for better classification performance. However, a large number of labeled samples are difficult to obtain ...because it is cost- and time-consuming to label an HSI in a pixel-wise way. Therefore, how to overcome the problem of insufficient accuracy and stability under the condition of small labeled training sample size (SLTSS) is still a challenge for HSIC. In this paper, we proposed a novel multiple superpixel graphs learning method based on adaptive multiscale segmentation (MSGLAMS) for HSI classification to address this problem. First, the multiscale-superpixel-based framework can reduce the adverse effect of improper selection of a superpixel segmentation scale on the classification accuracy while saving the cost to manually seek a suitable segmentation scale. To make full use of the superpixel-level spatial information of different segmentation scales, a novel two-steps multiscale selection strategy is designed to adaptively select a group of complementary scales (multiscale). To fix the bias and instability of a single model, multiple superpixel-based graphical models obatined by constructing superpixel contracted graph of fusion scales are developed to jointly predict the final results via a pixel-level fusion strategy. Experimental results show that the proposed MSGLAMS has better performance when compared with other state-of-the-art algorithms. Specifically, its overall accuracy achieves 94.312%, 99.217%, 98.373% and 92.693% on Indian Pines, Salinas and University of Pavia, and the more challenging dataset Houston2013, respectively.
Deep learning (DL) based methods represented by convolutional neural networks (CNNs) are widely used in hyperspectral image classification (HSIC). Some of these methods have strong ability to extract ...local information, but the extraction of long-range features is slightly inefficient, while others are just the opposite. For example, limited by the receptive fields, CNN is difficult to capture the contextual spectral-spatial features from a long-range spectral-spatial relationship. Besides, the success of DL-based methods is greatly attributed to numerous labeled samples, whose acquisition are time-consuming and cost-consuming. To resolve these problems, a hyperspectral classification framework based on multi-attention Transformer (MAT) and adaptive superpixel segmentation-based active learning (MAT-ASSAL) is proposed, which successfully achieves excellent classification performance, especially under the condition of small-size samples. Firstly, a multi-attention Transformer network is built for HSIC. Specifically, the self-attention module of Transformer is applied to model long-range contextual dependency between spectral-spatial embedding. Moreover, in order to capture local features, an outlook-attention module which can efficiently encode fine-level features and contexts into tokens is utilized to improve the correlation between the center spectral-spatial embedding and its surroundings. Secondly, aiming to train a excellent MAT model through limited labeled samples, a novel active learning (AL) based on superpixel segmentation is proposed to select important samples for MAT. Finally, to better integrate local spatial similarity into active learning, an adaptive superpixel (SP) segmentation algorithm, which can save SPs in uninformative regions and preserve edge details in complex regions, is employed to generate better local spatial constraints for AL. Quantitative and qualitative results indicate that the MAT-ASSAL outperforms seven state-of-the-art methods on three HSI datasets.
Nowadays, graph convolutional networks (GCNs) are getting more attention in hyperspectral image classification (HSIC), and various algorithms based on GCNs have been proposed. However, because of ...hyperspectral images' (HSIs) complex spatial texture information, the long-range graph convolution (GConv) and short-range GConv may cause inaccurate or oversmoothed feature extraction of some nodes. Thus, a multiscale short- and long range graph convolutional network (MSLGCN) is proposed for HSIC. First, MSLGCN not only extracts spatial information of ground objects at different scales but also simultaneously captures global and local spectral features, which preserves objects' fine boundaries. Then, the rich multiscale information is complementary, enabling the MSLGCN to take full advantage of texture structures of varying sizes. In addition, a method to determine the superpixel scale by the intrinsic properties of HSIs is proposed to ensure that the segmentation boundary depicts the texture structure of the object accurately. Finally, the short-long graph convolution (SLGConv) is designed to fuse the advantages of global and local features, enabling the MSLGCN to extract accurate spatial-spectral features of nodes at any location. Experiments on three HSI datasets indicate that the MSLGCN can obtain better classification performance when compared with the other 11 state-of-the-art methods.
Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-scene classification, samples between source and target scenes are not drawn from the independent and ...identical distribution, resulting in significant performance degradation. To tackle this issue, a novel unsupervised domain adaptation (UDA) framework towards multi-level features and decision boundaries (ToMF-B) is proposed for the cross-scene HSIC, which can align task-related features and learn task-specific decision boundaries in parallel. Based on the maximum classifier discrepancy, a two-stage alignment scheme is proposed to bridge the interdomain gap and generate discriminative decision boundaries. In addition, to fully learn task-related and domain-confusing features, a CNN and Transformer-based multi-level features extractor (generator) is developed to enrich the feature representation of two domains. Furthermore, to alleviate the harm even the negative transfer to UDA caused by task-irrelevant features, a task-oriented feature decomposition method is leveraged to enhance the task-related features while suppressing task-irrelevant features, enabling the aligned domain-invariant features can be contributed to the classification task explicitly. Extensive experiments on three cross-scene HSI benchmarks have validated the effectiveness of the proposed framework.
At present, hyperspectral image classification (HSIC) technology based on deep learning has been widely explored. However, the time and labor cost of obtaining enough labeled samples are expensive. ...To obtain higher classification performance with a few number of labeled samples, a coarse-to-fine semi-supervised classification learning (CFSSL) method is proposed in this letter. First of all, the CFSSL performs coarse-grained classification with a few number of labeled samples, and the breaking-ties (BT) criterion is introduced to sample the coarse-grained classification results to ensure that the samples with high confidence are selected to generate pseudo-labels. Then, the pseudo-labels and their corresponding unlabeled samples are sent to the feature extraction network for fine-grained classification, so as to obtain more advanced classification results. Finally, in the fine-grained classification stage, a multi-scale convolution kernel attention aggregation network ( A 2 -MCKN) is designed to simultaneously extract the spatial-spectral features of the image and ensure clear texture boundaries of ground objects. Experimental results on two public datasets show that the CFSSL can obtain better accuracy than other methods with a few number of labeled samples.
Hyperspectral remote sensing images exhibit fine spectral curves, but they are also susceptible to spectral variations caused by factors such as cloud and haze. It is evident that these issues become ...more pronounced when there is a limited number of labeled samples available. Thus, a full range feature extraction network (FRFENet) based on quality-quantity-balance sample enhancement is proposed for hyperspectral image classification (HSIC). First, the full-range feature extraction method combines local-range, short-range, and long-range spatial-spectral features to address spectral variability and ensure accurate feature extraction, particularly in scenarios with limited labeled samples. Furthermore, the approach of balancing quality and quantity for pseudolabeled samples allows for an increased number of pseudolabels while maintaining their quality, effectively leveraging unlabeled samples. In addition, the utilization of superpixel region homogeneity directly contributes to an expanded training sample set, resulting in improved classification performance of the algorithm. Experiments on three hyperspectral image (HSI) encompass datasets indicate that the FRFENet can obtain better classification performance when compared with the other ten state-of-the-art methods.
Existing hyperspectral cross-domain few-shot learning (FSL) methods focus mainly on elaborating on training strategies or domain alignment algorithms, while paying less attention to the biased ...metaknowledge introduced by a large amount of source data and the implicit encouragement of learning target domain-specific attributes. In this article, from the perspective of disentangled representation learning, a novel cross-domain FSL method based on feature disentanglement (FDFSL) is proposed for hyperspectral image classification (HSIC). Specifically, to suppress the representation biased toward the source data and enable the model to implicitly focus on the inherent knowledge of the target domain, an orthogonal low-rank feature disentanglement method is employed to acquire desired features of source and target pipelines. Furthermore, to preserve more shared and discriminative information from the heterogeneous data space (i.e., the spectral dimensions of the source and target scenes are typically different), a multiorder spectral interaction block based on central position encoding (MICD) is proposed to fully integrate the respective features into the spectral domain, which allows the model to emphasize informative spectral dimensions in a data-driven manner. Finally, to diversify the feature representation space while preventing the model overfitting to domain alignment task, a self-distillation scheme is developed to facilitate the acquisition of task-relevant feature components. Extensive experiments and analysis on three public HSI datasets suggest the superiority of the proposed method. The code will be available on the website at https://github.com/Qba-heu/FDFSL .
Nowadays, domain adaptation (DA) is getting more attention in cross-scene hyperspectral image classification (HSIC), and various DA algorithms have been proposed. However, regular convolution ...indiscriminately extracting features around the center pixel will result in the inaccurate extraction of spatial-spectral features, which significantly affects the subsequent feature alignment (FA). Meanwhile, the method of aligning the category features of source and target domains (TDs) from a single level may not cope well with complex HSIs. Therefore, we propose a multilevel FA algorithm based on spatial attention deformable convolution (MFA-SADC), which achieves MFA from feature to feature (F-to-F), feature to cluster-center (F-to-C), and cluster-center to cluster-center (C-to-C). In addition, spatial attention deformable convolution (SAD-Conv) is proposed to compose the feature extraction network of MFA-SADC, which guarantees the purity of spatial-spectral features. Experiments on three HSI datasets indicate that MFA-SADC can obtain better classification performance when compared with the seven state-of-the-art methods.
At present, hyperspectral image classification (HSIC) technology based on deep learning has been widely explored. However, the time and labor costs of obtaining enough labeled samples are expensive. ...To obtain higher classification performance with a few number of labeled samples, a coarse-to-fine semisupervised classification learning (CFSSL) method is proposed in this letter. First of all, the CFSSL performs coarse-grained classification with a few number of labeled samples, and the breaking-ties (BTs) criterion is introduced to sample the coarse-grained classification results to ensure that the samples with high confidence are selected to generate pseudo-labels. Then, the pseudo-labels and their corresponding unlabeled samples are sent to the feature extraction network for fine-grained classification, to obtain more advanced classification results. Finally, in the fine-grained classification stage, a multiscale convolution kernel attention aggregation network (Formula Omitted-MCKN) is designed to simultaneously extract the spatial-spectral features of the image and ensure clear texture boundaries of ground objects. The experimental results on two public datasets show that the CFSSL can obtain better accuracy than other methods with a few number of labeled samples.
Nowadays, graph convolution networks are getting more and more attention in the field of hyperspectral image classification. The graph convolution can be divided into long-range and short-range graph ...convolution (GConv). However, the two graph convolutions cannot acquire global and local features at the same time, making the node features may not be accurate enough. Therefore, we propose a novel graph convolution approach, called short and long range graph convolution (SLGConv), which combines the advantages of long-range and short-range GConv. SLGConv can extract long-range (global) and short-range (local) spatial-spectral features, eliminating the disadvantages of each of long-range and short-range graph convolution. Furthermore, SLGConv can ensure that the features of nodes are not smoothed in the convolution process. Then, three layers of SLGConv are used to form the short and long range graph convolution network (SLGCN) for hyperspectral image classification. Experiments on three HSI datasets indicate that the SLGCN can obtain better classification performance when compared with seven state-of-the-art methods.
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