Hyperspectral images (HSIs) provide a large amount of spatial and spectral information to characterize ground objects. However, they also contain a lot of redundant information, which makes it difficult to extract complex local and global spatial-spectral features. Considering that HSIs present multi-scale similarity and anisotropic image features, multi-scale and multi-angle information can be used to effectively model local and global features and reduce the complexity of self-attention. This paper proposes a new multi-scale multi-angle attention network (MMAN) for HSI classification that models the internal relationship between image features at local and global scales. Firstly, three spectral-spatial feature extraction modules (at different scales) are constructed to extract the low-level features of the image. These modules are first used by a 3D convolutional layer for spectral feature extraction, and then input to a 2D convolutional layer for spatial feature extraction. Next, the serialized tokens are input to the multi-angle attention module. Finally, the learnable labels are identified through a linear layer, and the features of different scales are fused through a fully connected layer to realize the classification of samples. Experimental results on four standard datasets show that the proposed exhibits comparable or superior classification performance than other state-of-the-art methods.

Deep learning (DL) techniques have shown remarkable progress in remotely sensed hyperspectral image (HSI) classification tasks. The performance of DL-based models highly relies on the quality and quantity of labeled data. However, manual labeling is a laborious and expensive process that requires substantial efforts from human experts. Active learning (AL) techniques have been developed to alleviate the burden of manual annotation by selecting the most informative and uncertain samples for labeling. In this paper, we propose a new class-wise graph embedding-based AL (CGE-AL) framework implemented by a class-wise graph convolutional network (CGCN). First, we train a classifier with labeled data and infer latent features from labeled and unlabeled samples with the trained parameter. Then, we group the labeled data into multiple one-label sets by category. In a class-wise manner, we initialize the nodes of the graph with one-label and unlabeled features, which are then fed into CGCN. By updating the graph parameters with binary loss, CGCNs measure the uncertainty between labeled nodes and unlabeled nodes. To select the most valuable sample for labeling, we adopt the class minimum uncertainty to query the unlabeled nodes with higher overall uncertainty. We repeat this process with the updated labeled set to retrain our classification model and CGCNs. Extensive experiments demonstrate the outstanding performance of our method compared to other state-of-the-art AL-based approaches.

Hyperspectral images (HSIs) contain abundant information in the spatial and spectral domains, allowing for a precise characterization of categories of materials. Convolutional neural networks (CNNs) have achieved great success in HSI classification, owing to their excellent ability in local contextual modeling. However, CNNs suffer from fixed filter weights and deep convolutional layers, which lead to a limited receptive field and high computational burden. The recent Vision Transformer (ViT) models long-range dependencies with a self-attention mechanism and has been an alternative backbone to the CNNs traditionally used in HSI classification. However, such transformer-based architectures designate all input pixels of the receptive field as feature tokens in terms of feature embedding and self-attention, which inevitably limits the ability for learning multi-scale features and increases the computational cost. To overcome this issue, we propose a local semantic feature aggregation-based transformer (LSFAT) architecture which allows transformers to represent long-range dependencies of multi-scale features more efficiently. We introduce the concept of the homogeneous region into the transformer by considering a pixel aggregation strategy and further propose neighborhood aggregation-based embedding (NAE) and attention (NAA) modules, which are able to adaptively form multi-scale features and capture locally spatial semantics among them in a hierarchical transformer architecture. A reusable classification token is included together with the feature tokens in the attention calculation. In the last stage, a fully connected layer is employed to perform classification on the reusable token after transformer encoding. We verify the effectiveness of the NAE and NAA modules compared with the traditional ViT through extensive experiments. Our results demonstrate the excellent classification performance of the proposed method in comparison with other state-of-the-art approaches on several public HSIs.

This letter introduces a novel spatial-spectral classification method for hyperspectral images (HSIs) based on a structural-kernel collaborative representation (SKCR), which considers one weak assumption of spatial neighborhood that of the pixels in a superpixel belong to the same class when exploiting contextual information in HSI. The proposed method consists of the following steps. First, a superpixel segmentation strategy is used to construct self-adaptive regions for the HSI. Then, the structural information within each superpixel block is extracted based on the density peak and K nearest neighbors. Next, dual kernels are separately utilized for the exploitation of the spectral and the spatial information. Finally, the dual kernels are combined and incorporated into a support-vector-machine classifier. Since the weak assumption of spatial neighborhood is well considered in the collaborative representation, the proposed method showed excellent classification performance for two widely used real hyperspectral data sets even when the number of training samples was relatively small.

Spectral–spatial information plays an essential role in hyperspectral image (HSI) classification compared to pure spectral information. However, the neighbor spectral–spatial information of a pixel tends to be mixed into other ground coverings due to various external factors such as the weather and sensor jitter, and mainstream HSI classification methods present low sensitivity for spatial information in this situation. This article proposes a novel feature extraction method via 3-D block characteristics sharing (3-D-BCS) for HSI classification that redefines spatial–spectral information of a local region based on a superpixel perspective to overcome the spectral–spatial weak assumptions in feature extraction that consists of the following steps. First, 3-D blocks are obtained by performing an oversegmentation method on the raw HSI. Then, instead of global operation, a 3-D block-based Gabor filter is applied to the principal components of an HSI to extract the textural features. Next, an average operation is conducted on each shape adaptive region to address the spatial weak assumption and Gaussian weight is introduced into each superpixel block to overcome the spectral weak assumption. Thus, 3-D characteristics sharing blocks can be constructed by reshaping the above three kinds of spectral–spatial feature. Finally, the majority-based support vector machine (SVM) classifier is utilized to determine the final class labels of HSI at the decision fusion level. Experiments performed on several real hyperspectral data sets with limited training samples show that the proposed 3-D-BCS method outperforms the other types of the classification method.