In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two principles for spectral-spatial feature extraction of HSI are built, including the foundation of pixel-level HSI classification and the definition of spatial information. Based on the two principles, scaled dot-product central attention (SDPCA) tailored for HSI is designed to extract spectral-spatial information from a central pixel (i.e., a query pixel to be classified) and pixels that are similar to the central pixel on an HSI patch. Then, employed with the HSI-tailored SDPCA module, a central attention network (CAN) is proposed by combining HSI-tailored dense connections of the features of the hidden layers and the spectral information of the query pixel. MiniCAN as a simplified version of CAN is also investigated. Superior classification performance of CAN and miniCAN on three datasets of different scenarios demonstrates their effectiveness and benefits compared with state-of-the-art methods.

Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which contribute to a more accurate identification of materials and land covers. However, most existing methods of hyperspectral image analysis primarily focus on spectral knowledge or coarse-grained spatial information while neglecting the fine-grained morphological structures. In the classification task of complex objects, spatial morphological differences can help to search for the boundary of fine-grained classes, e.g., forestry tree species. Focusing on subtle traits extraction, a spatial-logical aggregation network (SLA-NET) is proposed with morphological transformation for tree species classification. The morphological operators are effectively embedded with the trainable structuring elements, which contributes to distinctive morphological representations. We evaluate the classification performance of the proposed method on two tree species datasets, and the results demonstrate that the proposed SLA-NET significantly outperforms other state-of-the-art classifiers.

The process of annotating hyperspectral image (HSI) data is characterized by its time-consuming and labor-intensive nature. To address this challenge, researchers often employ a meta-learning paradigm known as few-shot learning (FSL), which leverages source domains containing a substantial number of labeled samples to assist in the classification of target domains with limited labeled samples. Many existing FSL methods rely on a conditional domain-adversarial strategy to mitigate the domain shift between source and target domains. However, these methods overlook the fact that the degrees of conditional distribution discrepancies between the two domains can vary significantly across different classes, leading to suboptimal conditional distribution alignment. To address this problem, we propose a framework called Adaptive Domain-Adversarial Few-Shot Learning (ADAFSL). Overall, the proposed ADAFSL employs an adaptive strategy that assigns varying weights to the conditional adversarial losses for different classes based on their respective degrees of discrepancies, thereby achieving global conditional distribution alignment. Specifically, a local alignment score map is constructed by measuring the similarity between labeled and unlabeled samples using both Euclidean and class-covariance metrics. This map is then multiplied with the conditional adversarial loss map, thus allocating more emphasis to the classes exhibiting greater discrepancies between the two domains. Moreover, to enhance cross-domain FSL, we design a multi-scale spectral-spatial feature extraction (MSFE) module, which incorporates cascaded multi-scale dilated convolutions. Experimental results on four public HSI datasets demonstrate that the proposed ADAFSL outperforms other state-of-the-art methods. The source code of this method can be found at https://github.com/JieW-ww/ADAFSL.

In hyperspectral imagery (HSI) classification, objects corresponding to pixels of different classes exhibit varying size characteristics, which causes a challenge for effective pixel-wise feature extraction and classification. In this paper, we propose a novel multi-scale model, called multi-area target attention (MATA). The proposed MATA utilizes an architecture that includes a shared feature extractor and classifier to capture multi-scale spectral-spatial information effectively and efficiently. The feature extractor employs multi-scale target attention module to extract spectral-spatial information from target pixels and their similar pixels across multi-scale areas, while L ₂ -normalization is employed to address discrepancies between features of different scales. The classifier adopts a class-wise decision weighting strategy to account for the varying sizes of different classes and the different contributions of semantic features at each scale to each class. Experimental results on five public HSI datasets demonstrate that the proposed MATA outperforms existing state-of-the-art single- and multi-scale models, confirming its effectiveness and efficiency in HSI classification. Code is available at https://github.com/huanliu233/MATA.

Hyperspectral images (HSIs), with hundreds of narrow spectral bands, are increasingly used for ground object classification in remote sensing. However, many HSI classification models operate pixel-by-pixel, limiting the utilization of spatial information and resulting in increased inference time for the whole image. This paper proposes SegHSI, an effective and efficient end-to-end HSI segmentation model, alongside a novel training strategy. SegHSI adopts a head-free structure with cluster attention modules and spatial-aware feedforward networks (SA-FFN) for multiscale spatial encoding. Cluster attention encodes pixels through constructed clusters within the HSI, while SA-FFN integrates depth-wise convolution to enhance spatial context. Our training strategy utilizes a student-teacher model framework that combines labeled pixel class information with consistency learning on unlabeled pixels. Experiments on three public HSI datasets demonstrate that SegHSI not only surpasses other state-of-the-art models in segmentation accuracy but also achieves inference time at the scale of seconds, even reaching sub-second speeds for full-image classification. Code is available at https://github.com/huanliu233/SegHSI.

Pine wilt disease (PWD) is one of the most prevalent pine trees diseases, resulting in both ecological and economic havoc. UAV remote sensing segmentation plays a crucial role in early identifying and preventing PWD. However, deep learning segmentation models customized for PWD identification in scenarios with complex backgrounds have not received extensive exploration. In this paper, we propose a novel UAV remote sensing segmentation model called Clusterformer with a conventional encoder-decoder structure. The encoder is comprised of the specially designed Cluster Transformer, which includes a cluster token mixer and a spatial-channel feed-forward network (SC-FFN). The cluster token mixer utilizes constructed clusters from the feature maps to represent pixels, thereby reducing redundant and interfering information. The SC-FFN extracts multi-scale spatial information through depth-wise convolutions and channel information through a multilayer perceptron in sequence. The decoder primarily consists of the specially designed D-Cluster Transformer. The token mixer of the D-Cluster Transformer employs constructed clusters from high-level decoded tokens to represent low-level encoded tokens without relying on traditional upsampling methods such as interpolation, transpose convolution, or patch expansion. Consequently, more robust and less redundant features from high-level decoded feature maps are transferred to low-level encoded feature maps. Experimental results on two PWD datasets demonstrate that Clusterformer outperforms existing state-of-the-art segmentation models. This confirms the effectiveness and efficiency of Clusterformer in PWD identification. Code is available at https://github.com/huanliu233/Clusterformer.