Different tree species have different carbon storage and growth rates. Therefore, accurate segmentation and identification of individual trees can provide more detailed carbon storage data, which is the basis for accurately estimating forest carbon storage. However, individual tree segmentation and recognition in dense forest areas face challenges such as crown overlap, complex terrain, and species diversity. To address these challenges and improve recognition accuracy, this paper proposes a fuzzy boundary-aware network (FBAN) for hyperspectral individual tree segmentation and recognition in dense forests. The proposed FBAN inclues a boundary-aware module (BAM) that explores channel boundaries between trees and non-trees, spatial boundaries of trees, and spectral boundaries between different trees by intergrating channel attention, spaital attention, and spectral attention. This enhances the separability of individual trees, especially those of the same species that are contiguous in dense forest areas. Additionally, an adaptive crown-aware module (ACAM) is constructed to adapt diverse-size crown features by coupling Transformer layers with dialted convolution layers. Experimental results on different hyperspectral datasets show that the proposed FBAN network outperforms existing methods in dense forest areas and different tree canopy areas, e.g., the AP on the SZU-South dataset is 4.5 points higher than that of Mask2former. It not only improves the accuracy of individual tree segmentation and recognition but also exhibits high generalization and robustness.

Hyperspectral image super-resolution aims to reconstruct high-quality spatial-spectral cubes from RGB images. However, the limited spectral coverage of RGB inputs hinders the simultaneous modeling of spatial structures and spectral relationships. To address this limitation, we propose a differential low-rank adaptive spatial-spectral transformer (DiLAST). Initially, a differential operator is employed to enhance RGB features in a bottom-up manner, explicitly amplifying subtle inter-channel differences. The enhanced features are then fed into a U-shaped backbone, which integrates three complementary modules for joint spatial-spectral modeling. Specifically, a center spatial-spectral attention (CSSA) module employs cross-attention mechanisms to capture local-to-global dependencies across both spatial and spectral domains; an adaptive cross-scale fusion (ACF) module utilizes learnable gating weights to establish dynamic interaction pathways between shallow high-frequency details and deep semantic representations; and a low-rank spectral calibration (LRSC) module exploits low-rank matrix priors to reveal low-dimensional manifold structures among spectral bands, thereby enhancing spectral consistency. By leveraging the synergistic effects of spatial non-locality, global spectral correlation, and low-rank properties, the proposed DiLAST achieves PSNR improvements of 33.82 dB, 36.03 dB, and 37.01 dB on benchmark datasets. Moreover, the accuracy and practical applicability of the reconstructed spectra have been effectively validated in remote sensing scenarios and object tracking tasks. The code is accessible at https://github.com/renqi1998/DiLAST.

With its strong capability in modeling long-range dependencies, the Transformer achieves competitive performance in hyperspectral image (HSI) and multispectral image (MSI) fusion. However, existing Transformer-based methods face the trade-off between receptive field size and computational efficiency when dealing with spatially non-local features. Furthermore, the Transformer captures deep spectral relationships by modeling pairwise channel interactions. This global interaction may overlook features that contribute little to the overall context but are critical locally, thus affecting the accurate understanding of HSI content. To overcome these challenges, we propose a novel local-global collaborative network with Transformers (LGCT) specifically designed to achieve high-quality HSI reconstruction. The proposed LGCT includes two inverse feature streams to establish multiscale deep representations of the HSI and MSI features. The feature streams comprise collaborative Transformer blocks (CTBs) explicitly designed for the spectral and spatial domains. By combining global and local processing mechanisms, the proposed CTBs can efficiently emphasize potential crucial features that Transformer ignores when capturing deep spectral and spatial relationships, thus enabling efficient modeling of the spectral and spatial domains from details to the whole. Furthermore, to enhance the reusability of multiscale enhanced features from the spectral and spatial domains, a hierarchical and symmetric strategy is adopted to progressively fuse them to generate high-quality images. The results on both simulated and real datasets demonstrate the superior performance of the proposed method in terms of quantitative metrics and visual quality. The code will be released athttps://github.com/Hewq77/LGCT.

Super-resolution is vital for the quality improvement of hyperspectral images (HSIs) under the spatial and spectral resolution trade-off. However, deep learning HSI super-resolution approaches typically adopt the “one model and one scale” scheme that is inefficient in training and storing. This is difficult in maximizing orbit equipment performance and aligning multiple spatial resolution data in remote sensing. Therefore, this article intends to address HSI arbitrary-scale super-resolution, enabling the scaling of HSIs to arbitrary sizes using a single model. To do this end, we treat HSI arbitrary-scale super-resolution as a retrieval problem. It conceptualizes the HSI as a dictionary of pixelwise tokens with spatial-spectral features, position information, and scale information. Its objective is to employ a set of initialized tokens related to the high-resolution (HR) HSI as queries to retrieve matched spectral features from low-resolution (LR) one, which is so-called token-based query-to-spectrum. Since these query tokens can be constructed flexibly (e.g., through random initialization), we can generate a desired number of them to reconstruct our HR HSI, thus achieving arbitrary-scale super-resolution. This process considers not only position information but also spectral features so that it can decrease spectral distortion. With the above idea, we developed an HSI arbitrary-scale super-resolution method, dubbed as spectral-query transformer (SQformer). Specifically, it begins by converting the LR HSI into a dictionary of LR tokens and then constructs a desired number of HR tokens. To enable flexible token construction, we design an implicit spectral token (particularly a learnable vector) and replicate it$\alpha H \times \alpha W$times to form the HR tokens. Next, the HR and LR tokens are passed into a transformer decoder to find the most matched spectral response for the former by soft-weighting the LR tokens. Finally, the HR tokens are spatially rearranged in order, forming an HR HSI. Extensive experiments have demonstrated its effectiveness on remote sensing data. The code will be released at:https://github.com/ShuGuoJ/SQformer.git.

Forests play an irreplaceable role in carbon sinks. However, there are obvious differences in the carbon sink capacity of different tree species, so the scientific and accurate identification of surface forest vegetation is the key to achieving the double carbon goal. Due to the disordered distribution of trees, varied crown geometry, and high difficulty in labeling tree species, traditional methods have a poor ability to represent complex spatial–spectral structures. Therefore, how to quickly and accurately obtain key and subtle features of tree species to finely identify tree species is an urgent problem to be solved in current research. To address these issues, a texture-aware self-attention model (TASAM) is proposed to improve spatial contrast and overcome spectral variance, achieving accurate classification of tree species hyperspectral images (HSIs). In our model, a nested spatial pyramid module is first constructed to accurately extract the multiview and multiscale features that highlight the distinction between tree species and surrounding backgrounds. In addition, a cross-spectral–spatial attention module is designed, which can capture spatial–spectral joint features over the entire image domain. The Gabor feature is introduced as an auxiliary function to guide self-attention to autonomously focus on latent space texture features, further extract more appropriate and accurate information, and enhance the distinction between the target and the background. Verification experiments on three tree species hyperspectral datasets prove that the proposed method can obtain finer and more accurate tree species classification under the condition of limited labeled samples. This method can effectively solve the problem of tree species classification in complex forest structures and can meet the application requirements of tree species diversity monitoring, forestry resource investigation, and forestry carbon sink analysis based on HSIs.

Hyperspectral imagery (HSI) contains hundreds of bands, which provide a wealth of spectral information and enable better characterization of features. However, the excessive dimensionality also poses a dimensional disaster for subsequent processing. Fortunately, band selection (BS) gives a straightforward and effective way to pick out a subset of bands with rich information and low correlation. Although many hyperspectral BS methods, especially clustering-based ones, have been proposed by researchers in recent years, the contextual information of adjacent bands and the spatial structural information of materials are not well investigated. Therefore, in this article, a multiscale superpixel-level group-clustering framework (MSGCF) has been proposed for hyperspectral BS. Different from previous, a new superpixel-level distance measure is elaborately utilized to group and cluster the spectral bands, which jointly considers the spectral context and spatial structure information. Concretely, to preserve the spatial structural information of HSI, multiple superpixel segmentation is first performed to generate superpixel maps in multiscales, which enables complementarity of multiple superpixel segmentation algorithms and adaptation to diverse scales of land cover types. Second, the grouping and clustering paradigm is introduced to conduct the contextual information among bands. Here the maximum points of superpixel-level KL-$\ell _{1}$distance of adjacent bands are adopted as partition points to separate bands into groups, which encourages adjacent bands with strong correlation to be divided into the same group. Third, a superpixel-level fast density-based clustering method (SuFDPC) with superpixel-level$\ell _{2, 1}$distance is developed to select representative bands in every group. Finally, BS results are achieved with a ranking-based voting strategy by concerning information entropy and frequency of occurrence in a unified scheme. A series of ablation analyses and experimental comparisons on four real HSI datasets have been conducted, as well as similarity comparisons for the selected bands. The experimental results consistently demonstrated the effectiveness of our MSGCF approach. The codes of this work will be available athttp://jiasen.tech/papers/for the sake of reproducibility.

3-D Gabor, as a typical filter, plays a critical role in extracting discriminative spectral–spatial features from hyperspectral images (HSIs). However, the performance of traditional 3-D Gabor is limited by the uniform response to each direction, which is inconsistent with the complexity of land cover distribution. It has been a continuing concern for researchers to investigate the anisotropic 3-D Gabor filters. In addition, the 3-D Gabor wavelets do not make full use of spatial distribution information, thus reducing the accuracy. This article proposes a superpixel-guided variable 3-D Gabor phase coding fusion (SuVGF) framework for HSI classification with limited training samples. First, the variable 3-D Gabor filters are created based on various asymmetric sinusoidal waves and spatial kernel sizes to achieve multidirectional features. Second, the local Gabor phase ternary pattern is adopted to encode the Gabor phases and improve the feature discrimination. Meanwhile, a scale map is produced by the majority voting of multiscale simple noniterative clustering (SNIC) and entropy rate superpixel (ERS) segmentation, which contains sufficient and complementary spatial distribution information. Then, geometric optimization is employed on the scale map to reduce noise disturbances. Finally, all Gabor features are modified by the filter with the guidance of a scale map and fused together as a confidence cube, and the random forest algorithm is exploited for classification. TheSuVGF is applied to three real hyperspectral datasets to demonstrate the superiority of higher accuracy, stronger robustness, and less computational complexity in comparison with several state-of-the-art ones.

With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. In this article, we concentrate on this topic and provide a systematic review of the relevant literature. Specifically, the contributions of this paper are twofold. First, the research progress of related methods is categorized according to the learning paradigm, including transfer learning, active learning and few-shot learning. Second, a number of experiments with various state-of-the-art approaches has been carried out, and the results are summarized to reveal the potential research directions. More importantly, it is notable that although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git.

The clustering methods have a good application in many aspects, in which the density peak (DP) clustering can effectively cluster similar neighboring pixels so that the features can be extracted well for hyperspectral images (HSIs) classification. In this work, a DP based covariance matrix (DPCM) method is proposed for the feature extraction of HSIs, which not only can effectively extract features but also can reduce the within-class variations and the between-class interference. The proposed method consists of the following steps: First, maximum noise fraction is employed on the original HSI to reduce the computational complexity and eliminate noise. Second, the local densities of the sample are calculated by the DP clustering. Therefore, a reconstructed image can be obtained in which each pixel has a density feature vector. Then, the covariance matrix between each density pixel in the density map is calculated. Last, the extracted covariance matrices are fed back to the support vector machine based on the logarithm Euclidean kernel for label assignment. Experiments are conducted on the Indian pine data set, in which each of the five randomly selected marker data are selected as the training sample. The experimental results show that the method can effectively improve the classification accuracy and is superior to other classification methods.

The clustering methods have a good application in many aspects, in which the density peak (DP) clustering can effectively cluster similar neighboring pixels, so that the features can be extracted well for hyperspectral images (HSIs) classification. In this work, a density peak based covariance matrix (DPCM) method is proposed for HSIs classification, which not only can effectively extracts features, but also can reduce the within-class variations and the between-class interference. The proposed method consists of the following steps: first, maximum noise fraction (MNF) is employed on the original HSI to reduce the computational complexity and eliminate noise. Second, the local densities of sample is calculated by the DP clustering. Therefore, the density map can be obtained in which each pixel has a density value in the original image. Then, the covariance matrix between each density pixel in the density map is calculated. Last, the extracted covariance matrices are fed back to the support vector machine (SVM) based on the logarithm Euclidean kernel for label assignment. Experiments on the Indian pine data set show that this method is superior to other classification methods.