Hyperspectral super-resolution is commonly accomplished by the fusing of a hyperspectral imaging of low spatial resolution with a multispectral image of high spatial resolution, and many tensor-based approaches to this task have been recently proposed. Yet, it is assumed in such tensor-based methods that the spatial-blurring operation that creates the observed hyperspectral image from the desired super-resolved image is separable into independent horizontal and vertical blurring. Recent work has argued that such separable spatial degradation is ill-equipped to model the operation of real sensors which may exhibit, for example, anisotropic blurring. To accommodate this fact, a generalized tensor formulation based on a Kronecker decomposition is proposed to handle any general spatial-degradation matrix, including those that are not separable as previously assumed. Analysis of the generalized formulation reveals conditions under which exact recovery of the desired super-resolved image is guaranteed, and a practical algorithm for such recovery, driven by a blockwise-group-sparsity regularization, is proposed. Extensive experimental results demonstrate that the proposed generalized tensor approach outperforms not only traditional matrix-based techniques but also state-of-the-art tensor-based methods; the gains with respect to the latter are especially significant in cases of anisotropic spatial blurring.

Accurate and comprehensive tree cover mapping plays a vital role in forest management and ecosystem assessment. However, the lack of spectral information and the low quality of available black and white (B&W) imagery makes it difficult to retroactively predict an historical tree cover map using common methods. Here, a network structure called B&WTreeNet, based on semantic segmentation, is proposed to make full use of the limited labeled training remote sensing data to obtain tree cover information from historical datasets. The proposed B&WTreeNet is capable of cross-temporal semantic segmentation, including various data augmentation methods, to address inconsistent tree cover features caused by variations in image quality. The luminance enhancer (LE) and other modules in B&WTreeNet can extract the characteristics of tree cover effectively, successfully compensating for the limited spectral information in B&W image datasets. The countrywide historical tree cover map in Switzerland generated for the 1980s using a limited training dataset from 2018 to 2019 agrees well with manual interpretation results.

Deep learning has garnered extensive attention in hyperspectral image (HSI) processing. However, its application in HSI semantic segmentation tasks has been relatively limited. Although segmentation methods can often interpret images up to two orders of magnitude faster than classification methods when interpreting images of the same scene, the segmentation task requires the training data to be fully labeled, i.e., each pixel has a corresponding label. Such data are scarce in HSI data. To address this problem, this article proposes a lightweight segmentation network based on a hierarchical attention segmentation network (LHAS), in which a generalized data augmentation (GDA) method is utilized to acquire relatively sufficient data for semantic segmentation. Specifically, the hierarchical attention module is designed to extract global and local information on HSI patches from different layers. A prototype auxiliary module (PAM) of cluster contrast has also been developed to enhance feature discrimination. Across two different datasets in various scenarios, the proposed LHAS demonstrates superior segmentation performance compared to existing methods, affirming its effectiveness. In addition, experiments conducted on embedded devices validate the efficacy of LHAS.

Hyperspectral imaging is endowed with outstanding discriminability between different land types by its comprehensive sensing of the spectrum, thus favored applying to anomaly detection. However, blurring effect, as a critical cause for quality deterioration of hyperspectral imaging, has been omitted by previous hyperspectral anomaly detection models. On one hand, given that anomalies are sparsely distributed in nature, such blurring effect entangling neighboring pixels severely weighs those detection models down. On the other hand, abnormal objects jeopardize the low-dimensional structure of the image, thus deblurring those images with anomalies is more challenging than normal ones. Hence, it is of much significance to investigate anomaly detection using blurred hyperspectral images. To this end, this paper proposes a generalized non-convex surrogated tensor framework that is able to perform anomaly detection robustly to blurring effects on hyperspectral images. The proposed framework is featured to be a unified paradigm which guarantees convergence for a broad class of non-convex surrogates. Through treating the spatial and spectral low-rankness adaptively via Block Term Decomposition, the unevenness in the multi-linear low-rankness of hyperspectral image is comprehensively considered, which together with the non-convex surrogates results in a tighter modeling of the low-dimensional prior of hyperspectral images. Extensive experiments demonstrate the superiority of the proposed method compared with the state-of-the-art methods on both hyperspectral image deblurring and anomaly detection.

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.

The semantic segmentation of wide-field remote sensing images plays a significant role in many fields. However, due to the complexity of the content of remote sensing images, the dataset often has an uneven distribution of land type between different classes and large gaps in the scales of different objects. This often creates great problems for fine segmentation. To solve the issues, an unbalanced class learning network with Scale-adaptive perception (UCSANet) is proposed, which can adaptively cope with Multi-scale objects and unbalanced classes. The design can be inserted in any convolution network easily and can enrich features without increasing too many parameters. The network groups feature and uses atrous convolutions with different dilated rates on different groups to extract Multi-scale features while separable convolutions reduce the amount of network parameters. Then, the fusion of features between different scales is achieved through the self-attention mechanism. Furthermore, a weight map is designed to adaptively combine the predictions of two segmentation heads with Cross-Entropy loss and Lovasz-Softmax loss respectively, which enable the network to focus on learning low-frequency classes without affecting high-frequency classes. Experimental results on GF-6 MSI datasets demonstrate that the proposed UCSANet performs significantly better than others and achieves multi-class segmentation more accurately.

Fusion with corresponding finer-resolution images has been a promising way to enhance hyperspectral images (HSIs) spatially. Recently, low-rank tensor-based methods have shown advantages compared with other kind of ones. However, these current methods either relent to blind manual selection of latent tensor rank, whereas the prior knowledge about tensor rank is surprisingly limited, or resort to regularization to make the role of low rankness without exploration on the underlying low-dimensional factors, both of which are leaving the computational burden of parameter tuning. To address that, a novel Bayesian sparse learning-based tensor ring (TR) fusion model is proposed, named as FuBay. Through specifying hierarchical sprasity-inducing prior distribution, the proposed method becomes the first fully Bayesian probabilistic tensor framework for hyperspectral fusion. With the relationship between component sparseness and the corresponding hyperprior parameter being well studied, a component pruning part is established to asymptotically approaching true latent rank. Furthermore, a variational inference (VI)-based algorithm is derived to learn the posterior of TR factors, circumventing nonconvex optimization that bothers the most tensor decomposition-based fusion methods. As a Bayesian learning methods, our model is characterized to be parameter tuning-free. Finally, extensive experiments demonstrate its superior performance when compared with state-of-the-art methods.

How to use multi-source data for semantic segmentation is a hot topic. In this article, a new multi-source image data semantic segmentation based on multi-source mutual supervision (MMS) has been proposed. First, multi-source data from the same region are trained separately using identical segmentation networks for initialization; then, MMS performs mutual supervision training on these initialized networks, thus adaptively cooperating differences in information distribution between multiple sources of image data, and combines real label constraints output consistency across different networks. Experimental results demonstrate that the proposed MMS strategy can effectively coordinate information between multi-source data, improve segmentation accuracy, and is effective in different semantic segmentation networks and different types of data sets.

The semantic segmentation of multi-temporal remote sensing images to construct wetland land surface coverage is the basis for the perception and dynamic modeling of geographic scenes. However, the segmentation of Spartina alterniflora (S.alterniflora) in remote sensing images on wetlands faces the problems such as low level for cooperative interpretation in multi-temporal images and high fragmentation in the distribution of S.alterniflora. To solve the issues, a multiple attention network (MARNet) based on transfer learning is proposed. The method is designed with a plug-and-play attention module to enhance the learning of vegetation features and improve the network’s ability to focus on small areas of S.alterniflora. At the same time, MARNet designs the transfer learning architecture from both inter-domain alignment and intra-domain adaptation perspectives,aligning the statistical distribution by using the maximum mean difference (MMD) between the source and target domains, and entropy minimization within the domain of the target domain to enhance the high confidence prediction of this domain. In addition, since the samples have a serious imbalance problem, redundant cutting and splicing steps are employed for the prediction results to prevent the poor edge prediction of some image blocks. Experimental results on three cross-year RSIs datasets demonstrate that the proposed MARNet performs significantly better than other networks and is able to extract S.alterniflora in wetlands more accurately.

Semantic segmentation of the remote sensing images (RSIs) has attracted increasing interest in recent years. However, large-area segmentation of the woodland presents challenges. The wide distribution and diverse tree species of the woodland make feature extraction difficult. For this reason, an infrared attention network (InfAttNet) is proposed to extract woodland from multispectral RSIs. InfAttNet has an extra infrared spectral encoder which makes use of the sensitivity of vegetation to near infrared and red edge spectrums. This extra encoder applies learning about vegetation to improve woodland segmentation. Several attention blocks are designed to enhance learning about vegetation features and so improve the performance. In addition, a new dataset is built, containing a large number of woodland RSIs and covering several typical woodland distribution regions in China. The experimental results demonstrate that, compared with other networks, InfAttNet has the highest accuracy and is capable of rapid extraction of the woodland in RSIs.

Gaofen-6 (GF-6) is a geostationary, earth-observation satellite, rely on it's multi-spectral images, GF-6 has the ability to support the monitoring of woodland resources. In this paper, the multi-spectral images sent by GF-6 are studied as dataset, and a model called Infrared Attention Network (InfAttNet) which based on semantic segmentation method is proposed to distinguish woodland from other land types to achieve the purpose of woodland extraction. To make full use of the spectral information, InfAttNet has an additional encoder to extract the features of infrared bands independently. Besides, infrared attention blocks help InfAttNet to enhance the characteristics of woodland. The experimental results proved that InfAttNet improves the accuracy of woodland extraction, and the segmentation effect is strengthened compared with classical networks.

For obtaining information about ecosystem resource, GF-1 satellite was launched on April 26, 2013, which is the first satellite of the China's High-Resolution Earth Observation System. After obtaining some of the remote sensing images from GF-1, we selected WFV(wide field vision) images and detected the woodland to separate it from other geography types in the images. First, WFV images were clipped and labeled, then two deep learning models, POI-Net and Deep-UNet were used for training. We fused the prediction matrixes of deep learning networks using proposed "Most-sure strategy". The results show that our method can effectively improve the accuracy of woodland detection and segmentation results are outstanding. In addition, the proposed framework can also detect woodland in images returned by GF-6 satellite.