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The intelligent segmentation of high-resolution remote sensing (HRS) image, also called as dense prediction task for HRS image, has been and will continue to be important research in the remote sensing community. In recent years, the growing wave of artificial intelligence (AI) technology has introduced innovative paradigms to this domain, yielding outstanding results and overcoming many challenges with conventional segmentation techniques. This paper provides a comprehensive review of these intelligent segmentation methodologies, including traditional pattern recognition, convolution neural network (CNN)-based, and Transformer-based techniques. However, the explosive but incomplete development of intelligent segmentation techniques also poses more challenges for earth observation experts, the most of which is the technical interpretability. Consequently, we consider these segmentation techniques in the aspect of explainable artificial intelligence (XAI). Data-centric XAI thinks the practical applications of the segmentation model while model-centric XAI will facilitate the understanding of decision-making processes and the adjustment of structural features. Moreover, this review identifies novel research questions and provides constructive insights and recommendations to HRS image segmentation tasks, which may shed new light on the intelligent segmentation methods within the remote sensing image understanding community. [ABSTRACT FROM AUTHOR]

Automatic building detection from remote sensing images holds significant applications in various domains, including cartography, land-use change detection, urban planning, and 3D intelligent city construction. Driven by the innovation of deep learning technology, convolutional neural networks (CNNs) have emerged as a powerful framework for automatic interpretation, exhibiting hierarchical feature generation and robust representation for building extraction. However, the inherent computational limitations of convolutional operators hinder the acquisition of an effective global receptive field. Recent advancements in deep learning have witnessed the adoption of the multi-head self-attention (MHSA) mechanism by Transformer, presenting the superiority of global context construction. Nonetheless, this network structure encounters difficulties in effectively modelling two-dimensional spatial structure features with scale variation in semantic segmentation and often has huge parameters with high algorithm complexity. To address the above problems, this paper proposed a hybrid deep neural network synergistically harnessing the superiority of CNN and Transformer to extract buildings from remote sensing images. Meanwhile, an improved MHSA mechanism is developed to facilitate the construction of multiscale global representations, concurrently reducing computational complexity. Furthermore, the cross-level MHSA modules the semantic correlation between the deep and shallow-level features to refine spatial fine-grained information. The proposed method was comprehensively evaluated on two building datasets via quantitative analysis and visualization. The experimental results confirmed that the developed network and modules outperform other state-of-the-art methods and can effectively improve the accuracy (93.4% F1-score on the WHU dataset, 89.38% F1-score on the CAMB dataset) with high efficiency for building extraction. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural network (CNN) and Transformer have been successfully applied in hyperspectral image (HSI) classification. Despite their success, some challenges persist. Popular HSI classification methods typically employ only a single patch as the network input, which may not provide robust information for various datasets or within different scenarios of a specific dataset. Large-scale patches are usually conducive to classifying large-scale scenes, while small-scale patches excel at classifying boundary locations. In addition, the majority of methods do not distinguish between the central pixel and its adjacent pixels, although the information from the central position is crucial. In this paper, a novel double-branch hierarchical partial convolution network (DBHP-Net) is proposed to address the aforementioned challenges. Concretely, DBHP-Net employs a new convolution, named TConv, to emphasize mining the spatial-channel features of the central position. Then, the TConvFormer Block is built by adopting the advanced engineering techniques of Transformer. Furthermore, we construct our model in a hierarchical style. The double-branch structure with large-scale and small-scale patches provides complementary information from the dataset. Experimental results based on randomly selected samples and spatially disjoint samples of two benchmark datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]

The purpose of pan-sharpening is to generate high-resolution multispectral (HRMS) images by combining multispectral images (MS) and panchromatic images (PAN), which have become an important part of remote sensing image processing. Therefore, how to better extract complete feature information from MS images and PAN images has become the focus of our attention. In this paper, we propose a new pansharpening method, called Transformer-based Dual-path cross fusion network (TDF) for Pan-sharpening remote sensing images, which aims to extract the spatial details of PAN images while maintaining the spectral fidelity of MS images. The whole network structure can be divided into two parts: in the encoder part, we adopt the Swin-Transformer module for the downsampling operation, which expands the sensory field of the network to the feature map, and then extracts the global information. However, since the Swin-Transformer module is not good at extracting pixel-level details, we introduce the Base Feature Extraction (BFE) and the Invertible Neural Network Block (INNB) modules for the interaction between the local and the global feature information. we also introduce the Edge-Enhancement Block (EEB) to further enhance the feature extraction at multi-scales during the image fusion process. In the decoder section, we once again employ the Swin Transformer module for downsampling. After the convolution operation and activation function, we utilize the Sub-Pixel Convolutional Neural Network for upsampling to generate the ultimate high-resolution multispectral images. Simulation experiments and real experiments are conducted on QuickBird (QB) and WorldView2 (WV2) datasets, which demonstrated our method are superior to the current methods. [ABSTRACT FROM AUTHOR]

Vision Transformers (ViTs) have become one of the main architectures in deep learning with the self-attention mechanism, and are becoming an alternative to Convolutional Neural Networks (CNNs) for remote sensing scene classification tasks. However, the earlier self-attention layer of ViTs focuses on local features rather than global features, and the deeper self-attention layer focuses on global features but ignores the different impact of different frequency information. This will greatly increase the training and computational cost due to the quadratic complexity of the self-attention mechanism on the long sequence representation. In this paper, we propose a hybrid CNN – vision transformer structure (HCVNet), which uses convolutional layers to replace the earlier self-attention layers, and a novel Frequency Multi-head Self Attention (F-MSA) mechanism to replace the deeper self-attention layers. Specifically, F-MSA is a dual-stream structure that reduces the computational cost and improves the classification performance by encoding the high/low frequency information separately. In addition, a Semantic-aware Localization (SaL) module is introduced, which can guide the selection of crop by learning prior knowledge, avoiding the issue of pure background sampling. Our method performed an accuracy value of 97.20 ± 0.02% on the Aerial Image Dataset and 93.89 ± 0.03% on the NWPU-RESISC45 Dataset, with low complexity costs. [ABSTRACT FROM AUTHOR]