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Water body extraction from remote sensing images is an important task. Deep learning has become a more popular method for extracting water bodies from remote sensing images. However, these methods are usually aimed at a specific sensor and are not applicable. Thus, we proposed a new network, called the dense-local-feature-compression (DLFC) network aiming at extracting water body from different remote sensing images automatic. In this network, each layer of the network can receive the feature maps of all layers before it by the densely connected module of DenseNet. The concatenate operation on the feature dimension is used when connecting across layers. It can realize the different levels of features reuse. The local-feature-compression module is introduced before concatenate operation. It can obtain the more abstract features further by the convolution operation. Through the DLFC, we can fuse the spatial and spectral information for the remote sensing images that can extract water body from different remote sensing images. Besides, we construct a new water body dataset based on GaoFen-2 (GF-2) remote sensing images. The proposed DLFC achieved excellent performance with GF-2, GaoFen-6, Sentinel-2, and ZY-3 remote sensing images. Compared with the traditional water body extraction method and contemporary networks, the DLFC exhibits noticeable improvement. The results indicate that the DLFC can realize water body extraction from multisource remote sensing images automatically and rapidly.

Change detection (CD), a crucial technique for observing ground-level changes over time, is a challenging research area in the remote sensing field. Deep learning methods for CD have made significant progress in remote sensing intelligent interpretation. However, with very high-resolution (VHR) satellite imagery, technical challenges such as insufficient mining of shallow-level features, complex transmission of deep-level features, and difficulties in identifying change information features have led to severe fragmentation and low completeness issues of CD targets. To reduce costs and enhance efficiency in monitoring tasks such as changes in national resources, it is crucial to promote the practical implementation of automatic change detection technology. Therefore, we propose a deep learning approach utilizing heterogeneity enhancement and homogeneity restraint for CD. In addition to comprehensively extracting multilevel features from multitemporal images, we introduce a cosine similarity-based module and a module for progressive fusion enhancement of multilevel features to enhance deep feature extraction and the change information utilization within feature associations. This ensures that the change target completeness and the independence between change targets can be further improved. Comparative experiments with six CD models on two benchmark datasets demonstrate that the proposed approach outperforms conventional CD models in various metrics, including recall (0.6868, 0.6756), precision (0.7050, 0.7570), F1 score (0.6958, 0.7140), and MIoU (0.7013, 0.7000), on the SECOND and the HRSCD datasets, respectively. According to the core principles of change detection, the proposed deep learning network effectively enhances the completeness of target vectors and the separation of individual targets in change detection with VHR remote sensing images, which has significant research and practical value. [ABSTRACT FROM AUTHOR]

Obtaining accurate and timely crop mapping is essential for refined agricultural refinement and food security. Due to the spectral similarity between different crops, the influence of image resolution, the boundary blur and spatial inconsistency that often occur in remotely sensed crop mapping, remotely sensed crop mapping still faces great challenges. In this article, we propose to extend a neighborhood window centered on the target pixel to enhance the receptive field of our model and extract the spatial and spectral features of different neighborhood sizes through a multiscale network. In addition, we also designed a coordinate convolutional module and a convolutional block attention module to further enhance the spatial information and spectral features in the neighborhoods. Our experimental results show that this method allowed us to obtain accuracy scores of 0.9481, 0.9115, 0.9307 and 0.8729 for OA, kappa coefficient, F1 score and IOU, respectively, which were better than those obtained using other methods (Resnet-18, MLP and RFC). The comparison of the experimental results obtained from different neighborhood window sizes shows that the spatial inconsistency and boundary blurring in crop mapping could be effectively reduced by extending the neighborhood windows. It was also shown in the ablation experiments that the coordinate convolutional and convolutional block attention modules played active roles in the network. Therefore, the method proposed in this article could provide reliable technical support for remotely sensed crop mapping. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (LiDAR) is a popular technology in remote sensing that can significantly improve the efficiency of digital elevation model (DEM) construction. However, it is challenging to identify the real terrain features in complex areas using LiDAR data. To solve this problem, this work proposes a multi-information fusion method based on PointNet++ to improve the accuracy of DEM construction. The RGB data and normalized coordinate information of the point cloud was added to increase the number of channels on the input side of the PointNet++ neural network, which can improve the accuracy of the classification during feature extraction. Low and high density point clouds obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and the United States Geological Survey (USGS) were used to test this proposed method. The results suggest that the proposed method improves the Kappa coefficient by 8.81% compared to PointNet++. The type I error was reduced by 2.13%, the type II error was reduced by 8.29%, and the total error was reduced by 2.52% compared to the conventional algorithm. Therefore, it is possible to conclude that the proposed method can obtain DEMs with higher accuracy. [ABSTRACT FROM AUTHOR]