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.

Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this task by using both high-level and low-level feature maps. However, it is difficult to utilize different level features rationally with the present deep learning networks. To tackle this problem, a novel network based on DenseNets and the attention mechanism was proposed, called the dense-attention network (DAN). The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module. The proposed encoder–decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only red–green–blue (RGB) images demonstrated that the proposed DAN achieved a higher score (96.16% overall accuracy (OA), 92.56% F1 score, 90.56% mean intersection over union (MIOU), less training and response time and higher-quality value) when compared with other deep learning methods.

Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease.

Road extraction is an important task in remote sensing image information extraction. Recently, deep learning semantic segmentation has become an important method of road extraction. Due to the impact of the loss of multiscale spatial features, the results of road extraction still contain incomplete or fractured results. In this paper, we proposed a deep learning model, which is called the dense-global-residual network, that reduces the loss of spatial information and enhances context awareness. In the dense-global-residual network, the residual network is used to extract the features at different levels. To obtain more abundant multiscale features, a dense and global spatial pyramid pooling module based on Atrous Spatial Pyramid Pooling is built to perceive and aggregate the contextual information. The proposed method obtains better results on the GF-2 road dataset and public Massachusetts road dataset of aerial imagery. In order to prove the effectiveness of our method, we compared with four methods, such as DeepLabV3+, U-net, D-LinkNet and coord-dense-global model, the accuracy of our method is considerably better. Moreover, the dense-global-residual network can also effectively extract roads, especially trees and building shadows occlude the road. In addition, our method can successfully extract roads in regions of different development levels in universality experiments. This indicates that the proposed method can effectively maintain the completeness and continuity of roads and improve the accuracy of road segmentation from high-resolution remote sensing images.

Land use classification is a fundamental task of information extraction from remote sensing imagery. Semantic segmentation based on deep convolutional neural networks (DCNNs) has shown outstanding performance in this task. However, these methods are still affected by the loss of spatial features. In this study, we proposed a new network, called the dense-coordconv network (DCCN), to reduce the loss of spatial features and strengthen object boundaries. In this network, the coordconv module is introduced into the improved DenseNet architecture to improve spatial information by putting coordinate information into feature maps. The proposed DCCN achieved an obvious performance in terms of the public ISPRS (International Society for Photogrammetry and Remote Sensing) 2D semantic labeling benchmark dataset. Compared with the results of other deep convolutional neural networks (U-net, SegNet, Deeplab-V3), the results of the DCCN method improved a lot and the OA (overall accuracy) and mean F1 score reached 89.48% and 86.89%, respectively. This indicates that the DCCN method can effectively reduce the loss of spatial features and improve the accuracy of semantic segmentation in high resolution remote sensing imagery.

Multi-spectral information is crucial for remote sensing crop monitoring, but current methods struggle with inadequate feature extraction, leading to poor generalization and incomplete segmentation. The Segment Anything Model (SAM) shows significant potential for generalization across fields, offering a promising solution for crop monitoring. This paper introduces a crop segmentation method based on SAM using Multi-stage Adaptation Fine-tuning, namely MAF-SAM, effectively utilizing information from multispectral remote sensing and the transfer and generalization abilities of SAM. In its first stage, MAF-SAM employs a prefix adapter to extract primary low-level multispectral features and compresses them into three channels to meet the requirements of subsequent stages. The second stage introduces a low-rank adaptation fine-tuning strategy to inject crop-specific knowledge into the image encoder, enhancing MAF-SAM’s adaptability in particular crop segmentation tasks. In the third stage, it utilizes a mask decoder with no-prompt embedding to automatically generate masks with accurate class information. MAF-SAM achieves F1 scores and IoU for soybean and corn of 0.9294, 0.8680, 0.8760, and 0.7723, respectively, along with a Kappa coefficient of 0.9543. It demonstrates superior temporal and spatial transfer capabilities relative to five other advanced segmentation methods in our study area.