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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.

ABSTRACT. In this paper, we first apply an integral identity on Ricci solitons to prove that closed locally conformally flat gradient Ricci solitons are of constant sectional curvature. We then generalize this integral identity to complete noncompact gradient shrinking Ricci solitons, under the conditions that the Ricci curvature is bounded from below and the Riemannian curvature tensor has at most exponential growth. As a consequence of this identity, we classify complete locally conformally flat gradient shrinking Ricci solitons with Ricci curvature bounded from below.

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.

Abstract:Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method-a measure for the spectral distortion of change information-is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

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Building change detection has always been an important research focus in production and urbanization. In recent years, deep learning methods have demonstrated a powerful ability in the field of detecting remote sensing changes. However, due to the heterogeneity of remote sensing and the characteristics of buildings, the current methods do not present an effective means to perceive building changes or the ability to fuse multi-temporal remote sensing features, which leads to fragmented and incomplete results. In this article, we propose a multi-branched network structure to fuse the semantic information of the building changes at different levels. In this model, two accessory branches were used to guide the buildings’ semantic information under different time sequences, and the main branches can merge the change information. In addition, we also designed a feature enhancement layer to further strengthen the integration of the main and accessory branch information. For ablation experiments, we designed experiments on the above optimization process. For MDEFNET, we designed experiments which compare with typical deep learning model and recent deep learning change detection methods. Experimentation with the WHU Building Change Detection Dataset showed that the method in this paper obtained accuracies of 0.8526, 0.9418, and 0.9204 in Intersection over Union (IoU), Recall, and F1 Score, respectively, which could assess building change areas with complete boundaries and accurate results.

Wetlands are one of the most important ecosystems on the Earth and play a critical role in regulating regional climate, preventing floods, and reducing flood severity. However, it is difficult to detect wetland changes in multitemporal Landsat 8 OLI satellite images due to the mixed composition of vegetation, soil, and water. The main objective of this study is to quantify change to wetland cover by an image-to-image comparison change detection method based on the image fusion of multitemporal images. Spectral distortion is regarded as candidate change information, which is generated by the spectral and spatial differences between multitemporal images during the process of image cross-fusion. Meanwhile, the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were extracted from the cross-fused image as a normalized index image to enhance and increase the information about vegetation and water. Then, the modified iteratively reweighted multivariate alteration detection (IR-MAD) is applied to the generally fused images and normalized difference index images, providing a good evaluation of spectral distortion. The experimental results show that the proposed method performed better to reduce the detection errors due to the complicated areas under different ground types, especially in cultivated areas and forests. Moreover, the proposed method was tested and quantitatively assessed and achieved an overall accuracy of 96.67% and 93.06% for the interannual and seasonal datasets, respectively. Our method can be a tool to monitor changes in wetlands and provide effective technical support for wetland conservation.