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The identification and early warning of potential landslides can effectively reduce the number of casualties and the amount of property loss. At present, interferometric synthetic aperture radar (InSAR) is considered one of the mainstream methods for the large-scale identification and detection of potential landslides, and it can obtain long-term time-series surface deformation data. However, the method of identifying anomalous deformation areas using InSAR data is still mainly manual delineation, which is time-consuming, labor-consuming, and has no generally accepted criterion. In this study, a two-stage detection deep learning network (InSARNet) is proposed and used to detect anomalous deformation areas in Maoxian County, Sichuan Province. Compared with the most commonly used detection models, it is demonstrated that the InSARNet has a better performance in the detection of anomalous deformation in mountainous areas, and all of the quantitative evaluation indexes are higher for InSARNet than for the other models. After the anomalous deformation areas are identified using the proposed model, the possible relationship between the anomalous deformation areas and potential landslides is investigated. Finally, the fact that the automatic and rapid identification of potential landslides is the inevitable trend of future development is discussed. [ABSTRACT FROM AUTHOR]

Both short-distance association knowledge and long-distance interaction knowledge in the knowledge base contain rich semantics. When learning entity and relation representation in a knowledge base, if we can learn short-distance association knowledge and long-distance interaction knowledge at the same time, we can learn the representation method with rich semantic information and keep the original structure of the knowledge base. Among the knowledge contained in a large number of records, some knowledge reflects individual characteristics and can be called local knowledge; others reflect group characteristics and can be called global knowledge. Using different ways to learn local and global knowledge in the deep learning model will better reflect the difference between the two kinds of knowledge at the model level, and make the model have the ability to understand both individual characteristics and overall characteristics. Through layer-by-layer forward propagation and error back propagation algorithms, the entire network is gradually optimized in an "end-to-end" manner. This "end-to-end" approach leaves some means of introducing prior knowledge flexibly into the model. Although it can reduce the burden on researchers, this "data-driven" approach brings the shortcomings of poor interpretability of learning results and weak generalization ability. Combining the specific prior knowledge implicit in the data with the deep learning algorithm can optimize the algorithm in a targeted manner and avoid blind searching in the solution space, so as to obtain a model with better performance and wider use. To this end, this paper investigates combining prior knowledge with deep learning to design efficient algorithms to address the classification of English translation styles. This paper combines local knowledge with global knowledge and deep learning methods and proposes a memory neural network method combining local knowledge and global knowledge. By recording the local knowledge in the local memory module and simultaneously recording the global knowledge in the global memory module, the method effectively learns the latent information in a large number of records. This paper combines short-distance association knowledge with long-distance interaction knowledge and a distributed representation learning method based on deep learning and proposes a deep learning method combining short-distance association knowledge and long-distance interaction knowledge. On the IWSLT English translation task, experiments show that the method significantly improves translation quality, confirming that grammatical dependencies enhance attention by supplementing dependent grammatical information, resulting in more effective and richer context vectors that more accurately represent contextual situations. Additional experimental analysis showed that the model underwent careful parameter selection and analysis. By mining valuable long-distance interactive knowledge in the knowledge base and using it in the distributed representation learning of the knowledge base, while constraining the short-distance related knowledge and constraining the long-distance interactive knowledge, the learned knowledge can be used to effectively complete the knowledge base distributed representation for discovering new relations. [ABSTRACT FROM AUTHOR]

Agricultural drought seriously threatens the food and ecological security of most of the world's developing countries. Data-driven integrated agricultural drought index with remote sensing provides an effective tool to monitor, evaluate, and predict the agricultural drought. However, there is still a lack of comprehensive analytical work on taking the most effective machine learning (ML) and deep learning (DL) methods to construct such integrated drought index. In other words, it is still unclear whether the recent DL methods can improve integrated drought monitoring as compared with the currently widely used ML methods. Therefore, we critically evaluated the performances of four representative DL methods (represents the four currently popular DL network types) i.e., Entity Embedding Deep Neural Network (EEDNN), One-dimensional Convolutional Neural Network (1D-CNN), Gated Recurrent Unit (GRU), and Self-Attention Mechanism (SAM) and three widely used tree-based ML methods i.e., Cubist, Random Forest (RF), and Light Gradient Boosting Machine (LGBM), through constructing a QuickDRI like integrated drought index (abbreviated as QuickDRI-China). About 30 years of meteorological data, 14 years of remote sensing data, and various biophysical variables in China such as land use/land cover, available water capacity, irrigated agriculture, elevation, and ecoregion were employed in this study. Results showed that the EEDNN performed best, followed by the RF and LGBM, and then the other methods including the currently wide used Cubist, according to the station accuracy evaluations, spatial description evaluations, and responses to specific drought event. The tree-based ML methods such as RF and LGBM are still competitive in constructing the integrated agricultural drought index at the current stage. However, the higher accuracy, the smoother spatial description, and the more responsive ability of the EEDNN demonstrate great potential of DL methods. The future integrated agricultural drought monitoring with remote sensing should develop a specialized DL network for heterogeneous agricultural drought features. • Various deep learning approaches were examined for integrated drought monitoring. • Tree-based ensemble learning methods were effective. • Deep learning has unique advantages for assessing spatial description of agricultural drought. • Deep learning has great potential to improve integrated drought monitoring. [ABSTRACT FROM AUTHOR]

Early blight and late blight are important factors restricting tomato yield. However, it is still a challenge to accurately and objectively detect and segment crop diseases in order to evaluate disease damage. In this paper, the Disease Segmentation Detection Transformer (DS-DETR) is proposed to segment leaf disease spots efficiently based on several improvements to DETR. Additionally, a damage assessment is carried out by the area ratio of the segmented leaves to the disease spots. First, an unsupervised pre-training method was introduced into DETR with the Plant Disease Classification Dataset (PDCD) to solve the problem of the long training epochs and slow convergence speed of DETR. This method can train the Transformer structures in advance to obtain leaf disease features. Loading the pre-training model weight in DS-DETR can speed up the convergence speed of the model. Then, Spatially Modulated Co-Attention (SMCA) was used to assign Gaussian-like spatial weights to the query box of DS-DETR. The different positions in the image are trained using the query boxes with different weights to improve the accuracy of the model. Finally, an improved relative position code was added to the Transformer structure of DS-DETR. Relative position coding promotes the capture of the sequence order of input tokens by the Transformer. The spatial location feature is strengthened by establishing the location relationship between different instances. Based on these improvements, the DS-DETR model was tested on the Tomato leaf Disease Segmentation Dataset (TDSD) constructed by us. The experimental results show that the DS-DETR proposed by us achieved 0.6823 for APmask, which improved by 12.87%, 8.25%, 3.67%, 1.95%, 10.27%, and 9.52% compared with the state-of-the-art: Mask RCNN, BlendMask, CondInst, SOLOv2, ISTR, and DETR, respectively. In addition, the disease grading accuracy reached 0.9640 according to the segmentation results given by our proposed model. [ABSTRACT FROM AUTHOR]

Pansharpening is an important yet challenging remote sensing image processing task, which aims to reconstruct a high-resolution (HR) multispectral (MS) image by fusing a HR panchromatic (PAN) image and a low-resolution (LR) MS image. Though deep learning (DL)-based pansharpening methods have achieved encouraging performance, they are infeasible to fully utilize the deep semantic features and shallow contextual features in the process of feature fusion for a HR-PAN image and LR-MS image. In this paper, we propose an efficient full-depth feature fusion network (FDFNet) for remote sensing pansharpening. Specifically, we design three distinctive branches called PAN-branch, MS-branch, and fusion-branch, respectively. The features extracted from the PAN and MS branches will be progressively injected into the fusion branch at every different depth to make the information fusion more broad and comprehensive. With this structure, the low-level contextual features and high-level semantic features can be characterized and integrated adequately. Extensive experiments on reduced- and full-resolution datasets acquired from WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate that the proposed FDFNet only with less than 100,000 parameters performs better than other detail injection-based proposals and several state-of-the-art approaches, both visually and quantitatively. [ABSTRACT FROM AUTHOR]