Due to the urgent demand for remote sensing big data analysis, large-scale remote sensing image retrieval (LSRSIR) attracts increasing attention from researchers. Generally, LSRSIR can be divided ...into two categories as follows: uni-source LSRSIR (US-LSRSIR) and cross-source LSRSIR (CS-LSRSIR). More specifically, US-LSRSIR means the inquiry remote sensing image and images in the searching data set come from the same remote sensing data source, whereas CS-LSRSIR is designed to retrieve remote sensing images with a similar content to the inquiry remote sensing image that are from a different remote sensing data source. In the literature, US-LSRSIR has been widely exploited, but CS-LSRSIR is rarely discussed. In practical situations, remote sensing images from different kinds of remote sensing data sources are continually increasing, so there is a great motivation to exploit CS-LSRSIR. Therefore, this paper focuses on CS-LSRSIR. To cope with CS-LSRSIR, this paper proposes source-invariant deep hashing convolutional neural networks (SIDHCNNs), which can be optimized in an end-to-end manner using a series of well-designed optimization constraints. To quantitatively evaluate the proposed SIDHCNNs, we construct a dual-source remote sensing image data set that contains eight typical land-cover categories and 10 000 dual samples in each category. Extensive experiments show that the proposed SIDHCNNs can yield substantial improvements over several baselines involving the most recent techniques.
In this letter, different from traditional methods using original spectral features or handcraft spectral-spatial features, we propose to adaptively learn a suitable feature representation from ...unlabeled data. This is achieved by learning a feature mapping function based on stacked sparse autoencoder. Considering that hyperspectral imagery (HSI) is intrinsically defined in both the spectral and spatial domains, we further establish two variants of feature learning procedures for sparse spectral feature learning and multiscale spatial feature learning. Finally, we embed the learned spectral-spatial feature into a linear support vector machine for classification. Experiments on two hyperspectral images indicate the following: 1) the learned spectral-spatial feature representation is more discriminative for HSI classification compared to previously hand-engineered spectral-spatial features, especially when the training data are limited and 2) the learned features appear not to be specific to a particular image but general in that they are applicable to multiple related images (e.g., images acquired by the same sensor but varying with location or time).
Although the deep semantic segmentation network (DSSN) has been widely used in remote sensing (RS) image semantic segmentation, it still does not fully mind the spatial relationship cues between ...objects when extracting deep visual features through convolutional filters and pooling layers. In fact, the spatial distribution between objects from different classes has a strong correlation characteristic. For example, buildings tend to be close to roads. In view of the strong appearance extraction ability of DSSN and the powerful topological relationship modeling capability of the graph convolutional neural network (GCN), a DSSN-GCN framework, which combines the advantages of DSSN and GCN, is proposed in this paper for RS image semantic segmentation. To lift the appearance extraction ability, this paper proposes a new DSSN called the attention residual U-shaped network (AttResUNet), which leverages residual blocks to encode feature maps and the attention module to refine the features. As far as GCN, the graph is built, where graph nodes are denoted by the superpixels and the graph weight is calculated by considering the spectral information and spatial information of the nodes. The AttResUNet is trained to extract the high-level features to initialize the graph nodes. Then the GCN combines features and spatial relationships between nodes to conduct classification. It is worth noting that the usage of spatial relationship knowledge boosts the performance and robustness of the classification module. In addition, benefiting from modeling GCN on the superpixel level, the boundaries of objects are restored to a certain extent and there are less pixel-level noises in the final classification result. Extensive experiments on two publicly open datasets show that DSSN-GCN model outperforms the competitive baseline (i.e., the DSSN model) and the DSSN-GCN when adopting AttResUNet achieves the best performance, which demonstrates the advance of our method.
Driven by the urgent demand for flood monitoring, water resource management and environmental protection, water-body detection in remote sensing imagery has attracted increasing research attention. ...Deep semantic segmentation networks (DSSNs) have gradually become the mainstream technology used for remote sensing image water-body detection, but two vital problems remain. One problem is that the traditional structure of DSSNs does not consider multiscale and multishape characteristics of water bodies. Another problem is that a large amount of unlabeled data is not fully utilized during the training process, but the unlabeled data often contain meaningful supervision information. In this paper, we propose a novel multiscale residual network (MSResNet) that uses self-supervised learning (SSL) for water-body detection. More specifically, our well-designed MSResNet distinguishes water bodies with different scales and shapes and helps retain the detailed boundaries of water bodies. In addition, the optimization of MSResNet with our SSL strategy can improve the stability and universality of the method, and the presented SSL approach can be flexibly extended to practical applications. Extensive experiments on two publicly open datasets, including the 2020 Gaofen Challenge water-body segmentation dataset and the GID dataset, demonstrate that our MSResNet can obviously outperform state-of-the-art deep learning backbones and that our SSL strategy can further improve the water-body detection performance.
As a fundamental and important task in remote sensing, remote sensing image scene understanding (RSISU) has attracted tremendous research interest in recent years. RSISU includes the following ...sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection. Although these sub-tasks have different goals, they share some communal hints. Hence, this paper tries to discuss them as a whole. Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU. To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications of RSISU.
With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM2.5, the concentrations of air pollutants in China have ...gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM2.5 concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM2.5 concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM2.5 concentrations. We find that PM2.5 concentrations in Mid-Eastern China during these years is mainly characterized by “north-south superiority and central inferiority”, seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM2.5 concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM2.5 concentrations drop by more than 30 %. By 2021, PM2.5 concentrations rebound slightly, rising by 10 % in most provinces.
Abstract
Long noncoding RNAs (lncRNAs) have emerged as new regulatory molecules implicated in diverse biological processes, including therapeutic resistance. However, the mechanisms underlying ...lncRNA-mediated temozolomide (TMZ) resistance in glioblastoma (GBM) remain largely unknown. To illustrate the role of lncRNA in TMZ resistance, we induce TMZ-resistant GBM cells, perform a lncRNA microarray of the parental and TMZ-resistant cells, and find an unreported lncRNA in GBM, lnc-TALC (temozolomide-associated lncRNA in glioblastoma recurrence), correlated with TMZ resistance via competitively binding miR-20b-3p to facilitate c-Met expression. A phosphorylated AKT/FOXO3 axis regulated lnc-TALC expression in TMZ-resistant GBM cells. Furthermore, lnc-TALC increased MGMT expression by mediating the acetylation of H3K9, H3K27 and H3K36 in MGMT promoter regions through the c-Met/Stat3/p300 axis. In clinical patients, lnc-TALC is required for TMZ resistance and GBM recurrence. Our results reveal that lnc-TALC in GBM could serve as a therapeutic target to overcome TMZ resistance, enhancing the clinical benefits of TMZ chemotherapy.
With the development of high-resolution imagery, ship detection in optical satellite images has attracted a lot of research interest because of the broad applications in fishery management, vessel ...salvage, etc. Major challenges for this task include cloud, wave, and wake clutters, and even the variability of ship sizes. In this letter, we propose an unsupervised ship detection method toward overcoming these existing issues. Visual saliency, which focuses on highlighting salient signals from scenes, is applied to extract candidate regions followed by a homogeneous filter presented to confirm suspected ship targets with complete profiles. Then, a novel descriptor, ship histogram of oriented gradient, which characterizes the gradient symmetry of ship sides, is provided to discriminate real ships. Experimental results on numerous panchromatic satellite images demonstrate the good performance of our method compared to state-of-the-art methods.
•Soil properties were still changed even after three years biochar addition.•Biochar increased bacterial abundance and the α-diversity of bacterial community.•Biochar influenced the relative ...abundances of some functional bacteria.•Biochar indirectly impacts on bacterial community by altering soil properties.
Although biochar has been widely evaluated as a soil-amendment, the response of soil bacterial community to biochar addition, especially after several years’ addition, has not yet been fully understood. Here, we studied the effect of a single addition of biochar on bacterial community compositions in a black soil of northeast China. The biochar was added with dosages of 0%, 2%, 4% and 8% of the total mass of the top 20cm soils in the spring of 2012, and soil samples were collected seasonally four times in 2014. The abundance and composition of bacterial community were determined using quantitative real-time PCR and Illumina MiSeq sequencing methods, respectively. The results showed that soil pH, moisture, total C, total N, total P, NO3−-N, available K and the C/N ratio significantly increased with biochar addition, but that soil bulk density and total K content decreased. The bacterial abundance increased with biochar addition, especially at a higher dosage. The biochar addition increased the alpha-diversity of soil bacterial community and changed the bacterial community compositions. Taxonomic analyses showed that Proteobacteria, Acidobacteria, Actinobacteria, Chloroflexi and Bacteroidetes were the dominant phyla in this study, and the relative abundances of Acidobacteria decreased but Chloroflexi increased with biochar addition. Additionally, biochar addition increased the relative abundances of Bacillus and Pedomicrobium, but decreased the relative abundance of Bradyrhizobium. Canonical correspondence analysis indicated that bacterial community compositions were closely associated with soil parameters such as pH, total C, total N and total K. Given the changes of these soil parameters were highly correlated with the amounts of biochar addition, which suggested that the impacts of long-term biochar amendment on the soil bacterial community were linked to the alteration of soil physiochemical properties.
As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest. Human beings can ...easily perform MLRSSC by examining the visual elements contained in the scene and the spatio-topological relationships of these visual elements. However, most of existing methods are limited by only perceiving visual elements but disregarding the spatio-topological relationships of visual elements. With this consideration, this paper proposes a novel deep learning-based MLRSSC framework by combining convolutional neural network (CNN) and graph neural network (GNN), which is termed the MLRSSC-CNN-GNN. Specifically, the CNN is employed to learn the perception ability of visual elements in the scene and generate the high-level appearance features. Based on the trained CNN, one scene graph for each scene is further constructed, where nodes of the graph are represented by superpixel regions of the scene. To fully mine the spatio-topological relationships of the scene graph, the multi-layer-integration graph attention network (GAT) model is proposed to address MLRSSC, where the GAT is one of the latest developments in GNN. Extensive experiments on two public MLRSSC datasets show that the proposed MLRSSC-CNN-GNN can obtain superior performance compared with the state-of-the-art methods.
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