Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery

The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1 (GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for large-scale Earth observation. The advantages of the high temporal-spatial resolution and the wide field of view make the GF-1 WFV imagery very popular. However, cloud cover is an inevitable problem in GF-1 WFV imagery, which influences its precise application. Accurate cloud and cloud shadow detection in GF-1 WFV imagery is quite difficult due to the fact that there are only three visible bands and one near-infrared band. In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery. The MFC algorithm first implements threshold segmentation based on the spectral features and mask refinement based on guided filtering to generate a preliminary cloud mask. The geometric features are then used in combination with the texture features to improve the cloud detection results and produce the final cloud mask. Finally, the cloud shadow mask can be acquired by means of the cloud and shadow matching and follow-up correction process. The method was validated using 108 globally distributed scenes. The results indicate that MFC performs well under most conditions, and the average overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive analysis with the official provided cloud fractions, MFC shows a significant improvement in cloud fraction estimation, and achieves a high accuracy for the cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral bands. The proposed method could be used as a preprocessing step in the future to monitor land-cover change, and it could also be easily extended to other optical satellite imagery which has a similar spectral setting.Comment: This manuscript has been accepted for publication in Remote Sensing of Environment, vol. 191, pp.342-358, 2017. (http://www.sciencedirect.com/science/article/pii/S003442571730038X

Mechanism of Lactiplantibacillus plantarum regulating Ca2+ affecting the replication of PEDV in small intestinal epithelial cells

Porcine epidemic diarrhea virus (PEDV) mainly invades the small intestine and promotes an inflammatory response, eventually leading to severe diarrhea, vomiting, dehydration, and even death of piglets, which seriously threatens the economic development of pig farming. In recent years, researchers have found that probiotics can improve the intestinal microenvironment and reduce diarrhea. At the same time, certain probiotics have been shown to have antiviral effects; however, their mechanisms are different. Herein, we aimed to investigate the inhibitory effect of Lactiplantibacillus plantarum supernatant (LP-1S) on PEDV and its mechanism. We used IPEC-J2 cells as a model to assess the inhibitory effect of LP-1S on PEDV and to further investigate the relationship between LP-1S, Ca2+, and PEDV. The results showed that a divalent cation chelating agent (EGTA) and calcium channel inhibitors (Bepridil hydrochloride and BAPTA-acetoxymethylate) could inhibit PEDV proliferation while effectively reducing the intracellular Ca2+ concentration. Furthermore, LP-1S could reduce PEDV-induced loss of calcium channel proteins (TRPV6 and PMCA1b), alleviate intracellular Ca2+ accumulation caused by PEDV infection, and promote the balance of intra- and extracellular Ca2+ concentrations, thereby inhibiting PEDV proliferation. In summary, we found that LP-1S has potential therapeutic value against PEDV, which is realized by modulating Ca2+. This provides a potential new drug to treat PEDV infection

The UAVid Dataset for Video Semantic Segmentation

Video semantic segmentation has been one of the research focus in computer vision recently. It serves as a perception foundation for many fields such as robotics and autonomous driving. The fast development of semantic segmentation attributes enormously to the large scale datasets, especially for the deep learning related methods. Currently, there already exist several semantic segmentation datasets for complex urban scenes, such as the Cityscapes and CamVid datasets. They have been the standard datasets for comparison among semantic segmentation methods. In this paper, we introduce a new high resolution UAV video semantic segmentation dataset as complement, UAVid. Our UAV dataset consists of 30 video sequences capturing high resolution images. In total, 300 images have been densely labelled with 8 classes for urban scene understanding task. Our dataset brings out new challenges. We provide several deep learning baseline methods, among which the proposed novel Multi-Scale-Dilation net performs the best via multi-scale feature extraction. We have also explored the usability of sequence data by leveraging on CRF model in both spatial and temporal domain

Generating annual high resolution land cover products for 28 metropolises in China based on a deep super-resolution mapping network using Landsat imagery

High resolution of global land cover dynamic is indicative for understanding the influence of anthropogenic activity on environmental change. However, most of the land cover products are based on Landsat image that only has 30 m resolution, which is insufficient to distinguish the heterogenous urban structure; while very high spatial resolution image usually has low temporal resolution, which is difficult to monitor the urban dynamic. Deep-learning-driven super-resolution mapping is a prevailing way of achieving very-high-resolution land cover dynamic products in aspect of alleviating the mixed pixel problem of Landsat image. However, two limitations are obvious: 1) the fixed grid of kernel during the upsampling process favors spatial homogeneity and suppresses the learning of spatial heterogeneity of urban composition and 2) geometric or radiation variation over large spatial and long temporal extent in remote sensing images makes the super-resolution mapping approach difficult to transfer for application. Here, we attempt to solve these two limitations: 1) a progressive edge-guided super-resolution architecture is designed to allow nonuniformed kernel specific at the low-confidence edge region and intensify the learning of heterogenous compositions’ patterns and 2) an alternating optimization strategy is designed to minimize the resultant entropy and modulate the classification hyperplane to accommodate to the manifold of the discrepant region. Validation experiments are investigated based on a fine-grained and large-extent super-resolution (FLAS) dataset constructed in this study, and it is found that our approach remarkably enhances rich detailed patterns of heterogenous region and outperforms other state-of-the-art algorithms. Besides, we applied DETNet to the large spatial extent of 28 metropolises in China (>40,000 km2) and the large temporal extent of continuous 21-year (2000–2020) in Wuhan city to examine transferability. From the land cover areas variation, we find that the expansion rate of cropland is faster than the urban expansion over the past 10 years, which are gradually becoming the principal source for the encroachment of forest and lakes. From detailed urban dynamic reflected by the 21-year products, we find that urban-villages between the old city zone and the outer high-tech development zone are gradually disappeared. The captured dynamic is consistence with the urban-village renovation policy during this period, which is meant to redistribute the spatial configuration of the city for a more sustainable urban structure. We believe that the proposed method can facilitate a seamless and fine-grained observation system that can fill the weakness of the existing land cover activities and provide a brand-new insight into the urban dynamic and its underlying mechanism.</p