A high-resolution remote sensing image building extraction method based on deep learning

Traditional building extraction from very high resolution remote sensing optical imagery is limited by low precision and incomplete boundary. In this paper, a high-resolution remote sensing image building extraction method based on deep learning is proposed. Firstly, Principal Component Analysis is used to pre-train network structure in an unsupervised way and obtain the characteristics of remote sensing image. Secondly, an adaptive pooling model is proposed to reduce the feature information loss in the pooling process. The texture features are extracted by non-subsampled contour wave transformation and introduced to the network to improve the building extraction. Finally, the obtained image features are inputted into the softmax classifier for classification and building extraction results. A typical experiment areas selected. The comparison with typical building extraction method, the experimental results shows that the proposed method can extract the buildings with higher accuracy, especially the clearer and more complete boundary

Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning

Accurate information on urban surface water is important for assessing the role it plays in urban ecosystem services in the context of human survival and climate change. The precise extraction of urban water bodies from images is of great significance for urban planning and socioeconomic development. In this paper, a novel deep-learning architecture is proposed for the extraction of urban water bodies from high-resolution remote sensing (HRRS) imagery. First, an adaptive simple linear iterative clustering algorithm is applied for segmentation of the remote-sensing image into high-quality superpixels. Then, a new convolutional neural network (CNN) architecture is designed that can extract useful high-level features of water bodies from input data in a complex urban background and mark the superpixel as one of two classes: an including water or no-water pixel. Finally, a high-resolution image of water-extracted superpixels is generated. Experimental results show that the proposed method achieved higher accuracy for water extraction from the high-resolution remote-sensing images than traditional approaches, and the average overall accuracy is 99.14%

Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers

Cloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, e.g., Sentinel-2A 1.55⁻1.75 µm band) is widely applied to the detection of snow and clouds. However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. To solve this problem, a novel convolutional neural network (CNN) to classify cloud and snow on an object level is proposed in this paper. Specifically, a novel CNN structure capable of learning cloud and snow multiscale semantic features from high-resolution multispectral imagery is presented. In order to solve the shortcoming of “salt-and-pepper„ in pixel level predictions, we extend a simple linear iterative clustering algorithm for segmenting high-resolution multispectral images and generating superpixels. Results demonstrated that the new proposed method can with better precision separate the cloud and snow in the high-resolution image, and results are more accurate and robust compared to the other methods

Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks

In high-resolution image data, multilevel cloud detection is a key task for remote sensing data processing. Generally, it is difficult to obtain high accuracy for multilevel cloud detection when using satellite imagery which only contains visible and near-infrared spectral bands. So, multilevel cloud detection for high-resolution remote sensing imagery is challenging. In this paper, a new multilevel cloud detection technique is proposed based on the multiple convolutional neural networks for high-resolution remote sensing imagery. In order to avoid input the entire image into the network for cloud detection, the adaptive simple linear iterative clustering (A-SCLI) algorithm was applied to the segmentation of the satellite image to obtain good-quality superpixels. After that, a new multiple convolutional neural networks (MCNNs) architecture is designed to extract multiscale features from each superpixel, and the superpixels are marked as thin cloud, thick cloud, cloud shadow, and non-cloud. The results suggest that the proposed method can detect multilevel clouds and obtain a high accuracy for high-resolution remote sensing imagery

Anti-Inflammatory Pyranochalcone Derivative Attenuates LPS-Induced Acute Kidney Injury via Inhibiting TLR4/NF-κB Pathway

Treatment of septic acute kidney injury (AKI) has still been beyond satisfaction, although anti-inflammatory therapy is beneficial for sepsis-induced AKI. Compound 5b was derived from natural pyranochalcones and exhibited potent anti-inflammatory activity in adjuvant-induced arthritis. In this study, we aimed to investigate the renoprotective effects and potential mechanism of 5b against lipopolysaccharide (LPS)-induced AKI. C57BL/6 mice and human renal proximal tubule cell line (HK-2 cell) were treated with LPS, respectively. Compound 5b was orally administrated at a dose of 25 mg/kg/day for 5 days before LPS (10 mg/kg) intraperitoneal injection. Cells were pretreated with 25 μg/mL 5b for 30 min before LPS (1 μg/mL) treatment. Pretreatment with 5b markedly alleviated tubular injury and renal dysfunction in LPS-induced AKI. The expression of IL-1β, IL-6, and TNF-α both in renal tissue of AKI mice and in the LPS-stimulated HK-2 cell culture medium were reduced by 5b treatment (p < 0.05). The results of immunohistochemistry staining showed that 5b reduced the expression of NF-κB p65 in kidneys. Similarly, 5b decreased the LPS-induced levels of NF-κB p65 and TLR4 proteins in kidneys and HK-2 cells. These data demonstrated that a potent pyranochalcone derivative, 5b, exhibited renoprotective effect against LPS-induced AKI, which was associated with anti-inflammatory activity by inhibiting the TLR4/NF-κB pathway

Novel aspect of neprilysin in kidney fibrosis via ACSL4‐mediated ferroptosis of tubular epithelial cells

Abstract Although inhibition of neprilysin (NEP) might be a therapeutic strategy with the potential to improve the outcome of chronic kidney disease (CKD), the versatile function of NEP with its mechanism remains obscure in kidney fibrosis. In the study, we found that NEP was abnormally increased in tubular epithelial cells of CKD patients, as well as unilateral ureteral obstruction and adenine diet‐induced mice. Treatment with a United States Food and Drug Administration‐approved NEP inhibitor Sacubitrilat (LBQ657) could alleviate ferroptosis, tubular injury, and delay the progression of kidney fibrosis in experimental mice. Similarly, genetic knockdown of NEP also inhibited tubular injury and fibrosis in transforming growth factor (TGF)‐β1 ‐induced tubular cells. Mechanically, NEP overexpression aggravated the ferroptotic and fibrotic phenotype, which was restored by acyl‐CoA synthetase long‐chain family member 4 (ACSL4) knockdown. The NEP silencing attenuated TGF‐β1‐induced tubular cell ferroptosis and was exacerbated by ACSL4 overexpression. Collectively, for the first time, a novel aspect of NEP was explored in kidney fibrosis through ACSL4‐mediated tubular epithelial cell ferroptosis. Our data further confirmed that NEP inhibition exerted a promising therapeutic against fibrotic kidney diseases

2-methylquinazoline derivative F7 as a potent and selective HDAC6 inhibitor protected against rhabdomyolysis-induced acute kidney injury.

Histone deacetylases 6 (HDAC6) has been reported to be involved in the pathogenesis of rhabdomyolysis-induced acute kidney injury (AKI). Selective inhibition of HDAC6 activity might be a potential treatment for AKI. In our lab, N-hydroxy-6-(4-(methyl(2-methylquinazolin-4-yl)amino)phenoxy)nicotinamide (F7) has been synthesized and inhibited HDAC6 activity with the IC50 of 5.8 nM. However, whether F7 possessed favorable renoprotection against rhabdomyolysis-induced AKI and the involved mechanisms remained unclear. In the study, glycerol-injected mice developed severe AKI symptoms as indicated by acute renal dysfunction and pathological changes, accompanied by the overexpression of HDAC6 in tubular epithelial cells. Pretreatment with F7 at a dose of 40 mg/kg/d for 3 days significantly attenuated serum creatinine, serum urea, renal tubular damage and suppressed renal inflammatory responses. Mechanistically, F7 enhanced the acetylation of histone H3 and α-tubulin to reduce HDAC6 activity. Glycerol-induced AKI triggered multiple signal mediators of NF-κB pathway as well as the elevation of ERK1/2 protein and p38 phosphorylation. Glycerol also induced the high expression of proinflammatory cytokine IL-1β and IL-6 in kidney and human renal proximal tubule HK-2 cells. Treatment of F7 notably improved above-mentioned inflammatory responses in the injured kidney tissue and HK-2 cell. Overall, these data highlighted that 2-methylquinazoline derivative F7 inhibited renal HDAC6 activity and inflammatory responses to protect against rhabdomyolysis-induced AKI

Selective Histone Deacetylase 6 Inhibitor 23BB Alleviated Rhabdomyolysis-Induced Acute Kidney Injury by Regulating Endoplasmic Reticulum Stress and Apoptosis

Histone deacetylase 6 (HDAC6) contributed to the pathogenesis of rhabdomyolysis-induced acute kidney injury (AKI) and selective inhibition of HDAC6 activity may be a promising strategy for the treatment of AKI. Compound 23BB as a highly selective HDAC6 inhibitor was designed, synthesized by our lab and exhibited therapeutic potential in various cancer models with good safety. However, it remained unknown whether 23BB as a drug candidate could offer renal protective effect against rhabdomyolysis-induced AKI. In the present study, we investigated the effect of 23BB in a murine model of glycerol (GL) injection-induced rhabdomyolysis. Following GL injection, the mice developed severe AKI as indicated by acute renal dysfunction and histologic changes, accompanied by increased HDAC6 expression in the cytoplasm of tubular epithelial cells. Pharmacological inhibition of HDAC6 by 23BB pretreatment significantly reduced serum creatinine and serum blood urea nitrogen (BUN) levels as well as attenuated renal tubular damage in GL-injured kidneys. HDAC6 inhibition also resulted in reduced TdT-mediated dUTP nick-end labeling (TUNEL)-positive tubular cells, suppressed BAX, BAK, cleaved caspase-3 levels, and preserved Bcl-2 expression, indicating that 23BB exerted potent renoprotective effects by the regulation of tubular cell apoptosis. Moreover, GL-induced kidney injury triggered multiple signal mediators of endoplasmic reticulum (ER) stress including GRP78, CHOP, IRE1α, p-eIF2α, ATF4, XBP1, p-JNK, and caspase-12. Oral administration of 23BB improved above-mentioned responses in injured kidney tissues and suggested that 23BB modulated tubular cell apoptosis via the inactivation of ER stress. Overall, these data highlighted that renal protection of novel HDAC6 inhibitor 23BB is substantiated by the reduction of ER stress-mediated apoptosis in tubular epithelial cells of rhabdomyolysis-induced AKI

Table_2_Pharmacological Inhibition of Fatty Acid-Binding Protein 4 (FABP4) Protects Against Rhabdomyolysis-Induced Acute Kidney Injury.DOC

<p>Acute kidney injury (AKI) is a common and potentially life-threatening complication. Studies confirmed that circulating FABP4 depended on renal function of AKI patients. In our previous study, FABP4 was involved in the pathogenesis of I/R-induced AKI. However, the function of FABP4 in rhabdomyolysis-induced AKI remained poorly understood. In the study, we further investigated the effect of FABP4 in a murine model of glycerol injection-induced rhabdomyolysis. Following glycerol injection, the mice developed severe AKI as indicated by acute renal dysfunction and histologic changes, companied by the increased FABP4 expression in the cytoplasm of tubular epithelial cells. Pharmacological inhibition of FABP4 by a highly selective inhibitor BMS309403 significantly reduced serum creatinine level, proinflammatory cytokine mRNA expression of tumor necrosis factor-α, interleukin-6, and monocyte chemoattractant protein 1 as well as attenuated renal tubular damage in glycerol-injured kidneys. Oral administration of FABP4 inhibitor also resulted in a significant attenuation of ER stress indicated by transmission electron microscope analysis and its maker proteins expression of GRP78, CHOP, p-perk, and ATF4 in kidneys of AKI. Furthermore, BMS309403 could attenuate myoglobin-induced ER stress and inflammation in renal proximal tubular epithelial cell line (HK-2). Overall, these data highlighted that renal protection of selective FABP4 inhibitor was substantiated by the reduction of ER stress and inflammation in tubular epithelial cells of rhabdomyolysis-induced injured kidneys and suggested that the inhibition of FABP4 might be a promising therapeutic strategy for AKI treatment.</p