Articoli di riviste: "Water body extraction"

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Luo, Yuanjiang, Ao Feng, Hongxiang Li, Danyang Li, Xuan Wu, Jie Liao, Chengwu Zhang, Xingqiang Zheng e Haibo Pu. "New deep learning method for efficient extraction of small water from remote sensing images". PLOS ONE 17, n. 8 (5 agosto 2022): e0272317. http://dx.doi.org/10.1371/journal.pone.0272317.

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Extracting water bodies from remote sensing images is important in many fields, such as in water resources information acquisition and analysis. Conventional methods of water body extraction enhance the differences between water bodies and other interfering water bodies to improve the accuracy of water body boundary extraction. Multiple methods must be used alternately to extract water body boundaries more accurately. Water body extraction methods combined with neural networks struggle to improve the extraction accuracy of fine water bodies while ensuring an overall extraction effect. In this study, false color processing and a generative adversarial network (GAN) were added to reconstruct remote sensing images and enhance the features of tiny water bodies. In addition, a multi-scale input strategy was designed to reduce the training cost. We input the processed data into a new water body extraction method based on strip pooling for remote sensing images, which is an improvement of DeepLabv3+. Strip pooling was introduced in the DeepLabv3+ network to better extract water bodies with a discrete distribution at long distances using different strip kernels. The experiments and tests show that the proposed method can improve the accuracy of water body extraction and is effective in fine water body extraction. Compared with seven other traditional remote sensing water body extraction methods and deep learning semantic segmentation methods, the prediction accuracy of the proposed method reaches 94.72%. In summary, the proposed method performs water body extraction better than existing methods.

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Ye, Chul-Soo. "Water body extraction in SAR image using water body texture index". Korean Journal of Remote Sensing 31, n. 4 (31 agosto 2015): 337–46. http://dx.doi.org/10.7780/kjrs.2015.31.4.6.

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Jiang, Wei, Yuan Ni, Zhiguo Pang, Xiaotao Li, Hongrun Ju, Guojin He, Juan Lv, Kun Yang, June Fu e Xiangdong Qin. "An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery". Water 13, n. 12 (11 giugno 2021): 1647. http://dx.doi.org/10.3390/w13121647.

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Surface water bodies, such as rivers, lakes, and reservoirs, play an irreplaceable role in global ecosystems and climate systems. Sentinel-2 imagery provides new high-resolution satellite remote sensing data. Based on the analysis of the spectral characteristics of the Sentinel-2 satellite, a novel water index called the Sentinel-2 water index (SWI) that is based on the vegetation-sensitive red-edge band (Band 5) and shortwave infrared (Band 11) bands was developed. Four representative water body types, namely, Taihu Lake, Yangtze River, Chaka Salt Lake, and Chain Lake, were selected as study areas to conduct a water body extraction performance comparison with the normalized difference water index (NDWI). We found that (1) the contrast value of the SWI was larger than that of the NDWI in terms of various water body types, including purer water, turbid water, salt water, and floating ice, which suggested that the SWI could achieve better enhancement performance for water bodies. An (2) effective water body extraction method was proposed by integrating the SWI and Otsu algorithm, which could accurately extract various water body types with high overall accuracy. The (3) method effectively extracted large water bodies and wide river channels by suppressing shadow noise in urban areas. Our results suggested that the novel method can achieve efficient water body extraction for rapidly and accurately extracting various water bodies from Sentinel-2 data and the novel method has application potential for larger-scale surface water mapping.

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Naik, B. Chandrababu, e B. Anuradha. "Extraction of Water-body Area from High-resolution Landsat Imagery". International Journal of Electrical and Computer Engineering (IJECE) 8, n. 6 (1 dicembre 2018): 4111. http://dx.doi.org/10.11591/ijece.v8i6.pp4111-4119.

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Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

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Zhang, Yonghong, Huanyu Lu, Guangyi Ma, Huajun Zhao, Donglin Xie, Sutong Geng, Wei Tian e Kenny Thiam Choy Lim Kam Sian. "MU-Net: Embedding MixFormer into Unet to Extract Water Bodies from Remote Sensing Images". Remote Sensing 15, n. 14 (15 luglio 2023): 3559. http://dx.doi.org/10.3390/rs15143559.

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Water bodies extraction is important in water resource utilization and flood prevention and mitigation. Remote sensing images contain rich information, but due to the complex spatial background features and noise interference, problems such as inaccurate tributary extraction and inaccurate segmentation occur when extracting water bodies. Recently, using a convolutional neural network (CNN) to extract water bodies is gradually becoming popular. However, the local property of CNN limits the extraction of global information, while Transformer, using a self-attention mechanism, has great potential in modeling global information. This paper proposes the MU-Net, a hybrid MixFormer architecture, as a novel method for automatically extracting water bodies. First, the MixFormer block is embedded into Unet. The combination of CNN and MixFormer is used to model the local spatial detail information and global contextual information of the image to improve the ability of the network to capture semantic features of the water body. Then, the features generated by the encoder are refined by the attention mechanism module to suppress the interference of image background noise and non-water body features, which further improves the accuracy of water body extraction. The experiments show that our method has higher segmentation accuracy and robust performance compared with the mainstream CNN- and Transformer-based semantic segmentation networks. The proposed MU-Net achieves 90.25% and 76.52% IoU on the GID and LoveDA datasets, respectively. The experimental results also validate the potential of MixFormer in water extraction studies.

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Ye, Chul-Soo. "Water body extraction using block-based image partitioning and extension of water body boundaries". Korean Journal of Remote Sensing 32, n. 5 (31 ottobre 2016): 471–82. http://dx.doi.org/10.7780/kjrs.2016.32.5.6.

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Weng, Yijie, Zongmei Li, Guofeng Tang e Yang Wang. "OCNet-Based Water Body Extraction from Remote Sensing Images". Water 15, n. 20 (12 ottobre 2023): 3557. http://dx.doi.org/10.3390/w15203557.

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Water body extraction techniques from remotely sensed images are crucial in water resources distribution studies, climate change studies and other work. The traditional remote sensing water body extraction has the problems of low accuracy and being time-consuming and laborious, and the water body recognition technique based on deep learning is more efficient and accurate than the traditional threshold method; however, there is the problem that the basic model of semantic segmentation is not well-adapted to complex remote sensing images. Based on this, this study adopts an OCNet feature extraction network to modify the base model of semantic segmentation, and the resulting model achieves excellent performance on water body remote sensing images. Compared with the traditional water body extraction method and the base network, the OCNet modified model has obvious improvement, and is applicable to the extraction of water bodies in true-color remote sensing images such as high-score images and unmanned aerial vehicle remote sensing images. The results show that the model in this study can realize automatic and fast extraction of water bodies from remote sensing images, and the predicted water body image accuracy (ACC) can reach 85%. This study can realize fast and accurate extraction of water bodies, which is of great significance for water resources acquisition and flood disaster prediction.

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Zhang, Q., X. Hu e Y. Xiao. "A NOVEL HYBRID MODEL BASED ON CNN AND MULTI-SCALE TRANSFORMER FOR EXTRACTING WATER BODIES FROM HIGH RESOLUTION REMOTE SENSING IMAGES". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (5 dicembre 2023): 889–94. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-889-2023.

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Abstract. Extracting water bodies from high-resolution remote sensing images has always been a challenging and hot task in the field of remote sensing. Considering that the accuracy and reliability of water body extraction still have some room for improvement, this paper proposes a hybrid network model based on CNN and multi-scale transformer for water body extraction from high-resolution remote sensing images. Specifically, the proposed network first uses a CNN model to extract a series of multi-scale features from shallow to deep from remote sensing images. These multi-scale features are then fed into a designed multi-scale transformer module to extract global contextual association information of water bodies. Afterwards, the water separability in the new multi-scale features output from the multi-scale transformer module is evaluated separately, and the features at different scales are adaptively weighted and fused according to their water separability. Subsequently, the network adaptively refines the fused features with the aid of a hybrid attention model to generate refined features that can effectively distinguish between water bodies and non-water bodies. Finally, these refined features are input into the prediction head to generate the final water body extraction results. The proposed network integrates the ability of CNN to capture local detail features and the ability of transformer to model global contextual semantic associations in a large range. Therefore, it can more accurately identify water bodies in remote sensing images, and the extracted water body boundaries have high accuracy and continuity. Finally, water body extraction experiments on the public dataset demonstrate the effectiveness of the proposed network. Moreover, the results of comparative experiments also show that compared with existing networks or methods such as U-Net, FCN8s, DeepLabv3+, and MSFA-Net, the proposed network has certain advantages in terms of water body extraction accuracy.

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He, S. A., e Xiao Yan Zhu. "Preparation of Zirconia Fiber Body with Extrusion-Extraction Molding". Key Engineering Materials 519 (luglio 2012): 291–96. http://dx.doi.org/10.4028/www.scientific.net/kem.519.291.

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Zirconia fiber body was prepared with Extrude-extracting, using zirconium slurry made of partial stabilized Zirconia ultra-fine powder. The result shows that，acetone is the first choice as extraction agent because of its notable effect of water extraction on zirconium slurry. The Zirconia fiber body, which length is over 2 centimeters and solid content is more than 98% ( weight percent ), can be prepared while the range of solid fraction in slurry is in 36 vol％～49vol％, with addition less than 1% ammonium polyacrylic acid, the extrusion force is range in 1641.5 Pa～6566.2 Pa. The solidfication mechanism transformation from slurry streamlet to fiber body is particle caking, result in water being extracted by aceton and static repulsion force falling as powder surface electronmotive force being reduced, when zirconium slurry extruded into solvent with low dielectric constant. Difference of velocity distribution of slurry passing through spinneret orifice and very small surface tension between slurry and extraction agent cause coarseness occurred on the fiber body surface.

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Che, Xianghong, Min Feng, Hao Jiang, Jia Song e Bei Jia. "Downscaling MODIS Surface Reflectance to Improve Water Body Extraction". Advances in Meteorology 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/424291.

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Inland surface water is essential to terrestrial ecosystems and human civilization. Accurate mapping of surface water dynamic is vital for both scientific research and policy-driven applications. MODIS provides twice observation per day, making it perfect for monitoring temporal water dynamic. Although MODIS provides two bands at 250 m resolution, accurately deriving water area always depends on observations from the spectral bands with 500 m resolution, which limits its discrimination ability over small lakes and rivers. The paper presents an automated method for downscaling the 500 m MODIS surface reflectance (SR) to 250 m to improve the spatial discrimination of water body extraction. The method has been tested at Co Ngoin and Co Bangkog in Qinghai-Tibet plateau. The downscaled SR and the derived water bodies were compared to SR and water body mapped from Landsat-7 ETM+ images were acquired on the same date. Consistency metrics were calculated to measure their agreement and disagreement. The comparisons indicated that the downscaled MODIS SR showed significant improvement over the original 500 m observations when compared with Landsat-7 ETM+ SR, and both commission and omission errors were reduced in the derived 250 m water bodies.

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Wang, Yanjun, Shaochun Li, Yunhao Lin e Mengjie Wang. "Lightweight Deep Neural Network Method for Water Body Extraction from High-Resolution Remote Sensing Images with Multisensors". Sensors 21, n. 21 (7 novembre 2021): 7397. http://dx.doi.org/10.3390/s21217397.

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Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, the shadows of buildings and other ground objects are in the same spectrum as water bodies, the existing deep convolutional neural network is difficult to train, the consumption of computing resources is large, and the methods cannot meet real-time requirements. In this paper, a water body extraction method based on lightweight MobileNetV2 is proposed and applied to multisensor high-resolution remote sensing images, such as GF-2, WorldView-2 and UAV orthoimages. This method was validated in two typical complex geographical scenes: water bodies for farmland irrigation, which have a broken shape and long and narrow area and are surrounded by many buildings in towns and villages; and water bodies in mountainous areas, which have undulating topography, vegetation coverage and mountain shadows all over. The results were compared with those of the support vector machine, random forest and U-Net models and also verified by generalization tests and the influence of spatial resolution changes. First, the results show that the F1-score and Kappa coefficients of the MobileNetV2 model extracting water bodies from three different high-resolution images were 0.75 and 0.72 for GF-2, 0.86 and 0.85 for Worldview-2 and 0.98 and 0.98 for UAV, respectively, which are higher than those of traditional machine learning models and U-Net. Second, the training time, number of parameters and calculation amount of the MobileNetV2 model were much lower than those of the U-Net model, which greatly improves the water body extraction efficiency. Third, in other more complex surface areas, the MobileNetV2 model still maintained relatively high accuracy of water body extraction. Finally, we tested the effects of multisensor models and found that training with lower and higher spatial resolution images combined can be beneficial, but that using just lower resolution imagery is ineffective. This study provides a reference for the efficient automation of water body classification and extraction under complex geographical environment conditions and can be extended to water resource investigation, management and planning.

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Chen, Chao, Liyan Wang, Yanli Chu e Xinyue He. "The method for water body information extraction in complex environment using GF-1 WFV images". E3S Web of Conferences 213 (2020): 03024. http://dx.doi.org/10.1051/e3sconf/202021303024.

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Water body is one of the most active and important earth resources, and which has a profound impact on the natural system and human society. In order to acquire surface water body information quickly, accurately and efficiently, the method of water body information extraction using remote sensing imagery has attracted the attention of many searchers. On the basis of sorting out relevant research results of water body information extraction using remote sensing imagery, this paper proposed the method of water body information extraction based on the tasseled cap transformation for complex environments such as shadow and dense vegetation. First, radiometric calibration and atmospheric correction were carried out for remote sensing images. Then, the tasseled cap transformation was performed to obtain the greenness component and wetness component. Finally, the model of water body information extraction based on the tasseled cap transformation was constructed, and the water body information was extracted. In a region of Hunan province, China, the experiment using GF-1 WFV remote sensing image shows that the extracted water body information has a clear boundary and complete shape, and the Kappa coefficient, overall accuracy and user accuracy are 0.89, 92.72%, and 88.04%, respectively.

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Yue, Hui, Yao Li, Jiaxin Qian e Ying Liu. "A new accuracy evaluation method for water body extraction". International Journal of Remote Sensing 41, n. 19 (7 luglio 2020): 7311–42. http://dx.doi.org/10.1080/01431161.2020.1755740.

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Song, Jia, e Xiangbing Yan. "The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks". Remote Sensing 15, n. 2 (15 gennaio 2023): 514. http://dx.doi.org/10.3390/rs15020514.

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Water resources are important strategic resources related to human survival and development. Water body extraction from remote sensing images is a very important research topic for the monitoring of global and regional surface water changes. Deep learning networks are one of the most effective approaches and training data is indispensable for ensuring the network accurately extracts water bodies. The training data for water body extraction includes water body samples and non-water negative samples. Cloud shadows are essential negative samples due to the high similarity between water bodies and cloud shadows, but few studies quantitatively evaluate the impact of cloud shadow samples on the accuracy of water body extraction. Therefore, the training datasets with different proportions of cloud shadows were produced, and each of them includes two types of cloud shadow samples: the manually-labeled cloud shadows and unlabeled cloud shadows. The training datasets are applied on a novel transformer-based water body extraction network to investigate how the negative samples affect the accuracy of the water body extraction network. The evaluation results of Overall Accuracy (OA) of 0.9973, mean Intersection over Union (mIoU) of 0.9753, and Kappa of 0.9747 were obtained, and it was found that when the training dataset contains a certain proportion of cloud shadows, the trained network can handle the misclassification of cloud shadows well and more accurately extract water bodies.

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Ji, Zhonglin, Yu Zhu, Yaozhong Pan, Xiufang Zhu e Xuechang Zheng. "Large-Scale Extraction and Mapping of Small Surface Water Bodies Based on Very High-Spatial-Resolution Satellite Images: A Case Study in Beijing, China". Water 14, n. 18 (16 settembre 2022): 2889. http://dx.doi.org/10.3390/w14182889.

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Surface water is a crucial resource and environmental element for human survival and ecosystem stability; therefore, accurate information on the distribution of surface water bodies is essential. Extracting this information on a large scale is commonly implemented using moderate- and low-resolution satellite images. However, the detection and analysis of more detailed surface water structures and small water bodies necessitate the use of very high-resolution (VHR) satellite images. The large-scale application of VHR images for water extraction requires convenient and accurate methods. In this paper, a method combining a pixel-level water index and image object detection is proposed. The method was tested using 2018/2019 multispectral 4-m resolution images obtained from the Chinese satellite Gaofen-2 across Beijing, China. Results show that the automatic extraction of water body information over large areas using the proposed method and VHR images is feasible. Kappa coefficient and overall accuracy of 0.96 and 99.8% after post-classification improvement were obtained for testing images inside the Beijing area. The Beijing water body dataset obtained included a total of 489.53 km2 of surface water in 2018/2019, 108.01 km2 of which were ponds with an area smaller than 2 km2. This study can be applied for water body extraction and mapping in other large regions and provides a reference for other methods for using VHR images to extract water body information on a large scale.

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Zhang, Zhili, Meng Lu, Shunping Ji, Huafen Yu e Chenhui Nie. "Rich CNN Features for Water-Body Segmentation from Very High Resolution Aerial and Satellite Imagery". Remote Sensing 13, n. 10 (13 maggio 2021): 1912. http://dx.doi.org/10.3390/rs13101912.

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Extracting water-bodies accurately is a great challenge from very high resolution (VHR) remote sensing imagery. The boundaries of a water body are commonly hard to identify due to the complex spectral mixtures caused by aquatic vegetation, distinct lake/river colors, silts near the bank, shadows from the surrounding tall plants, and so on. The diversity and semantic information of features need to be increased for a better extraction of water-bodies from VHR remote sensing images. In this paper, we address these problems by designing a novel multi-feature extraction and combination module. This module consists of three feature extraction sub-modules based on spatial and channel correlations in feature maps at each scale, which extract the complete target information from the local space, larger space, and between-channel relationship to achieve a rich feature representation. Simultaneously, to better predict the fine contours of water-bodies, we adopt a multi-scale prediction fusion module. Besides, to solve the semantic inconsistency of feature fusion between the encoding stage and the decoding stage, we apply an encoder-decoder semantic feature fusion module to promote fusion effects. We carry out extensive experiments in VHR aerial and satellite imagery respectively. The result shows that our method achieves state-of-the-art segmentation performance, surpassing the classic and recent methods. Moreover, our proposed method is robust in challenging water-body extraction scenarios.

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Zhang, Hong, Jun Wei Wang, Sheng Zhong Dong, Fang Xu Xu e Sheng Hou Wang. "The Optimization of Extraction of Cordycepin from Fruiting Body of Cordyceps militaris (L.) Link". Advanced Materials Research 393-395 (novembre 2011): 1024–28. http://dx.doi.org/10.4028/www.scientific.net/amr.393-395.1024.

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The optimization of extraction of cordycepin from fruiting body of Cordyceps militaris YCC-01 by water extraction, ethanol extraction, ultrasonic extraction, and synergistic extraction is studied in this paper. The optimal conditions, water extraction at 85°C for 2.5h plus ultrasonic extraction at 600W for 35min, were determined through high performance liquid chromatography (HPLC). The dried fruiting body of cordycepin content was 9.559 mg/g by this synergistic extraction method. The yield was 66.2% higher than the control group 85°C water extraction 2.5h and 11.3% higher than the room temperature ultrasonic extraction 35min. This method has a short extraction time, low cost, low loss of active ingredients and other characteristics with good prospects.

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Zhang, Tianyi, Chenhao Qin, Weibin Li, Xin Mao, Liyun Zhao, Biao Hou e Licheng Jiao. "Water Body Extraction of the Weihe River Basin Based on MF-SegFormer Applied to Landsat8 OLI Data". Remote Sensing 15, n. 19 (25 settembre 2023): 4697. http://dx.doi.org/10.3390/rs15194697.

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In the era of big data, making full use of remote sensing images to automatically extract surface water bodies (WBs) in complex environments is extremely challenging. Due to the weak capability of existing algorithms in extracting small WBs and WB edge information from remote sensing images, we proposed a new method—Multiscale Fusion SegFormer (MF-SegFormer)—for WB extraction in the Weihe River Basin of China using Landsat 8 OLI images. The MF-SegFormer method adopts a cascading approach to fuse features output by the SegFormer encoder at multiple scales. A feature fusion (FF) module is proposed to enhance the extraction of WB edge information, while an Atrous Spatial Pyramid Pooling (ASPP) module is employed to enhance the extraction of small WBs. Furthermore, we analyzed the impact of four kinds of band combinations on WB extraction by the MF-SegFormer model, including true color composite images, false color images, true color images, and false color images enhanced by Gaussian stretch. We also compared our proposed method with several different approaches. The results suggested that false color composite images enhanced by Gaussian stretching are beneficial for extracting WBs, and the MF-SegFormer model achieves the highest accuracy across the study area with a precision of 77.6%, recall of 84.4%, F1-score of 80.9%, and mean intersection over union (mIoU) of 83.9%. In addition, we used the determination coefficient (R2) and root-mean-square error (RMSE) to evaluate the performance of river width extraction. Our extraction results in an overall R2 of 0.946 and an RMSE of 28.21 m for the mainstream width in the “Xi’an-Xianyang” section of the Weihe River. The proposed MF-SegFormer method used in this study outperformed other methods and was found to be more robust for WB extraction.

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Liu, Haiyang, Hongda Hu, Xulong Liu, Hao Jiang, Wanxia Liu e Xiaoling Yin. "A Comparison of Different Water Indices and Band Downscaling Methods for Water Bodies Mapping from Sentinel-2 Imagery at 10-M Resolution". Water 14, n. 17 (30 agosto 2022): 2696. http://dx.doi.org/10.3390/w14172696.

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Satellite-based remote sensing is important for monitoring the spatial distribution of water resources. The water index is currently one of the most widely used water body extraction methods. Based on Sentinel-2 remote sensing image, this study combines area-to-point regression kriging interpolation, bilinear interpolation, and the Gram–Schmidt (GS) pan-sharpening method with the water indices MNDWI, AWEIsh and WI2015 to compare different water body extraction methods. The experimental results showed that all water indices have satisfactory extraction ability, with the kappa coefficient as an accuracy threshold above 0.8. Moreover, the GS downscaling method combined with the WI2015 yielded the best performance. This research demonstrates the efficacy of the WI2015 method to extract water bodies in urban areas and its ability to comprehensively describe river water bodies. The findings indicate that high-resolution band information is particularly important for improving low-resolution band downscaling results and can significantly minimize erroneous water body extraction.

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Zhang, Jing. "Water Body Information Extraction from Remote Sensing Images based on PSPNet". International Journal of Computer Science and Information Technology 2, n. 1 (24 marzo 2024): 319–25. http://dx.doi.org/10.62051/ijcsit.v2n1.33.

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Remote sensing image has the characteristics of real-time, periodicity and wide monitoring range. It can quickly and accurately obtain water area, distribution and other information, which is of great significance to the utilization and development of water resources, agricultural irrigation, flood disaster assessment and so on. Since traditional water information extraction methods only use part of image band information, the accuracy of water information extraction is low and has certain limitations. In recent years, convolutional neural network technology has developed rapidly and achieved good results in water information extraction from remote sensing images. Therefore, in this paper, Pyramid Scene Parsing Neural Network (PSPNet) was used to extract water information from Ziyuan-3 multispectral remote sensing images, to make sample sets of water, and train the convolutional neural network model. Compared with the traditional normalized difference water index (NDWI) and support vector machine (SVM), the results show that PSPNet has the highest accuracy and the lowest misclassification rate.

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Wu, Dan, e Shenglan Ye. "Research on Water Extraction from Remote Sensing Images based on Ada Boost Algorithm". Frontiers in Computing and Intelligent Systems 4, n. 1 (6 giugno 2023): 102–4. http://dx.doi.org/10.54097/fcis.v4i1.9478.

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The extraction of water in remote sensing image is a key step in the application of remote sensing image. Aiming at the existing problems in remote sensing image water extraction, a water extraction method based on Ada Boost algorithm is proposed. In this method, multiple thresholds are set for image segmentation, and the results are voted, and finally the water extraction results are obtained. Using the threshold value of 7 algorithms to synthesize, using the Ada Boost algorithm, the advantages and disadvantages of each algorithm are analyzed and compared. The experimental results show that the water body extracted by a single method and a combination of 7 methods is not ideal, but the water body extracted by a combination of two, three, four and five methods is relatively good, among which the best effect is the water body extracted by a combination of three methods.

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Woo, Park Cheol, Jeon Jong Ju, Moon Yong Ho e Eom Il Kyu. "Green Algae Detection Using Water Body Extraction and Color information". Journal of the Institute of Electronics and Information Engineers 56, n. 5 (31 maggio 2019): 43–51. http://dx.doi.org/10.5573/ieie.2019.56.5.43.

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Germán Torrijos. C et al.,, Germán Torrijos C. et al ,. "Water Body Extraction using Mixture Analysis Techniques and Mathematical Morphology". International Journal of Mechanical and Production Engineering Research and Development 10, n. 5 (2020): 789–96. http://dx.doi.org/10.24247/ijmperdoct202079.

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Zhou, Ya'nan, Jiancheng Luo, Zhanfeng Shen, Xiaodong Hu e Haiping Yang. "Multiscale Water Body Extraction in Urban Environments From Satellite Images". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, n. 10 (ottobre 2014): 4301–12. http://dx.doi.org/10.1109/jstars.2014.2360436.

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Haibo, Yang, Wang Zongmin, Zhao Hongling e Guo Yu. "Water Body Extraction Methods Study Based on RS and GIS". Procedia Environmental Sciences 10 (2011): 2619–24. http://dx.doi.org/10.1016/j.proenv.2011.09.407.

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Yu, Long, Zhiyin Wang, Shengwei Tian, Feiyue Ye, Jianli Ding e Jun Kong. "Convolutional Neural Networks for Water Body Extraction from Landsat Imagery". International Journal of Computational Intelligence and Applications 16, n. 01 (marzo 2017): 1750001. http://dx.doi.org/10.1142/s1469026817500018.

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Traditional machine learning methods for water body extraction need complex spectral analysis and feature selection which rely on wealth of prior knowledge. They are time-consuming and hard to satisfy our request for accuracy, automation level and a wide range of application. We present a novel deep learning framework for water body extraction from Landsat imagery considering both its spectral and spatial information. The framework is a hybrid of convolutional neural networks (CNN) and logistic regression (LR) classifier. CNN, one of the deep learning methods, has acquired great achievements on various visual-related tasks. CNN can hierarchically extract deep features from raw images directly, and distill the spectral–spatial regularities of input data, thus improving the classification performance. Experimental results based on three Landsat imagery datasets show that our proposed model achieves better performance than support vector machine (SVM) and artificial neural network (ANN).

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Weng, Wen Lu, Hao Min Lo, Shih Jung Chan e Wen Huang Liu. "Bioactive Component Extraction from Antrodia camphorata Fruiting Body on Artificial Agar Media". Advanced Materials Research 750-752 (agosto 2013): 1485–88. http://dx.doi.org/10.4028/www.scientific.net/amr.750-752.1485.

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Supercritical fluid extraction (carbon dioxide), water extraction and ethanol extraction are used to extract bioactive components from fruiting bodies of Antrodia Camphorata to find out the optimum extraction condition through operation variable changes. Analysis comparisons tell that the best condition for water and ethanol approaches is 95% ethanol as the solvent, 45 °C as the operation temperature and 24 hours for extraction while supercritical fluid extraction prefers 95 % ethanol as the co-solvent, 250 bar as the working pressure and 45 °C as the operation temperature. There exist more and significant low polarity peaks in HPLC detection chromatograms for supercritical fluid extract liquids.

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Guo, Hongxiang, Guojin He, Wei Jiang, Ranyu Yin, Lei Yan e Wanchun Leng. "A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images". ISPRS International Journal of Geo-Information 9, n. 4 (25 marzo 2020): 189. http://dx.doi.org/10.3390/ijgi9040189.

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Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.

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Guo, Zhishun, Lin Wu, Yabo Huang, Zhengwei Guo, Jianhui Zhao e Ning Li. "Water-Body Segmentation for SAR Images: Past, Current, and Future". Remote Sensing 14, n. 7 (6 aprile 2022): 1752. http://dx.doi.org/10.3390/rs14071752.

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Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation.

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Reddy, S. L. K., C. V. Rao, P. R. Kumar, R. V. G. Anjaneyulu e B. G. Krishna. "A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (19 novembre 2018): 323–28. http://dx.doi.org/10.5194/isprs-archives-xlii-5-323-2018.

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<p><strong>Abstract.</strong> Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to be complemented with additional information for the extraction of complete water canal networks. The proposed index MNDWI-2 is able to find the water bodies and water canals as well from the Landsat-8 OLI imagery and is based on the SWIR2 band. In this paper, we use Level-1 precision terrain corrected OLI imagery at 30 meter spatial resolution. The proposed MNDWI-2 index is derived using SWIR2 (B7) band and Green (B3) band. The usage of SWIR2 band over SWIR1 results in very low reflectance values for water features, detection of shallow water and delineation of water features with rest of the features in the image. The computed MNDWI-2 index values are threshold by making the values greater than zero as 1 and less than zero as zero. The binarised values of 1 represent the water bodies and 0 represent the non-water body. This normalized index detects the water bodies and canals as well as vegetation which appears in the form of noise. The vegetation from the MNDWI-2 image is removed by using the NDVI index, which is calculated using the Top of Atmosphere (TOA) corrected images. The paper presents the results of water canal extraction in comparison with the major available indexes. The proposed index can be used for water and water canal extraction from L8 OLI imagery, and can be extended for other high resolution sensors.</p>

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Liu, Min, Jiangping Liu e Hua Hu. "A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images". Applied Sciences 14, n. 4 (6 febbraio 2024): 1344. http://dx.doi.org/10.3390/app14041344.

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Extraction of lake water bodies from remote sensing images provides reliable data support for water resource management, environmental protection, natural disaster early warning, and scientific research, and helps to promote sustainable development, protect the ecological environment and human health. With reference to the classical encoding-decoding semantic segmentation network, we propose the network model R50A3-LWBENet for lake water body extraction from remote sensing images based on ResNet50 and three attention mechanisms. R50A3-LWBENet model uses ResNet50 for feature extraction, also known as encoding, and squeeze and excitation (SE) block is added to the residual module, which highlights the deeper features of the water body part of the feature map during the down-sampling process, and also takes into account the importance of the feature map channels, which can better capture the multiscale relationship between pixels. After the feature extraction is completed, the convolutional block attention module (CBAM) is added to give the model a global adaptive perception capability and pay more attention to the water body part of the image. The feature map is up-sampled using bilinear interpolation, and the features at different levels are fused, a process also known as decoding, to finalize the extraction of the lake water body. Compared with U-Net, AU-Net, RU-Net, ARU-Net, SER34AUNet, and MU-Net, the R50A3-LWBENet model has the fastest convergence speed and the highest MIoU accuracy with a value of 97.6%, which is able to better combine global and local information, refine the edge contours of the lake’s water body, and have stronger feature extraction capability and segmentation performance.

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Jiang, Zhiqi, Yijun Wen, Gui Zhang e Xin Wu. "Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data". Sustainability 14, n. 7 (23 marzo 2022): 3797. http://dx.doi.org/10.3390/su14073797.

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For the Sentinel-2 multispectral satellite image remote sensing data, due to the rich spatial information, the traditional water body extraction methods cannot meet the needs of practical applications. In this study, a random forest-based RF_16 optimal combination model algorithm is proposed to extract water bodies. The research process uses Sentinel-2 multispectral satellite images and DEM data as the basic data, collected 24 characteristic variable indicators (B2, B3, B4, B8, B11, B12, NDVI, MSAVI, B5, B6, B7, B8A, NDI45, MCARI, REIP, S2REP, IRECI, PSSRa, NDWI, MNDWI, LSWI, DEM, SLOPE, SLOPE ASPECT), and constructed four combined models with different input variables. After analysis, it was determined that RF_16 was the optimal combination for extracting water body information in the study area. Model. The results show that: (1) The characteristic variables that have an important impact on the accuracy of the model are the improved normalized difference water index (MNDWI), band B2 (Blue), normalized water index (NDWI), B4 (Red), B3 (Green), and band B5 (Vegetation Red-Edge 1); (2) The water extraction accuracy of the optimal combined model RF_16 can reach 93.16%, and the Kappa coefficient is 0.8214. The overall accuracy is 0.12% better than the traditional Relief F algorithm. The RF_16 method based on the optimal combination model of random forest is an effective means to obtain high-precision water body information in the study area. It can effectively reduce the “salt and pepper effect” and the influence of mixed pixels such as water and shadows on the water extraction accuracy.

33

Yang, Liping, Joshua Driscol, Sarigai Sarigai, Qiusheng Wu, Christopher D. Lippitt e Melinda Morgan. "Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing". Sensors 22, n. 6 (21 marzo 2022): 2416. http://dx.doi.org/10.3390/s22062416.

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Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.

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Qi, Baogui, Yin Zhuang, He Chen, Shan Dong e Lianlin Li. "Fusion Feature Multi-Scale Pooling for Water Body Extraction from Optical Panchromatic Images". Remote Sensing 11, n. 3 (24 gennaio 2019): 245. http://dx.doi.org/10.3390/rs11030245.

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Water body extraction is a hot research topic in remote sensing applications. Using panchromatic optical remote sensing images to extract water bodies is a challenging task, because these images have one level of gray information, variable imaging conditions, and complex scene information. Refined water body extraction from optical panchromatic images often experiences serious under- or over- segmentation problems. In this paper, for producing refined water body extraction results from optical panchromatic images, we propose a fusion feature multi-scale pooling for Markov modeling method. Markov modeling includes two aspects: label field initialization and feature field establishment. These two aspects are jointly created by the fusion feature multi-scale pooling process, and this process is proposed to enhance the feature difference between water bodies and land cover. Then, the greedy algorithm in the iteration conditional method is used to extract refined water bodies according to the rebuilt Markov initial label and feature fields. Finally, to prove the effectiveness of proposed method, extensive experiments were used with collected 2.5m SPOT 5 and 1m GF-2 optical panchromatic images and evaluation indexes (precision, recall, overall accuracy, kappa coefficient and boundary detection ratios) to demonstrate that our proposed method can produce more refined water body extraction results than the state-of-the-art methods. The global and local refined indexes are improved by about 7% and 10%, respectively.

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Li, Hengkai, Zikun Xu, Yanbing Zhou, Xiaoxing He e Minghua He. "Flood Monitoring Using Sentinel-1 SAR for Agricultural Disaster Assessment in Poyang Lake Region". Remote Sensing 15, n. 21 (5 novembre 2023): 5247. http://dx.doi.org/10.3390/rs15215247.

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An extensive number of farmlands in the Poyang Lake region of China have been submerged due to the impact of flood disasters, resulting in significant agricultural economic losses. Therefore, it is of great importance to conduct the long-term temporal monitoring of flood-induced water body changes using remote sensing technology. However, the scarcity of optical images and the complex, fragmented terrain are pressing issues in the current water body extraction efforts in southern hilly regions, particularly due to difficulties in distinguishing shadows from numerous mountain and water bodies. For this purpose, this study employs Sentinel-1 synthetic aperture radar (SAR) data, complemented by water indices and terrain features, to conduct research in the Poyang Lake area. The results indicate that the proposed multi-source data water extraction method based on microwave remote sensing data can quickly and accurately extract a large range of water bodies and realize long-time monitoring, thus proving a new technical means for the accurate extraction of floodwater bodies in the Poyang Lake region. Moreover, the comparison of several methods reveals that CAU-Net, which utilizes multi-band imagery as the input and incorporates a channel attention mechanism, demonstrated the best extraction performance, achieving an impressive overall accuracy of 98.71%. This represents a 0.12% improvement compared to the original U-Net model. Moreover, compared to the thresholding, decision tree, and random forest methods, CAU-Net exhibited a significant enhancement in extracting flood-induced water bodies, making it more suitable for floodwater extraction in the hilly Poyang Lake region. During this flood monitoring period, the water extent in the Poyang Lake area rapidly expanded and subsequently declined gradually. The peak water area reached 4080 km2 at the height of the disaster. The severely affected areas were primarily concentrated in Yongxiu County, Poyang County, Xinjian District, and Yugan County.

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Pang, Mingkun, Hongyu Pan, Hang Zhang e Tianjun Zhang. "Experimental Investigation of the Effect of Groundwater on the Relative Permeability of Coal Bodies around Gas Extraction Boreholes". International Journal of Environmental Research and Public Health 19, n. 20 (20 ottobre 2022): 13609. http://dx.doi.org/10.3390/ijerph192013609.

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Water infiltration in boreholes is a common problem in mine gas pre-extraction, where water infiltration can significantly reduce the efficiency of gas extraction and curtail the life cycle of the borehole. It is important to evaluate the effect of groundwater on the permeability of the coal body around a gas extraction borehole. In order to determine the seepage parameters of the fractured coal body system around the borehole, a water–gas two-phase seepage test was designed to determine the relative seepage parameters of the fractured coal media seepage system. The main conclusion is that the relative permeability of gas can be effectively increased by increasing the negative extraction pressure at the early stage of extraction to accelerate drainage to reduce the water saturation of the coal seam. Under the combined effect of porosity and seepage pressure, the relative permeability of gas and water in the fractured coal rock body shows three stages. The dependence of the total permeability on the effective stress is closely related to the stages in the evolution of the pore structure, and the total effective permeability decreases with the increase in the effective stress. A decrease in porosity can lead to a decrease in permeability and an increase in the non-Darcy factor. Through an in-depth analysis of the damage and permeability pattern of the coal body around the perimeter of the dipping borehole, the efficient and safe extraction of gas from dipping boreholes in water-rich mines is thus ensured.

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Wu, Xiao Dong, Yi Ning Wang, Rui He Wang, Han Han Zhang, Bei Lin Qi e Ming Zhu. "Research on Horizontal Well Inhibiting Water Coning and Tapping the Potential of Remaining Oil". Applied Mechanics and Materials 527 (febbraio 2014): 57–64. http://dx.doi.org/10.4028/www.scientific.net/amm.527.57.

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The effects of well type, water extraction time and water extraction quantity on the control of bottom water coning are studied by analytical method. The results suggest that a reservoir with low vertical permeability and interlayer above the water oil contact would have good effect of water extraction and cone control. The effect of water extraction with horizontal well is better than vertical well; the earlier the water extraction is applied, the better the effect of water control is obtained; the larger the quantity of water extraction is, the more obvious is the water control effect, and water extraction time and water extraction quantity has optimal value. In addition, water extraction and cone control is not effective to all bottom water reservoirs that are developed with horizontal well. If the vertical permeability of reservoir is high and have not effective block off of interlining or interlayer above the water oil contact or the water body is giant, the water cut of horizontal well will go up rapidly and the effect of water extraction will be difficult to achieve.

38

Chen Rujun, 陈如俊, 普运伟 Pu Yunwei, 周家厚 Zhou Jiahou, 李俊 Li Jun e 王雪峰 Wang Xuefeng. "基于GF-2号影像细小水体提取研究". Laser & Optoelectronics Progress 60, n. 16 (2023): 1628002. http://dx.doi.org/10.3788/lop222488.

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Ning, Fang-Shii, e Yu-Chan Lee. "Combining Spectral Water Indices and Mathematical Morphology to Evaluate Surface Water Extraction in Taiwan". Water 13, n. 19 (6 ottobre 2021): 2774. http://dx.doi.org/10.3390/w13192774.

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Rivers in Taiwan are characterised by steep slopes and high sediment concentrations. Moreover, with global climate change, the dynamics of channel meandering have become complicated and frequent. The primary task of river governance and disaster prevention is to analyse river changes. Spectral water indices are mostly used for surface water estimation, which separates the water from the background based on a threshold value, but it can be challenging in the case of environmental noise. Edge detection uses a canny edge detector and mathematical morphology for extracting geometrical features from the image and effective edge detection. This study combined spectral water indices and mathematical morphology to capture water bodies based on downloaded remote sensing images. From the findings, this study summarised the applicability of various spectral water body indices to the surface water extraction of different river channel patterns in Taiwan. The normalised difference water index and the modified normalised difference water index are suitable for braided rivers, whereas the automated water extraction index is ideal for meandering rivers.

40

Yu, Jie, Yang Cai, Xin Lyu, Zhennan Xu, Xinyuan Wang, Yiwei Fang, Wenxuan Jiang e Xin Li. "Boundary-Guided Semantic Context Network for Water Body Extraction from Remote Sensing Images". Remote Sensing 15, n. 17 (1 settembre 2023): 4325. http://dx.doi.org/10.3390/rs15174325.

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Automatically extracting water bodies is a significant task in interpreting remote sensing images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance in processing RSIs, which have been widely used for fine-grained extraction of water bodies. However, it is difficult for the extraction accuracy of CNNs to satisfy the requirements in practice due to the limited receptive field and the gradually reduced spatial size during the encoder stage. In complicated scenarios, in particular, the existing methods perform even worse. To address this problem, a novel boundary-guided semantic context network (BGSNet) is proposed to accurately extract water bodies via leveraging boundary features to guide the integration of semantic context. Firstly, a boundary refinement (BR) module is proposed to preserve sufficient boundary distributions from shallow layer features. In addition, abstract semantic information of deep layers is also captured by a semantic context fusion (SCF) module. Based on the results obtained from the aforementioned modules, a boundary-guided semantic context (BGS) module is devised to aggregate semantic context information along the boundaries, thereby enhancing intra-class consistency of water bodies. Extensive experiments were conducted on the Qinghai–Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. The results demonstrate that the proposed BGSNet outperforms the mainstream approaches in terms of OA, MIoU, F1-score, and kappa. Specifically, BGSNet achieves an OA of 98.97% on the QTPL dataset and 95.70% on the LoveDA dataset. Additionally, an ablation study was conducted to validate the efficacy of the proposed modules.

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Zhao, Zitong, Jin Yang, Mingjia Wang, Jiaqi Chen, Ci Sun, Nan Song, Jinyu Wang e Shulong Feng. "The PCA-NDWI Urban Water Extraction Model Based on Hyperspectral Remote Sensing". Water 16, n. 7 (27 marzo 2024): 963. http://dx.doi.org/10.3390/w16070963.

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Accurate extraction of water bodies is the basis of remote sensing monitoring of water environments. Due to the complex types of ground objects around urban water bodies, high spectral and spatial resolution are needed to achieve accurate extraction of water bodies. Addressing the limitation that most spectral index methods used for water body extraction are more suitable for open waters such as oceans and lakes, this study proposes a PCA-NDWI accurate extraction model for urban water bodies based on hyperspectral remote sensing, which combines Principal Component Analysis (PCA) with Normalized Difference Water Index (NDWI). Furthermore, aiming at the common water shadow problem in urban hyperspectral remote sensing images, the advantages of the PCA-NDWI model were further verified by experiments. By comparing the accuracy and F1-Measure of the PCA-NDWI, NDWI, HDWI, and K-means models, the results demonstrated that the PCA-NDWI model was better than the other tested methods. The accuracy and F1-Measure of the PCA-NDWI model water extraction data were 0.953 and 0.912, respectively, and the accuracy and F1-Measure of the PCA-NDWI model water shadow extraction data were 0.858 and 0.872, respectively. Therefore, the PCA-NDWI model can effectively separate shadows and the surrounding features of urban water bodies, accurately extract water body information, and has great application potential in water resources management.

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Guo, Jia, Xiaoping Wang, Bin Liu, Ke Liu, Yong Zhang e Chenfeng Wang. "Remote-Sensing Extraction of Small Water Bodies on the Loess Plateau". Water 15, n. 5 (23 febbraio 2023): 866. http://dx.doi.org/10.3390/w15050866.

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The mixed pixel of low-resolution remote-sensing image makes the traditional water extraction method not effective for small water body extraction. This study takes the Loess Plateau with complex terrain as the research area and develops a multi-index fusion threshold segmentation algorithm (MFTSA) for a large-scale small water body extraction algorithm based on GEE (Google Earth Engine). MFTSA uses the AWEI (automated water extraction index), MNDWI (modified normalized difference water index), NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index) for multi-index synergy to extract small water bodies. It also uses slope data generated by the SRTM (Shuttle Radar Topography Mission digital elevation model) and NIR band reflectance to eliminate suppressing high reflectivity noise and shadow noise. An MFTSA algorithm was proposed and the results showed that: (1) The overall extraction accuracy of the MFTSA algorithm on the Loess Plateau was 98.14%, and the correct extraction rate of small water bodies was 92.82%. (2) Compared with traditional water index methods and classification methods, the MFTSA algorithm could extract small water bodies with higher integrity and clearer and more accurate boundaries. (3) The MFTSA algorithm was used to extract a total of 69,900 small water bodies on the Loess Plateau, accounting for 97.63% of the total water bodies, and the area was 482.11 square kilometers, accounting for 16.50% of the total water bodies.

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Sun, J. Y., G. Z. Wang, G. J. He, D. C. Pu, W. Jiang, T. T. Li e X. F. Niu. "STUDY ON THE WATER BODY EXTRACTION USING GF-1 DATA BASED ON ADABOOST INTEGRATED LEARNING ALGORITHM". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (7 febbraio 2020): 641–48. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-641-2020.

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Abstract. Surface water system is an important part of global ecosystem, and the changes in surface water may lead to disasters, such as drought, waterlog, and water-borne diseases. The rapid development of remote sensing technology has supplied better strategies for water bodies extraction and further monitoring. In this study, AdaBoost and Random Forest (RF), two typical algorithms in integrated learning, were applied to extract water bodies in Chaozhou area (mainly located in Guangzhou Province, China) based on GF-1 data, and the Decision Tree (DT) was used for comparative tests to comprehensively evaluate the performance of classification algorithms listed above for surface water body extraction. The results showed that: (1) Compared with visual interpretation, AdaBoost performed better than RF in the extraction of several typical water bodies, such as rivers, lakes and ponds Moreover, the water extraction results of the strong classifiers using AdaBoost or RF were better than the weak basic classifiers. (2) For the quantitative accuracy statistics, the overall accuracy (96.5%) and kappa coefficient (93%) using AdaBoost exceeded those using RF (5.3% and 10.6%), respectively. The classification time of AdaBoost increased by 403 seconds and 918 seconds relative to RF and DT methods. However, in terms of visual interpretation, quantitative statistical accuracy and classification time, AdaBoost algorithm was more suitable for the water body extraction. (3) For the sample proportion comparison experiment of AdaBoost, four sampling proportions (0.1%, 0.2%, 1% and 2%) were chosen and 0.1% sampling proportion reached the optimum classification accuracy (93.9%) and kappa coefficient (87.8%).

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Ramkumar, E., V. S. Bala Murali, T. Guna, S. M. Dharshan e S. Ajay Vishnu. "Eco Friendly Dye Extraction From Cyanophyta for Textiles". International Journal of Innovative Technology and Exploring Engineering 10, n. 4 (28 febbraio 2021): 72–74. http://dx.doi.org/10.35940/ijitee.d8468.0210421.

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In countries like India there is always a scarcity for fresh water along with it polluting the available fresh water sources is a major threat. The major fresh water bodies are affected by eutrophication. It is the phenomenon in which algae forms a layer above the water surface and does not allow the sun light to enter into the water body. Due to this reason organisms which is present in the water body gets affected and the ecosystem gets damaged. The algae which are taken from the water bodies is dropped as waste in garbage or let to dry out on roads. “One man’s waste is other man’s treasure” so instead of wasting the collected algae, the algae can be used to prepare a dye which can be used to dye clothes for different uses. The collected algae are used as a raw material in an algal dying machine where the processes carried out are cleaning, drying the algae, grind it, boil them with water and other essentials, filter the residue and finally filtrate will be used to print on cloth.

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Yu, Long, Ruonan Zhang, Shengwei Tian, Liu Yang e Yalong Lv. "Deep Multi-Feature Learning for Water Body Extraction from Landsat Imagery". Automatic Control and Computer Sciences 52, n. 6 (novembre 2018): 517–27. http://dx.doi.org/10.3103/s0146411618060123.

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Gu, Lingxiao, Quanhua Zhao, Guanghui Wang e Yu Li. "Water body extraction based on region similarity combined adaptively band selection". International Journal of Remote Sensing 42, n. 8 (18 gennaio 2021): 2963–80. http://dx.doi.org/10.1080/01431161.2020.1842545.

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Nguyen, D. D. "WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B8 (28 luglio 2012): 181–86. http://dx.doi.org/10.5194/isprsarchives-xxxix-b8-181-2012.

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Kaplan, Gordana, e Ugur Avdan. "Object-based water body extraction model using Sentinel-2 satellite imagery". European Journal of Remote Sensing 50, n. 1 (gennaio 2017): 137–43. http://dx.doi.org/10.1080/22797254.2017.1297540.

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Zhu, Yu, Li Jian Sun e Chuan Yin Zhang. "Summary of water body extraction methods based on ZY-3 satellite". IOP Conference Series: Earth and Environmental Science 100 (dicembre 2017): 012200. http://dx.doi.org/10.1088/1755-1315/100/1/012200.

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de Souza, Guilherme Silva, Pedro Augusto Soares, Luiz Otávio Moras Filho, Gustavo Soares Santos, Luiza Ignez Mollica Marotta, Guilherme Augusto Bertelli Fernandes Clemente, Clayton Reis De Oliveira e Fabiano Martins Cunha. "Mining for sand extraction - Rio Grande: Brazilian environmental legislation applied". Revista de Gestão e Secretariado 14, n. 12 (5 dicembre 2023): 21018–31. http://dx.doi.org/10.7769/gesec.v14i12.3146.

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Abstract (sommario):

Sand is a fundamental natural resource, and its availability is crucial for economic growth and infrastructure development. The present work aimed to analyze the sand mining processes in an enterprise on the banks of the Rio Grande, in the southwest of Minas Gerais, mainly the environmental aspects and the focus on meeting legal conditions and requirements that encompass the process of extracting sand. sand extraction is a globally widespread activity, essential in the construction industry and several other industrial applications. Rivers provide a natural source of sand, a vital component in manufacturing concrete, asphalt, glass, and ceramic products and foundry. However, this activity is not without controversy, due to the significant environmental impacts it entails. In extracting sand from the bed of rivers or lakes, a dredger is used to remove sand and other materials found at the bottom of the water body, using water as a means of transport. Sand extraction is a globally widespread activity, playing an essential role in the construction industry and several other industrial applications. Rivers provide a natural source of sand, a vital component in manufacturing concrete, asphalt, glass, and ceramic products and foundry. However, this activity is not without controversy, due to the significant environmental impacts it entails. In extracting sand from the bed of rivers or lakes, a dredger is used to remove sand and other materials found at the bottom of the water body, using water as a means of transport. The main operation of this project consists of the extraction of sand and gravel intended for direct use in civil construction, as established by code A-03-01-8 in COPAM Normative Deliberation no. 217/2017 (Brazilian State Legislation), being a potential polluting activity. Furthermore, the aforementioned development is located within the buffer zone of the Serra da Canastra National Park, weighing 1 for the locational framing criteria.

Articoli di riviste sul tema "Water body extraction"

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