scholarly journals HA-Net: A Lake Water Body Extraction Network Based on Hybrid-scale Attention and Transfer Learning

2021 ◽  
Vol 13 (20) ◽  
pp. 4121
Author(s):  
Zhaobin Wang ◽  
Xiong Gao ◽  
Yaonan Zhang
Keyword(s):  
Remote Sensing ◽  
Lake Water ◽  
Water Body ◽  
Single Layer ◽  
Landsat 8 ◽  
The Tibetan Plateau ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Training Time ◽  
Water Rate

Due to the large quantity of noise and complex spatial background of the remote sensing images, how to improve the accuracy of semantic segmentation has become a hot topic. Lake water body extraction is crucial for disaster detection, resource utilization, and carbon cycle, etc. The the area of lakes on the Tibetan Plateau has been constantly changing due to the movement of the Earth’s crust. Most of the convolutional neural networks used for remote sensing images are based on single-layer features for pixel classification while ignoring the correlation of such features in different layers. In this paper, the two-branch encoder is presented, which is a multiscale structure that combines the features of ResNet-34 with a feature pyramid network. Secondly, adaptive weights are distributed to global information using the hybrid-scale attention block. Finally, PixelShuffle is used to recover the feature maps’ resolution, and the densely connected block is used to refine the boundary of the lake water body. Likewise, we transfer the best weights which are saved on the Google dataset to the Landsat-8 dataset to ensure that our proposed method is robust. We validate the superiority of Hybrid-scale Attention Network (HA-Net) on two given datasets, which were created by us using Google and Landsat-8 remote sensing images. (1) On the Google dataset, HA-Net achieves the best performance of all five evaluation metrics with a Mean Intersection over Union (MIoU) of 97.38%, which improves by 1.04% compared with DeepLab V3+, and reduces the training time by about 100 s per epoch. Moreover, the overall accuracy (OA), Recall, True Water Rate (TWR), and False Water Rate (FWR) of HA-Net are 98.88%, 98.03%, 98.24%, and 1.76% respectively. (2) On the Landsat-8 dataset, HA-Net achieves the best overall accuracy and the True Water Rate (TWR) improvement of 2.93% compared to Pre_PSPNet, which proves to be more robust than other advanced models.

scholarly journals LaeNet: A Novel Lightweight Multitask CNN for Automatically Extracting Lake Area and Shoreline from Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 56
Author(s):  
Wei Liu ◽  
Xingyu Chen ◽  
Jiangjun Ran ◽  
Lin Liu ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Landsat 8 ◽  
The Tibetan Plateau ◽  
Lake Area ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Typical Part ◽  
Application Fields

Variations of lake area and shoreline can indicate hydrological and climatic changes effectively. Accordingly, how to automatically and simultaneously extract lake area and shoreline from remote sensing images attracts our attention. In this paper, we formulate lake area and shoreline extraction as a multitask learning problem. Different from existing models that take the deep and complex network architecture as the backbone to extract feature maps, we present LaeNet—a novel end-to-end lightweight multitask fully CNN with no-downsampling to automatically extract lake area and shoreline from remote sensing images. Landsat-8 images over Selenco and the vicinity in the Tibetan Plateau are utilized to train and evaluate our model. Experimental results over the testing image patches achieve an Accuracy of 0.9962, Precision of 0.9912, Recall of 0.9982, F1-score of 0.9941, and mIoU of 0.9879, which align with the mainstream semantic segmentation models (UNet, DeepLabV3+, etc.) or even better. Especially, the running time of each epoch and the size of our model are only 6 s and 0.047 megabytes, which achieve a significant reduction compared to the other models. Finally, we conducted fieldwork to collect the in-situ shoreline position for one typical part of lake Selenco, in order to further evaluate the performance of our model. The validation indicates high accuracy in our results (DRMSE: 30.84 m, DMAE: 22.49 m, DSTD: 21.11 m), only about one pixel deviation for Landsat-8 images. LaeNet can be expanded potentially to the tasks of area segmentation and edge extraction in other application fields.

scholarly journals Potential Impact of Flood on Schistosomiasis in Poyang Lake Regions Based on Multi-Source Remote Sensing Images

2020 ◽  
Author(s):  
Jing-Bo Xue ◽  
Xin-Yi Wang ◽  
Li-Juan Zhang ◽  
Yu-Wan Hao ◽  
Zhe Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Poyang Lake ◽  
Transmission Risk ◽  
Schistosomiasis Japonica ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Poyang Lake Region ◽  
Lake Region ◽  
Dual Polarized

Abstract BackgroundFlooding may be the most important factors contributing to the rebound of Oncomelania hupensis in endemic foci. This study aimed to assess the risk of schistosomiasis japonica transmission impacted by flooding around the Poyang Lake region using multi-source remote sensing images.MethodsNormalized Difference Vegetation Index (NDVI) data collected by the Landsat 8 satellite was used as an ecological and geographical suitability indicator of O. hupensis snail habitats in the Poyang Lake region. The flood-affected water body expansion was estimated using dual polarized threshold calculations based on the dual polarized synthetic aperture radar (SAR). The image data were captured from Sentinel-1B satellite in May 2020 before the flood and in July 2020 during the flood. The spatial database of snail habitats distribution was created by using the 2016 snail survey in Jiangxi Province. The potential spread of O. hupensis snails after the flood was predicted by an overlay analysis of the NDVI maps of flood-affected water body areas. In addition, the risk of schistosomiasis transmission was classified based on O. hupensis snail density data and the related NDVI. ResultsThe surface area of Poyang Lake was approximately 2,207 km2 in May 2020 before the flood and 4,403 km2 in July 2020 during the period of the flood peak, and the flood-caused expansion of water body was estimated as 99.5%. After the flood, the potential snail habitats were predicted to be concentrated in areas neighboring the existing habitats in marshlands of the Poyang Lake. The areas with high risk of schistosomiasis transmission were predicted to be mainly distributed in Yongxiu, Xinjian, Yugan and Poyang (District) along Poyang Lake. By comparing the predictive results and actual snail distribution, the predictive accuracy of the model was estimated as 87%, which meant the 87% of actual snail distribution were correctly identified as the snail habitats in the model predictions. ConclusionsFlood-affected water body expansion and environmental factors pertaining to snail breeding may be rapidly extracted from Landsat 8 and Sentinel-1B remote sensing images. The applications of multi-source remote sensing data are feasible for the timely and effective assessment of the potential schistosomiasis transmission risk caused by snail spread during the flood disaster, which is of great significance for precision control of schistosomiasis.

scholarly journals MONITORING AND ASSESSMENT OF ECOLOGICAL CHANGE IN COASTAL CITIES BASED ON RSEI

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 461-470
Author(s):  
X. Y. Liu ◽  
X. X. Zhang ◽  
Y. R. He ◽  
H. J. Luan
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Principal Component ◽  
Vegetation Coverage ◽  
Weighting Coefficient ◽  
Landsat 8 ◽  
Ecological Change ◽  
Remote Sensing Images ◽  
Environment Change ◽  
Sentinel 2A

Abstract. With the speeding up of urbanization process, ecological problems, such as unsustainable land use and environmental pollution,have emerged one after another in cites. Nowadays, green development and ecological priority are the important concepts and trends of the current new urban planning in China. In this study, Pingtan County, a coastal city in Fujian Province, China, was taken as the research area. Based on two Landsat 8 remote sensing images (2016, 2017), and two Sentinel-2A remote sensing images (2016, 2017), we first adopt the modified normalized water body index (MNDWI) to mask the water body. Four indicators, including greenness, humidity, dryness and heat were extracted to synthesize the remote sensing ecological index (RSEI), which were obtained by principal component analysis method. Based on the RSEI values acquired from Landsat 8 and Sentinel-2A images, the ecological environment change trend in Pingtan County was evaluated .The experimental results show that: 1) The RSEI indicators based on Landsat 8 and sentinel data all show a downward trend, but due to due to the influence of image spatial resolution and PCA weighting coefficient, the RSEI index has different degrees of decline. 2) The main reason for the decline in RSEI is the increase in NDSI indicators. Compared with July 2016, the bare ground increased in April 2017. Although the NDVI has increased, the overall trend is still declining. Therefore, it is necessary to ecologically return farmland and improve vegetation coverage in the future development process. 3) In recent years, the ecological quality of new construction land near drinking water sources has declined, so it is necessary to strengthen monitoring of changes in the region.

scholarly journals Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method

2020 ◽  
Vol 12 (12) ◽  
pp. 1937
Author(s):  
Mengjiao Qin ◽  
Linshu Hu ◽  
Zhenhong Du ◽  
Yi Gao ◽  
Lianjie Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Super Resolution ◽  
Input Image ◽  
Geographical Information ◽  
Landsat 8 ◽  
The Tibetan Plateau ◽  
Lake Area ◽  
Remote Sensing Images ◽  
Important Indicator

Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications.

scholarly journals Potential impact of flooding on schistosomiasis in Poyang Lake regions based on multi-source remote sensing images

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jing-Bo Xue ◽  
Xin-Yi Wang ◽  
Li-Juan Zhang ◽  
Yu-Wan Hao ◽  
Zhe Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Vegetation Index ◽  
Predictive Accuracy ◽  
Poyang Lake ◽  
Transmission Risk ◽  
Jiangxi Province ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Dual Polarized

Abstract Background Flooding is considered to be one of the most important factors contributing to the rebound of Oncomelania hupensis, a small tropical freshwater snail and the only intermediate host of Schistosoma japonicum, in endemic foci. The aim of this study was to assess the risk of intestinal schistosomiasis transmission impacted by flooding in the region around Poyang Lake using multi-source remote sensing images. Methods Normalized Difference Vegetation Index (NDVI) data collected by the Landsat 8 satellite were used as an ecological and geographical suitability indicator of O. hupensis habitats in the Poyang Lake region. The expansion of the water body due to flooding was estimated using dual-polarized threshold calculations based on dual-polarized synthetic aperture radar (SAR). The image data were captured from the Sentinel-1B satellite in May 2020 before the flood and in July 2020 during the flood. A spatial database of the distribution of snail habitats was created using the 2016 snail survey in Jiangxi Province. The potential spread of O. hupensis snails after the flood was predicted by an overlay analysis of the NDVI maps in the flood-affected areas around Poyang Lake. The risk of schistosomiasis transmission was classified based on O. hupensis snail density data and the related NDVI. Results The surface area of Poyang Lake was approximately 2207 km2 in May 2020 before the flood and 4403 km2 in July 2020 during the period of peak flooding; this was estimated to be a 99.5% expansion of the water body due to flooding. After the flood, potential snail habitats were predicted to be concentrated in areas neighboring existing habitats in the marshlands of Poyang Lake. The areas with high risk of schistosomiasis transmission were predicted to be mainly distributed in Yongxiu, Xinjian, Yugan and Poyang (District) along the shores of Poyang Lake. By comparing the predictive results and actual snail distribution, we estimated the predictive accuracy of the model to be 87%, which meant the 87% of actual snail distribution was correctly identified as snail habitats in the model predictions. Conclusions Data on water body expansion due to flooding and environmental factors pertaining to snail breeding may be rapidly extracted from Landsat 8 and Sentinel-1B remote sensing images. Applying multi-source remote sensing data for the timely and effective assessment of potential schistosomiasis transmission risk caused by snail spread during flooding is feasible and will be of great significance for more precision control of schistosomiasis.

scholarly journals MSLWENet: A Novel Deep Learning Network for Lake Water Body Extraction of Google Remote Sensing Images

2020 ◽  
Vol 12 (24) ◽  
pp. 4140
Author(s):  
Zhaobin Wang ◽  
Xiong Gao ◽  
Yaonan Zhang ◽  
Guohui Zhao
Keyword(s):  
Remote Sensing ◽  
Lake Water ◽  
Water Body ◽  
Water Quality Monitoring ◽  
Input Image ◽  
Experimental Results ◽  
Water Bodies ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Multi Scale

Lake water body extraction from remote sensing images is a key technique for spatial geographic analysis. It plays an important role in the prevention of natural disasters, resource utilization, and water quality monitoring. Inspired by the recent years of research in computer vision on fully convolutional neural networks (FCN), an end-to-end trainable model named the multi-scale lake water extraction network (MSLWENet) is proposed. We use ResNet-101 with depthwise separable convolution as an encoder to obtain the high-level feature information of the input image and design a multi-scale densely connected module to expand the receptive field of feature points by different dilation rates without increasing the computation. In the decoder, the residual convolution is used to abstract the features and fuse the features at different levels, which can obtain the final lake water body extraction map. Through visual interpretation of the experimental results and the calculation of the evaluation indicators, we can see that our model extracts the water bodies of small lakes well and solves the problem of large intra-class variance and small inter-class variance in the lakes’ water bodies. The overall accuracy of our model is up to 98.53% based on the evaluation indicators. Experimental results demonstrate that the MSLWENet, which benefits from the convolutional neural network, is an excellent lake water body extraction network.

scholarly journals Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

2021 ◽  
Vol 13 (9) ◽  
pp. 1713
Author(s):  
Songwei Gu ◽  
Rui Zhang ◽  
Hongxia Luo ◽  
Mengyao Li ◽  
Huamei Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Life ◽  
Attention Mechanism ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Remote Sensing Images ◽  
Training Time ◽  
Adversarial Networks ◽  
Remote Sensing Image Classification

Deep learning is an important research method in the remote sensing field. However, samples of remote sensing images are relatively few in real life, and those with markers are scarce. Many neural networks represented by Generative Adversarial Networks (GANs) can learn from real samples to generate pseudosamples, rather than traditional methods that often require more time and man-power to obtain samples. However, the generated pseudosamples often have poor realism and cannot be reliably used as the basis for various analyses and applications in the field of remote sensing. To address the abovementioned problems, a pseudolabeled sample generation method is proposed in this work and applied to scene classification of remote sensing images. The improved unconditional generative model that can be learned from a single natural image (Improved SinGAN) with an attention mechanism can effectively generate enough pseudolabeled samples from a single remote sensing scene image sample. Pseudosamples generated by the improved SinGAN model have stronger realism and relatively less training time, and the extracted features are easily recognized in the classification network. The improved SinGAN can better identify sub-jects from images with complex ground scenes compared with the original network. This mechanism solves the problem of geographic errors of generated pseudosamples. This study incorporated the generated pseudosamples into training data for the classification experiment. The result showed that the SinGAN model with the integration of the attention mechanism can better guarantee feature extraction of the training data. Thus, the quality of the generated samples is improved and the classification accuracy and stability of the classification network are also enhanced.

scholarly journals An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2818
Author(s):  
Hai Sun ◽  
Xiaoyi Dai ◽  
Wenchi Shou ◽  
Jun Wang ◽  
Xuejing Ruan
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Water Body ◽  
Remote Sensing Data ◽  
Flood Inundation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Tracking Model

Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model's performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

Sign in / Sign up

Export Citation Format

Share Document