A Deep Learning Method of Water Body Extraction From High Resolution Remote Sensing Images With Multisensors

Water body extraction from remote sensing images is an important task. Deep learning has become a more popular method for extracting water bodies from remote sensing images. However, these methods are usually aimed at a specific sensor and are not applicable. Thus, we proposed a new network, called the dense-local-feature-compression (DLFC) network aiming at extracting water body from different remote sensing images automatic. In this network, each layer of the network can receive the feature maps of all layers before it by the densely connected module of DenseNet. The concatenate operation on the feature dimension is used when connecting across layers. It can realize the different levels of features reuse. The local-feature-compression module is introduced before concatenate operation. It can obtain the more abstract features further by the convolution operation. Through the DLFC, we can fuse the spatial and spectral information for the remote sensing images that can extract water body from different remote sensing images. Besides, we construct a new water body dataset based on GaoFen-2 (GF-2) remote sensing images. The proposed DLFC achieved excellent performance with GF-2, GaoFen-6, Sentinel-2, and ZY-3 remote sensing images. Compared with the traditional water body extraction method and contemporary networks, the DLFC exhibits noticeable improvement. The results indicate that the DLFC can realize water body extraction from multisource remote sensing images automatically and rapidly.

[1]  Xiaodong Li,et al.  Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band , 2016, Remote. Sens..

[2]  Fuchun Sun,et al.  Aerial Scene Classification with Convolutional Neural Networks , 2015, ISNN.

[3]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Gui-Song Xia,et al.  Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery , 2015, Remote. Sens..

[5]  Wei Li,et al.  DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Xiao Xiang Zhu,et al.  Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources , 2017, IEEE Geoscience and Remote Sensing Magazine.

[7]  Xi Chen,et al.  Recognizing Global Reservoirs From Landsat 8 Images: A Deep Learning Approach , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  E. Work,et al.  Utilization of satellite data for inventorying prairie ponds and lakes , 1976 .

[10]  Richard Sliuzas,et al.  Spatial impact of urban expansion on surface water bodies—A case study of Wuhan, China , 2010 .

[11]  S. K. McFeeters The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[12]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[13]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[14]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  F. Ling,et al.  Analysis of Landsat-8 OLI imagery for land surface water mapping , 2014 .

[16]  Fabio Roli,et al.  Support vector machines for remote sensing image classification , 2001, SPIE Remote Sensing.

[17]  Alan C. Bovik,et al.  Surface Water Mapping by Deep Learning , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Hui Yang,et al.  An Improved Method for Road Extraction from High-Resolution Remote-Sensing Images that Enhances Boundary Information , 2020, Sensors.

[19]  Y Zhou Information Extraction of Thin Rivers around Built-up Lands with False NDWI , 2014 .

[20]  Zhiwei Li,et al.  Extracting urban water by combining deep learning and Google Earth Engine , 2019, ArXiv.

[21]  Wei Wu,et al.  Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water , 2018, Remote. Sens..

[22]  Liangpei Zhang,et al.  Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data , 2020, 2001.04650.

[23]  Ei Moh Moh Aung,et al.  Ayeyarwady River Regions Detection and Extraction System from Google Earth Imagery , 2018, 2018 IEEE International Conference on Information Communication and Signal Processing (ICICSP).

[24]  Hui Yang,et al.  Land Use Classification of the Deep Convolutional Neural Network Method Reducing the Loss of Spatial Features , 2019, Sensors.

[25]  Xiaoming Zhang,et al.  A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI , 2013, Remote. Sens..

[26]  Gang Fu,et al.  Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network , 2017, Remote. Sens..

[27]  Lei Guo,et al.  When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[28]  P. Frazier,et al.  Water body detection and delineation with Landsat TM data. , 2000 .

[29]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[30]  Jun Kong,et al.  Convolutional Neural Networks for Water Body Extraction from Landsat Imagery , 2017, Int. J. Comput. Intell. Appl..

[31]  Kaiming He,et al.  Group Normalization , 2018, ECCV.

[32]  Yang Chen,et al.  Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning , 2018 .

[33]  Hanqiu Xu Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[34]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[35]  Md. Nasir Sulaiman,et al.  Artificial Neural Networks for Satellite Image Classification of Shoreline Extraction for Land and Water Classes of the North West Coast of Peninsular Malaysia , 2018 .

[36]  Lucy Bastin,et al.  A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data , 2014 .

[37]  Pierre Grussenmeyer,et al.  Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery , 2018, Remote Sensing of Environment.