Urban Water Extraction with UAV High-Resolution Remote Sensing Data Based on an Improved U-Net Model

Obtaining water body images quickly and reliably is important to guide human production activities and study urban change. This paper presents a fast and accurate method to identify water bodies in complex environments based on UAV high-resolution images. First, an improved U-Net (SU-Net) model is proposed in this paper. By increasing the number of connections in the middle layer of the neural network, more image features can be retained through S-shaped circular connections. Second, aiming at the interference of mixed ground objects and dark ground objects on water detection, the fusion of a deep learning network and visual features is investigated. We analyse the influence of a wavelet transform and grey level cooccurrence matrix (GLCM) on water extraction. Using a confusion matrix to evaluate accuracy, the following conclusions are drawn: (1) Compared with existing methods, the SU-Net method achieves a significant improvement in accuracy, and the overall accuracy (OA) is 96.25%. The kappa coefficient (KC) is 0.952. (2) SU-Net combined with the GLCM has a higher accuracy (OA is 97.4%) and robustness in distinguishing mixed and dark objects. Based on this method, a distinct water boundary in urban areas, which provides data for urban water vector mapping, can be obtained.

[1]  P. Thangaraj,et al.  Improved Gabor Filter for Extracting Texture Edge Features in Ultrasound Kidney Images , 2010 .

[2]  M. N. Sumaiya,et al.  Unsupervised Change Detection of Flood affected areas in SAR images using Rayleigh based Bayesian Thresholding , 2018 .

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

[4]  P. Sathyanarayana,et al.  Image Texture Feature Extraction Using GLCM Approach , 2013 .

[5]  T. Dube,et al.  Surface water bodies mapping in Zimbabwe using landsat 8 OLI multispectral imagery: A comparison of multiple water indices , 2018, Physics and Chemistry of the Earth, Parts A/B/C.

[6]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[8]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Francesco Bianconi,et al.  Evaluation of the effects of Gabor filter parameters on texture classification , 2007, Pattern Recognit..

[10]  Fan Zhang,et al.  Deep Convolutional Neural Networks for Hyperspectral Image Classification , 2015, J. Sensors.

[11]  David C. Mason,et al.  etection of flooded urban areas in high resolution Synthetic perture Radar images using double scattering , 2013 .

[12]  Xuezhi Feng,et al.  Object-oriented method for urban vegetation mapping using IKONOS imagery , 2010 .

[13]  George P. Petropoulos,et al.  Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood , 2015, Remote. Sens..

[14]  Mark J. F. Gales,et al.  Speech Recognition using SVMs , 2001, NIPS.

[15]  Kamal E. Alameh,et al.  Plant discrimination by Support Vector Machine classifier based on spectral reflectance , 2018, Comput. Electron. Agric..

[16]  Tzai-Hung Wen,et al.  Extracting urban water bodies from high-resolution radar images: Measuring the urban surface morphology to control for radar's double-bounce effect , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Ling Shao,et al.  Building recognition in urban environments: A survey of state-of-the-art and future challenges , 2014, Inf. Sci..

[18]  Xinwu Li,et al.  Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses , 2013, Remote. Sens..

[19]  Jiang Heng-xian,et al.  A water extraction technique based on high-spatial remote sensing images , 2004 .

[20]  Youn Jung Park,et al.  SVM-based feature extraction for face recognition , 2010, Pattern Recognit..

[21]  Parisa Kordjamshidi,et al.  Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data , 2019, Sensors.

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

[23]  D. Roberts,et al.  Open water detection in urban environments using high spatial resolution remote sensing imagery , 2020 .

[24]  Chen Jing-bo.  Research on Urban Water Body Extraction Using Knowledge-based Decision Tree , 2013 .

[25]  Guofan Shao,et al.  Object-based urban vegetation mapping with high-resolution aerial photography as a single data source , 2013 .

[26]  Wenchi Shou,et al.  An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data , 2021, Remote Sensing.

[27]  Mark A. Trigg,et al.  Development of a global ~90m water body map using multi-temporal Landsat images , 2015, Remote Sensing of Environment.

[28]  Na Zhao,et al.  Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening , 2017, Remote. Sens..

[29]  Michal Strzelecki,et al.  MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..

[30]  A. J. Luis,et al.  A Rapid Extraction of Water Body Features from Antarctic Coastal Oasis Using Very High-Resolution Satellite Remote Sensing Data , 2015 .

[31]  Jianhua Gong,et al.  UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis , 2015, Remote. Sens..

[32]  Yu Peng.  Textured Image Segmentation Based on Gauss Markov Random Field Mixture Model , 2006 .

[33]  Yongchao Zhao,et al.  Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product , 2015, Remote. Sens..

[34]  Rune Hylsberg Jacobsen,et al.  A cloud detection algorithm for satellite imagery based on deep learning , 2019, Remote Sensing of Environment.

[35]  C. A. Rishikeshan,et al.  An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[36]  George Alan Blackburn,et al.  River network delineation from Sentinel-1 SAR data , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[37]  Albert J. Kettner,et al.  Near-real-time non-obstructed flood inundation mapping using synthetic aperture radar , 2019, Remote Sensing of Environment.

[38]  Jianhua Gong,et al.  Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China , 2015 .

[39]  Fen Chen,et al.  Classification of Lymphoma Cell Image Based on Improved SVM , 2015 .

[40]  Gulcan Sarp,et al.  Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey , 2017 .

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

[42]  Kondreddi Gopi.  Fingerprint Recognition Using Gabor Filter And Frequency Domain Filtering , 2012 .

[43]  Xing Fang,et al.  A novel water index for urban high-resolution eight-band WorldView-2 imagery , 2016, Int. J. Digit. Earth.