Abstract

Automatic extraction of water bodies from satellite imagery has been broadly studied for many reasons, including mapping of natural resources (i.e., forest and water resources), drinking water supplies, food production, agricultural planning, and disaster management. With the growth of global warming, it became essential to maintain the sustainable management of these resources for the preservation of human life. Several methods attempted to allocate water bodies from different satellite imagery in both spatial and spectral domains. In this paper, we present an automatic segmentation method to extract the water body from Landsat satellite imagery. The proposed segmentation approach consists of several stages, including histogram stretching, de-correlation, binarization of the image, and clutter removal using morphological operations. The segmentation results are promising.

Keyword

Rivers detection, Satellite images, Enhancement, Segmentation, Recognition, De-correlation.

Cite this article

Ciaburri C, Benitez MK, Sheta A, Braik M

Refference

[1][1]Stagl J, Mayr E, Koch H, Hattermann FF, Huang S. Effects of climate change on the hydrological cycle in central and eastern Europe. In managing protected areas in central and eastern Europe under climate change 2014 (pp. 31-43). Springer, Dordrecht.

[2][2]Rizvi IA, Mohan BK, Bhatia PR. Automatic object extraction using object based image classification technique from high resolution remotely sensed images. In proceedings of the international conference and workshop on emerging trends in technology 2010 (pp. 623-8).

[3][3]Hakdaoui S, Emran A. Extraction of water information based on sar radar and optical image processing: case of flood disaster in southern Morocco. In geospatial technology 2020 (pp. 15-29). Springer, Cham.

[4][4]Zhang F, Li J, Zhang B, Shen Q, Ye H, Wang S, Lu Z. A simple automated dynamic threshold extraction method for the classification of large water bodies from landsat-8 OLI water index images. International Journal of Remote Sensing. 2018; 39(11):3429-51.

[5][5]Zhang W, Hu B, Brown GS. Automatic surface water mapping using polarimetric SAR data for long-term change detection. Water. 2020; 12(3).

[6][6]Hu S, Qin J, Ren J, Zhao H, Ren J, Hong H. Automatic extraction of water inundation areas using sentinel-1 data for large plain areas. Remote Sensing. 2020; 12(2).

[7][7]Al-Amri SS, Kalyankar NV. Image segmentation by using threshold techniques. arXiv preprint arXiv:1005.4020. 2010.

[8][8]Gong Z, Wang Q, Guan H, Zhou D, Zhang L, Jing R, et al. Extracting tidal creek features in a heterogeneous background using Sentinel-2 imagery: a case study in the Yellow River Delta, China. International Journal of Remote Sensing. 2020; 41(10):3653-76.

[9][9]Sakurai-Amano T, Onuki S, Takagi M. Automatic extraction of rivers in tropical rain forests from JERS-1 SAR images using spectral and spatial information. In international geoscience and remote sensing symposium 2002 (pp. 3429-31). IEEE.

[10][10]Le TA, Lam DT, Vo P, Yoshitaka A, Le HB. Recover water bodies in multi-spectral satellite images with deep neural nets. In Proceedings of the ninth international symposium on information and communication technology 2018 (pp. 281-8).

[11][11]Bello MM, Nasidi NM, Shanono NJ. Remote sensing as a tool for irrigation water management.2014.

[12][12]Shah V, Choudhary A, Tewari K. River extraction from satellite image. International Journal of Computer Science Issues. 2011; 8(4):386-91.

[13][13]Zhaohui Z, Prinet V, Songde MA. Water body extraction from multi-source satellite images. In IGARSS 2003. International geoscience and remote sensing symposium. proceedings (IEEE Cat. No. 03CH37477) 2003 (pp. 3970-2). IEEE.

[14][14]Wang X, Xie H. A review on applications of remote sensing and geographic information systems (GIS) in water resources and flood risk management.

[15][15]Tymków P, Jóźków G, Walicka A, Karpina M, Borkowski A. Identification of water body extent based on remote sensing data collected with unmanned aerial vehicle. Water. 2019; 11(2).

[16][16]Randazzo G, Barreca G, Cascio M, Crupi A, Fontana M, Gregorio F, et al. Analysis of very high spatial resolution images for automatic shoreline extraction and satellite-derived bathymetry mapping. Geosciences. 2020; 10(5):172.

[17][17]Dhanachandra N, Manglem K, Chanu YJ. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science. 2015; 54:764-71.

[18][18]Syrris V, Ferri S, Ehrlich D, Pesaresi M. Image enhancement and feature extraction based on low-resolution satellite data. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015; 8(5):1986-95.

[19][19]Jiang W, He G, Long T, Ni Y, Liu H, Peng Y, Lv K, Wang G. Multilayer perceptron neural network for surface water extraction in Landsat 8 OLI satellite images. Remote Sensing. 2018; 10(5).

[20][20]Rishikeshan CA, Ramesh H. An ANN supported mathematical morphology based algorithm for lakes extraction from satellite images. ISH Journal of Hydraulic Engineering. 2018; 24(2):222-9.

[21][21]Miao Z, Fu K, Sun H, Sun X, Yan M. Automatic water-body segmentation from high-resolution satellite images via deep networks. IEEE Geoscience and Remote Sensing Letters. 2018; 15(4):602-6.

[22][22]Li K, Hu X, Jiang H, Shu Z, Zhang M. Attention-guided multi-scale segmentation neural network for interactive extraction of region objects from high-resolution satellite imagery. Remote Sensing. 2020; 12(5):789.

[23][23]Meng L, Zhang Z, Zhang W, Ye J, Wu C, Chen D, et al. An automatic extraction method for lakes and reservoirs using satellite images. IEEE Access. 2019; 7:62443-56.

[24][24]Feng W, Sui H, Huang W, Xu C, An K. Water body extraction from very high-resolution remote sensing imagery using deep U-Net and a superpixel-based conditional random field model. IEEE Geoscience and Remote Sensing Letters. 2018; 16(4):618-22.

[25][25]Rishikeshan CA, Ramesh H. An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images. ISPRS Journal of Photogrammetry and Remote Sensing. 2018; 146:11-21.

[26][26]Patel HJ, Dabhi VK, Prajapati HB. River water pollution analysis using high resolution satellite images: a survey. In 5th international conference on advanced computing & communication systems (ICACCS) 2019 (pp. 520-5). IEEE.

[27][27]Navarro JC, Salazar-Garibay A, Téllez-Quiñones A, Orozco-del-Castillo M, López-Caloca AA. Inland water body extraction in complex reliefs from Sentinel-1 satellite data. Journal of Applied Remote Sensing. 2019; 13(1).

[28][28]Dereli MA, Tercan E. Assessment of shoreline changes using historical satellite images and geospatial analysis along the lake salda in turkey. Earth Science Informatics. 2020:1-10.

[29][29]Wang Y, Li Z, Zeng C, Xia GS, Shen H. An urban water extraction method combining deep learning and google earth engine. Journal of selected topics in applied earth observations and remote sensing. 2020; 13:768-81.

[30][30]Liu Y, Zhang P, He Y, Peng Z. River detection based on feature fusion from synthetic aperture radar images. Journal of Applied Remote Sensing. 2020; 14(1):016505.

[31][31]Nguyen UN, Pham LT, Dang TD. An automatic water detection approach using Landsat 8 OLI and google earth engine cloud computing to map lakes and reservoirs in New Zealand. Environmental monitoring and assessment. 2019; 191(4):235.

[32][32]Abubakar FM. Study of image segmentation using thresholding technique on a noisy image. International Journal of Science and Research. 2013:49-51.

[33][33]Sekertekin A. A survey on global thresholding methods for mapping open water body using sentinel-2 satellite imagery and normalized difference water index. Archives of Computational Methods in Engineering. 2020:1-3.

[34][34]Japitana MV, Demetillo AT, Burce ME, Taboada EB. Catchment characterization to support water monitoring and management decisions using remote sensing. Sustainable Environment Research. 2019; 29(1):1-10.

[35][35]Guo Q, Wu X, Sang X, Fu Y, Zang Y, Gong X. An integrated study on change detection and environment evaluation of surface water. Applied Water Science. 2020.

[36][36]Karvelis PS, Fotiadis DI. A region based decorrelation stretching method: Application to multispectral chromosome image classification. In international conference on image processing 2008 (pp. 1456-9). IEEE.

[37][37]Zhao M, Zhang C, Zhang W, Li W, Zhang J. Decorrelation-stretch based cloud detection for total sky images. In visual communications and image processing (VCIP) 2015 (pp. 1-4). IEEE.

[38][38]Campbell NA. The decorrelation stretch transformation. International Journal of Remote Sensing. 1996; 17(10):1939-49.

[39][39]Comaniciu D, Meer P. Robust analysis of feature spaces: color image segmentation. In proceedings of computer society conference on computer vision and pattern recognition 1997 (pp. 750-5). IEEE.

[40][40]Sheta A, Alkasassbeh M, Braik M, Ayyash HA. Detection of oil spills in SAR images using threshold segmentation algorithms. International Journal of Computer Applications. 2012; 57(7):10-15.

[41][41]Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic imaging. 2004; 13(1):146-66.

[42][42]Vala HJ, Baxi A. A review on Otsu image segmentation algorithm. International Journal of Advanced Research in Computer Engineering & Technology. 2013; 2(2):387-9.