Abstract
Over the past few years, deep learning (DL) algorithms have dramatically increased in popularity, especially in the field of remote sensing. Image segmentation basically involves the detection and classification of individual objects within the image. In case of satellite images, the objects may be buildings, roads, vegetation, waterbodies, and so on. In the present work, DeepLabv3+ which is a state-of-the-art network is used to extract water bodies. It has an encoder–decoder architecture with atrous convolution between encoder and decoder. Encoder is used to extract high-level information from the input image. This extracted information is then further used for the segmentation task. Quality of encoder architecture used therefore has significant impact on the result of segmentation task. The existing encoder architecture in DeepLabv3+ is Xception. Along with this, ResNet-50 and U-Net are the other two encoder architectures that are considered for this study.
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References
Menon AK, Sajith Variyar VV, Sivanpillai R, Sowmya V, Brown GK, Soman KP (2022) Epiphyte Segmentation using DRU-Net. In: Data engineering and ıntelligent computing: proceedings of 5th ICICC 2021, vol 1, pp 101–108. Springer, Singapore
Giri A, Sajith Variyar VV, Sowmya V, Sivanpillai R, Soman KP (2022) Multiple oil pad detection using deep learning. In: The international archives of the photogrammetry, remote sensing and spatial ınformation sciences, vol 46, pp 91–96
Gouda N, Amudha J (2020) Skin cancer classification using ResNet. In: 2020 IEEE 5th international conference on computing communication and automation (ICCCA), pp 536–541. IEEE
Kampffmeyer M, Salberg AB, Jenssen R (2016) Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1–9
Dong R, Pan X, Li F (2019) DenseU-net-based semantic segmentation of small objects in urban remote sensing images. IEEE Access 7:65347–65356
Yi Y, Zhang Z, Zhang W, Zhang C, Li W, Zhao T (2019) Semantic segmentation of urban buildings from VHR remote sensing imagery using a deep convolutional neural network. Remote Sens 11(15):1774
Du Z, Yang J, Ou C, Zhang T (2019) Smallholder crop area mapped with a semantic segmentation deep learning method. Remote Sens 11(7):888
Huang L, Wu X, Peng Q, Yu X (2021) Depth semantic segmentation of tobacco planting areas from unmanned aerial vehicle remote sensing images in plateau mountains. J Spectros 1–14
Shi P, Jiang Q, Shi C, Xi J, Tao G, Zhang S, Wu Q (2021) Oil well detection via large-scale and high-resolution remote sensing ımages based on ımproved YOLO v4. Remote Sens 13(16):3243
Dechesne C, Mallet C, Le Bris A, Gouet-Brunet V (2017) Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery. ISPRS J Photogram Remote Sens 126:129–145
Isikdogan F, Bovik AC, Passalacqua P (2017) Surface water mapping by deep learning. IEEE J Select Top Appl Earth Observ Rem Sens 10(11):4909–4918
Dong S, Pang L, Zhuang Y, Liu W, Yang Z, Long T (2019) Optical remote sensing water-land segmentation representation based on proposed SNS-CNN network. In: IGARSS 2019–2019 IEEE ınternational geoscience and remote sensing symposium, pp 3895–3898. IEEE
Filatov D, Yar GNAH (2022) Forest and water bodies segmentation through satellite ımages using U-Net. arXiv preprint arXiv:2207.11222
Guo H, He G, Jiang W, Yin R, Yan L, Leng W (2020) A multi-scale water extraction convolutional neural network (MWEN) method for GaoFen-1 remote sensing images. ISPRS Int J Geo-Inform 9(4):189
Dan LI, Baosheng WU, Bowei CHEN, Yuan XUE, Yi ZHANG (2020) Review of water body information extraction based on satellite remote sensing. J Tsinghua Univ (Sci Technol) 60(2):147–161
Cheng G, Xie X, Han J, Guo L, Xia GS (2020) Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities. IEEE J Select Top Appl Earth Observ Remot Sens 13:3735–3756
Satellite Images of WaterBodies-Kaggle. https://www.kaggle.com/datasets/franciscoescobar/satellite
McFeeters SK (1996) The use of the normalized difference water ındex (NDWI) in the delineation of open water features. Int J Remote Sens 17(7):1425–1432
Dutta A, Zisserman A (2019) The VIA annotation software for images, audio, and video. In: MM 2019—proceedings of the 27th ACM ınternational conference on Multimedia, pp 2276–2279
Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
He K, Zhang X, Ren S, Sun (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778. IEEE
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical ımage computing and computer-assisted ıntervention–MICCAI 2015: 18th international conference, Munich, Germany, 5–9 Oct 2015, Proceedings, Part III 18, pp 234–241. Springer International Publishing
Darshik AS, Dev A, Bharath M, Nair BA, Gopakumar G (2020) Semantic segmentation of spectral images: a comparative study using FCN8s and U-NET on RIT-18 dataset. In: 11th international conference on computing, communication and networking technologies (ICCCNT), pp 1–6. IEEE
Acknowledgements
Authors thank Prof. K. P. Soman, Head, Center for Computational Engineering and Networking (CEN) at Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, and colleagues Mr. Bichu George and Ms. Gosula Sunandini for their valuable support in dataset preprocessing task.
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Adarsh, S., Sowmya, V., Sivanpillai, R., Sajith Variyar, V.V. (2023). Performance Analysis of DeeplabV3+ Using State-of-the-Art Encoder Architectures for Waterbody Segmentation in Remote Sensing Images. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_34
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