Abstract
Spatial features with spectral properties enhance the quality of satellite image while mapping complex land cover. These features are integrated with the proposed classification approach for improving classification results. The ultimate objective of this investigation is to provide high-level features to the convolutional neural network (CNN) for mapping flooded regions (before and after) using remote sensing data. Here, boundary-based segmentation is done to recognize the dimensions and scales of objects. Modeling a fully trained Convolutional network is feasible for training a huge amount of data in remote sensing studies. Fine-tuned CNN is utilized with slight modification for attaining classified Landsat images. Classification outcomes and confusion matrix are manipulated using B-CNN are compared with classifiers like SVM, random forest (RF) to compute B-CNN efficiency.
References
Rujoiu-Mare M R and Mihai B A 2016 Mapping land cover using remote sensing data and GIS techniques: A case study of Prahova Subcarpathians. Procedia Environ. Sci. 32: 244–255
Bian X, Chen C, Tian L and Du Q 2017 Fusing local and global features for high-resolution scene classification. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 10: 2889–2901
Huang L, Chen C, Li W and Du Q 2016 Remote sensing image scene classification using multiscale completed local binary patterns and Fisher vectors. Remote Sens. 8: 483–499
Liu Y and Huang C 2018 Scene Classification via Triplet Networks. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 11: 220–237
Tuia D, Persello C, and Bruzzone L 2017 Recent advances in domain adaptation for the classification of remote sensing data. IEEE Geosci. Remote Sens. Mag. 4: 41–57
Zhong Y, Fei F, Liu Y, Zhao B, Jiao H and Zhang L 2017 SatCNN: satellite image dataset classification using agile convolutional neural networks. Remote Sens. Lett. 8: 136–145
Yuan Y, Wan J and Wang Q 2016 Congested scene classification via efficient unsupervised feature learning and density estimation. Pattern Recogn. 56: 159–169
Yao X, Han J, Cheng G, Qian X and Guo L 2016 Semantic annotation of high-resolution satellite images via weakly supervised learning. IEEE Trans. Geosci. Remote Sens. 54: 3660–3671
Maggiori E, Tarabalka Y, Charpiat G and Alliez P 2017 Convolutional neural networks for large-scale remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 55: 645–657
Paisitkriangkrai S, Sherrah J, Janney P and Van Den Hengel A 2016 Semantic labelling of aerial and satellite imagery IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9: 2868–2881
Prajoona V, Sriramakrishnan P, Sridhar S, Charlyn Pushpa Latha G, Priya A, Ramkumar S, Robert Singh A and Rajendran T 2020 Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine. J. Ambient Intell. Hum. Comput. 11: 1–14
Rajendran T, Sridhar K P, Manimurugan S and Deepa S 2019 Recent innovations in soft computing applications. Curr. Signal Transduct. Ther. 14: 129–130
Rajendran T, Sridhar K P, Manimurugan S and Deepa S 2019 Advanced algorithms for medical image processing. Open Biomed. Eng. J. 13: 102.
Hariraj V, Khairunizam W, Vikneswaran V, Ibrahim Z, Shahriman A B, Zuradzman M R, Rajendran T and Sathiyasheelan R 2018 Fuzzy multi-layer SVM classification of breast cancer mammogram images. Int. J. Mech. Eng. Technol. 9: 1281–1299
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Banupriya, R., Rajiv Kannan, A. Satellite image based flood classification in urban areas using B-convolutional networks. Sādhanā 45, 186 (2020). https://doi.org/10.1007/s12046-020-01423-0
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DOI: https://doi.org/10.1007/s12046-020-01423-0