Abstract
River is one of the most important land classes of our environment and civilization since the ancient times. Several factors including excessive river sedimentation, industrial waste, illegal mining, affect the river health to the extent of narrower river trails, change of courses, and different levels of water pollution. Hence, monitoring of river health has become a crucial issue, where remote sensing based observations are applied in recent times. There are several indexes to detect water bodies from multispectral images. However, detecting and isolating rivers, especially narrow rivers are found challenging. Further, higher degree of sinuosity triggers the change of river direction and narrowness of the river width. Due to this narrowness, a complete river trail appears as segments of disconnected trails. Additionally, the spectral properties of narrow river trails are found to be similar to different land classes, especially highways, when these indexes are used. In this work, we have proposed a novel technique to detect narrow river trails based on the spatial features and pixel associativity with the presence of highways without labelled dataset. The spatial texture of narrow river trails is assumed different from most of the other land classes detected in these water indexes. The roads, which are comprehensible from mid-resolution satellite images, are generally highways and have less sinuosity. These characteristics are considered here to separate narrow river trails from those land classes having near similar spectral characteristics. The proposed technique has precision, recall and accuracy of \(84.52\%\), \(71.51\%\), and \(96.97\%\), respectively.
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Mukherjee, J., Gupta, P., Gautam, H., Chintalapati, R. (2023). Detection of Narrow River Trails with the Presence of Highways from Landsat 8 OLI Images. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_50
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