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Sand mining is a booming industry. The river sandbank is one of the primarysources of sand mining. Detection of potential river sandbank regions for sandmining directly impacts the economy, society, and environment. In the past,semi-supervised and supervised techniques have been used to detect miningregions including sand mining. A few techniques employ multi-modal analysiscombining different modalities such as multi-spectral imaging, syntheticaperture radar (\emph{SAR}) imaging, aerial images, and point cloud data.However, the distinguishing spectral characteristics of river sandbank regionsare yet to be fully explored. This paper provides a novel method to detectriver sandbank regions for sand mining using multi-spectral images without anylabeled data over the seasons. Association with a river stream and theabundance of minerals are the most prominent features of such a region. Theproposed work uses these distinguishing features to determine the spectralsignature of a river sandbank region, which is robust to other high mineralabundance regions. It follows a two-step approach, where first, potential highmineral regions are detected and next, they are segregated using the presenceof a river stream. The proposed technique provides average accuracy, precision,and recall of 90.75%, 85.47%, and 73.5%, respectively over the seasons fromLandsat 8 images without using any labeled dataset.

Detection of River Sandbank for Sand Mining with the Presence of Other High Mineral Content Regions Using Multi-spectral Images