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

TL;DR

This paper introduces a novel unsupervised multi-spectral imaging method to detect river sandbanks for sand mining, effectively distinguishing them from other mineral-rich regions with high accuracy across seasons.

Contribution

The work presents a new spectral signature-based approach that identifies river sandbanks without labeled data, leveraging association with river streams and mineral abundance.

Findings

Achieved 90.75% accuracy in sandbank detection

Demonstrated robustness across different seasons

Effectively segregated sandbanks from other mineral regions

Abstract

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