Detection of coal stockpiles using geospatial satellite images

Abhijeet Chowdhary; Akash M. Jadhav; Abhishek Jaiswal; Bolaka Mukherjee; Urvi Shah; Pallavi Gawade; Bhushan Jagyasi; R.G. Varma Vegesna; Nasiruddin Mohammad; Gopali Contractor

doi:10.1117/12.2532523

8 October 2019 Detection of coal stockpiles using geospatial satellite images

Abhijeet Chowdhary, Akash M. Jadhav, Abhishek Jaiswal, Bolaka Mukherjee, Urvi Shah, Pallavi Gawade, Bhushan Jagyasi, R.G. Varma Vegesna, Nasiruddin Mohammad, Gopali Contractor

Author Affiliations +

Proceedings Volume 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X; 111560R (2019) https://doi.org/10.1117/12.2532523
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Spontaneous combustion of coal is a critical problem encountered by mining and thermal power industries. Usually, coal is stored in open area in the form of stockpiles in the coal mines and thermal power plants. In this paper, we have focused on localization of open-cast coal mines and coal stockpiles using satellite images to automate the entire process of prediction of spontaneous combustion in the coal stockpiles. We have used USGS Landsat-8 Satellite images, collected from various coal mines and thermal plants across the world. The satellite images consist of 11 bands including Red, Blue, Green, Near Infrared (NIR), and Shortwave Infrared (SWIR). Apart from the reflectance measurements obtained from these bands, we also use standard indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI) and Normalized Difference Water Index (NDWI) as the features to train the models. The ground truth for the training dataset has been created by manually annotating these images for three classes: coal mines, coal stockpiles and water bodies. The Fully Convolutional Network (FCN) based U-Net architecture has been trained to develop two models to classify pixels between (A) Coal Mine and Water and (B) Coal Stockpile and Water. In this paper, we present an exhaustive experimental results to demonstrate the effective localization of coal mine and coal stockpiles using the proposed FCN based approach.

Conference Presentation

Citation Download Citation

Abhijeet Chowdhary, Akash M. Jadhav, Abhishek Jaiswal, Bolaka Mukherjee, Urvi Shah, Pallavi Gawade, Bhushan Jagyasi, R.G. Varma Vegesna, Nasiruddin Mohammad, and Gopali Contractor "Detection of coal stockpiles using geospatial satellite images", Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 111560R (8 October 2019); https://doi.org/10.1117/12.2532523

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available