Coal mine area monitoring method by machine learning and multispectral remote sensing images

2019 ◽  
Vol 103 ◽  
pp. 103070 ◽  
Author(s):  
Dakuo He ◽  
Ba Tuan Le ◽  
Dong Xiao ◽  
Yachun Mao ◽  
Feng Shan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Coal Mine ◽  
Remote Sensing Images ◽  
Monitoring Method ◽  
Mine Area ◽  
Multispectral Remote Sensing ◽  
Area Monitoring

scholarly journals Curvelet Transform based Denoising of Multispectral Remote Sensing Images

2021 ◽  
Vol 2089 (1) ◽  
pp. 012064
Author(s):  
P. Lokeshwara Reddy ◽  
Santosh Pawar ◽  
S.L. Prathapa Reddy
Keyword(s):  
Remote Sensing ◽  
Signal To Noise Ratio ◽  
Curvelet Transform ◽  
Structural Similarity ◽  
Spatial Location ◽  
Multispectral Image ◽  
Sensor Technology ◽  
Remote Sensing Images ◽  
Unequally Spaced ◽  
Multispectral Remote Sensing

Abstract With the advent of sensor technology, the exertion of multispectral image (MSI) is comely omnipresent. Denoising is an essential quest in multispectral image processing which further improves recital of unmixing, classification and supplementary ensuing praxis. Explication and ocular analysis are essential to extricate data from remote sensing images for broad realm of supplications. This paper describes curvelet transform based denoising of multispectral remote sensing images. The implementation of curvelet transform is done by using both wrapping function and unequally spaced fast Fourier transform (USFFT) and they diverge in selection of spatial grid which is used to construe curvelets at every orientation and scale. The coefficients of curvelets are docket by a scaling factor, angle and spatial location criterion. This paper crisps on denoising of Linear Imaging Self Scanning Sensor (LISS) III images. The proposed denoising approach has also been collated with some existing schemes for assessment. The efficacy of proposed approach is analyzed with calculation of facet matrices such as Peak signal to noise ratio and Structural similarity at distinct variance of noise..

scholarly journals Research on Cultivated Land Change Based on GIS and High Resolution Remote Sensing Images

2018 ◽  
Vol 228 ◽  
pp. 02013
Author(s):  
Haibo Yu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Automatic Monitoring ◽  
Research Area ◽  
Decision Fusion ◽  
Land Change ◽  
Fuzzy Decision ◽  
Remote Sensing Images ◽  
Monitoring Method ◽  
Gis Data

This paper study an automatic monitoring method for land change based on high resolution remote sensing images and GIS data, and we use three classification methods to classify and fuse the research area. Secondly, the paper calculates the corresponding map class components and compares them with their historical attributes; it can automatically monitor land use change. The experimental results show that the fuzzy decision fusion classification can significantly improve the classification effect, and it can accurately determine the change area accurately and automatically. However, there are some partial errors in the region.

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