:: Volume 8, Issue 3 (1-2021) ::
jgit 2021, 8(3): 61-82 Back to browse issues page
Modeling the potential of Sand and Dust Storm sources formation using time series of remote sensing data, fuzzy logic and artificial neural network (A Case study of Euphrates basin)
Ramin Papi, Meysam Argany Dr. *, Shahab Moradipour, Masoud Soleimani
University of Tehran
Abstract:   (363 Views)
Due to the differences between the visible and thermal infrared images, the combination of these two types of images leads to better understanding of  the characteristics of targets and the environment. Thermal infrared images are really in distinguishing targets from the background based on the radiation differences and  land surface temperature (LST) calculation. However, their spatial resolution is relatively low, making it challenging to detect targets. Image fusion is an efficient method to be  employed to enhance spatial resolution of the thermal bands through fusing these images with high spatial resolution visible images. Multi-scale transforms (MST) and sparse representation (SR) are widely used in image fusion. To improve the performance of image fusion, these two types of methods can be combined. In this paper, nine image fusion methods based on the multi-scale transform and sparse representation, namely Laplacian pyramid (LP), ratio of low-pass pyramid (RP), wavelet transform (Wavelet), dual-tree complex wavelet transform (DTCWT), curvelet transform (CVT), nonsubsampled contourlet transform (NSCT), sparse representation (SR), hybrid sparse representation and Laplacian pyramid methods (LP-SR) and hybrid sparse representation and NSCT methods (NSCT-SR) are tested on FLIR and landsat-8 thermal infrared and visible images. To evaluate the performance of different image fusion methods we used following three the quantitative evaluation metrics: entropy (EN), mutual information (MI), and gradient based fusion metric )QAB/F(. Despite the lack of spectral coverage between the visible and thermal infrared bands of Landsat 8, quantitative evaluation metrics showed that the hybrid LP-SR method provides the best result (EN=7.362, MI=2.605, QAB/F =0.531) and fused images have a better visual quality. This method improves spatial details along with  preserving the thermal radiation information. This method is followed by RP, LP, and NSCT methods. Similar results were achieved in FLIR images.
Keywords: Sand and Dust Storm (SDS), Remote Sensing, Time Series, Artificial Neural Network, Euphrates Basin
Full-Text [PDF 2259 kb]   (109 Downloads)    
Type of Study: Research | Subject: RS
Received: 2020/01/4 | Accepted: 2020/12/16 | Published: 2021/01/19

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Volume 8, Issue 3 (1-2021) Back to browse issues page