Publication Type : Conference Paper
Publisher : Springer Nature Singapore
Source : Lecture Notes in Electrical Engineering
Url : https://doi.org/10.1007/978-981-99-0085-5_56
Campus : Amritapuri
School : School of Computing
Year : 2023
Abstract : The images from the Earth Observation (EO) sensors can be classified as panchromatic (PAN) and multispectral (MS) based on spectral bands. The MS bands have lower spatial resolution than PAN which makes the fusion of these bands aka pan-sharpening important. Here, different multi-resolution analysis (MRA) techniques are used for pan-sharpening. The results are compared using six different evaluation metrics such as correlation coefficient (CC), standard deviation (SD), peak-signal to noise ratio (PSNR), root mean square error (RMSE), spectral angle mapper (SAM), and erreur relative globale adimensionnelle de synth’ese (ERGAS). Though CC, SD, and RMSE are almost similar for all the methods, Haar has the highest PSNR, and Coiflet has the lowest SAM and ERGAS value.
Cite this Research Publication : Aparna S. Menon, J. Aravinth, S. Veni, Pan-Sharpening of Multi-spectral Remote Sensing Data Using Multi-resolution Analysis, Lecture Notes in Electrical Engineering, Springer Nature Singapore, 2023, https://doi.org/10.1007/978-981-99-0085-5_56