{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:16:39Z","timestamp":1753884999510,"version":"3.41.2"},"reference-count":34,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:p> The optical satellite sensors encounter certain constraints on producing high-resolution multispectral (HRMS) images. Pan-sharpening (PS) is a remote sensing image fusion technique, which is an effective mechanism to overcome the limitations of available imaging products. The prevalent issue in PS algorithms is the imbalance between spatial quality and spectral details preservation, thereby producing intensity variations in the fused image. In this paper, a PS method is proposed based on convolutional sparse coding (CSC) implemented in the non-subsampled shearlet transform (NSST) domain. The source images, panchromatic (PAN) and multispectral (MS) images, are decomposed using NSST.\u00a0The resultant high-frequency bands are fused using adaptive weights determined from chaotic grey wolf optimization (CGWO) algorithm. The CSC-based model is employed to fuse the low-frequency bands. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four datasets with different geographical content like urban area, vegetation, etc. and eight existing algorithms are used for evaluation of the proposed PS method. The comprehensive visual and quantitative results approve that the proposed method accomplishes considerable improvement in spatial and spectral details equivalence in the pan-sharpened image. <\/jats:p>","DOI":"10.1142\/s0219467823500134","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T15:12:04Z","timestamp":1640272324000},"source":"Crossref","is-referenced-by-count":2,"title":["Pan-Sharpening for Spectral Details Preservation Via Convolutional Sparse Coding in Non-Subsampled Shearlet Space"],"prefix":"10.1142","volume":"23","author":[{"given":"Dhara J.","family":"Sangani","sequence":"first","affiliation":[{"name":"ECE Department, Vishwakarma Government Engineering College, Chandkheda, Ahmedabad, Gujarat 382424, India"}]},{"given":"Rajesh A.","family":"Thakker","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering and Technology (GSET), Gujarat Technological University (GTU), Ahmedabad 382424, India"}]},{"given":"S.\u00a0D.","family":"Panchal","sequence":"additional","affiliation":[{"name":"Computer Engineering, Gujarat Technological University, Ahmedabad, India"}]},{"given":"Rajesh","family":"Gogineni","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communications Engineering, Dhanekula Institute of Engineering & Technology, Vijayawada 521139, India"}]}],"member":"219","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"S0219467823500134BIB001","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2014.2361734","volume":"53","author":"Vivone G.","year":"2015","journal-title":"IEEE Trans. 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