{"title":"针对多幅遥感图像镶嵌的全局和局部辐射度归一化的联合区块调整和变异优化","authors":"","doi":"10.1016/j.isprsjprs.2024.08.016","DOIUrl":null,"url":null,"abstract":"<div><p>Multi-temporal optical remote sensing images acquired from cross-sensor platforms often show significant radiometric differences, posing challenges when mosaicking images. These challenges include inconsistent global radiometric tones, unsmooth local radiometric transitions, and visible seamlines. In this paper, to address these challenges, we propose a two-stage approach for global and local radiometric normalization (RN) using joint block adjustment and variational optimization. In the first stage, a block adjustment based global RN (BAGRN) model is established to simultaneously perform global RN on all the images, eliminating global radiometric differences and achieving overall radiometric tonal consistency. In the second stage, a variational optimization based local RN (VOLRN) model is introduced to address the remaining local radiometric differences after global RN. The VOLRN model applies local RN to all the image blocks within a unified energy function and imposes the <span><math><mrow><msub><mi>l</mi><mn>1</mn></msub></mrow></math></span> norm constraint on the data fidelity term, providing the model with a more flexible local RN capability to radiometrically normalize the intersection and transition areas of the images. Therefore, the local radiometric discontinuities and edge artifacts can be eliminated, resulting in natural and smooth local radiometric transitions. The experimental results obtained on five challenging datasets of cross-sensor and multi-temporal remote sensing images demonstrate that the proposed approach excels in both visual quality and quantitative metrics. The proposed approach effectively eliminates global and local radiometric differences, preserves image gradients well, and has high processing efficiency. As a result, it outperforms the state-of-the-art RN approaches.</p></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":10.6000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Joint block adjustment and variational optimization for global and local radiometric normalization toward multiple remote sensing image mosaicking\",\"authors\":\"\",\"doi\":\"10.1016/j.isprsjprs.2024.08.016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Multi-temporal optical remote sensing images acquired from cross-sensor platforms often show significant radiometric differences, posing challenges when mosaicking images. These challenges include inconsistent global radiometric tones, unsmooth local radiometric transitions, and visible seamlines. In this paper, to address these challenges, we propose a two-stage approach for global and local radiometric normalization (RN) using joint block adjustment and variational optimization. In the first stage, a block adjustment based global RN (BAGRN) model is established to simultaneously perform global RN on all the images, eliminating global radiometric differences and achieving overall radiometric tonal consistency. In the second stage, a variational optimization based local RN (VOLRN) model is introduced to address the remaining local radiometric differences after global RN. The VOLRN model applies local RN to all the image blocks within a unified energy function and imposes the <span><math><mrow><msub><mi>l</mi><mn>1</mn></msub></mrow></math></span> norm constraint on the data fidelity term, providing the model with a more flexible local RN capability to radiometrically normalize the intersection and transition areas of the images. Therefore, the local radiometric discontinuities and edge artifacts can be eliminated, resulting in natural and smooth local radiometric transitions. The experimental results obtained on five challenging datasets of cross-sensor and multi-temporal remote sensing images demonstrate that the proposed approach excels in both visual quality and quantitative metrics. The proposed approach effectively eliminates global and local radiometric differences, preserves image gradients well, and has high processing efficiency. As a result, it outperforms the state-of-the-art RN approaches.</p></div>\",\"PeriodicalId\":50269,\"journal\":{\"name\":\"ISPRS Journal of Photogrammetry and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.6000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISPRS Journal of Photogrammetry and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0924271624003277\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS Journal of Photogrammetry and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0924271624003277","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
Joint block adjustment and variational optimization for global and local radiometric normalization toward multiple remote sensing image mosaicking
Multi-temporal optical remote sensing images acquired from cross-sensor platforms often show significant radiometric differences, posing challenges when mosaicking images. These challenges include inconsistent global radiometric tones, unsmooth local radiometric transitions, and visible seamlines. In this paper, to address these challenges, we propose a two-stage approach for global and local radiometric normalization (RN) using joint block adjustment and variational optimization. In the first stage, a block adjustment based global RN (BAGRN) model is established to simultaneously perform global RN on all the images, eliminating global radiometric differences and achieving overall radiometric tonal consistency. In the second stage, a variational optimization based local RN (VOLRN) model is introduced to address the remaining local radiometric differences after global RN. The VOLRN model applies local RN to all the image blocks within a unified energy function and imposes the norm constraint on the data fidelity term, providing the model with a more flexible local RN capability to radiometrically normalize the intersection and transition areas of the images. Therefore, the local radiometric discontinuities and edge artifacts can be eliminated, resulting in natural and smooth local radiometric transitions. The experimental results obtained on five challenging datasets of cross-sensor and multi-temporal remote sensing images demonstrate that the proposed approach excels in both visual quality and quantitative metrics. The proposed approach effectively eliminates global and local radiometric differences, preserves image gradients well, and has high processing efficiency. As a result, it outperforms the state-of-the-art RN approaches.
期刊介绍:
The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive.
P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields.
In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.