{"title":"ICL-Net: Inverse Cognitive Learning Network for Remote Sensing Image Dehazing","authors":"Weida Dong;Chunyan Wang;Xiping Xu","doi":"10.1109/JSTARS.2024.3454754","DOIUrl":null,"url":null,"abstract":"When imaging the Earth's surface, space-based optical imaging sensors are inevitably interfered by scattering media, such as clouds and haze, resulting in serious degradation of remote sensing images they capture. To enhance the quality of remote sensing images and mitigate the influence of clouds, haze, and other media, we construct a novel approach called the inverse cognitive learning network. The network mainly consists of multiscale inverse cognitive learning blocks that we designed. It has the capability to extract image features at multiple scales, adaptively focus on the global information and location-related local information, and effectively constrain the haze. In the multiscale inverse cognitive learning block, we embed the designed inverse cognitive learning module and parallel haze constraint module. The inverse cognitive learning module simulates the inverse process of human brain cognitive image, and gradually learns the haze information from the depth, moderate, and breadth channel features. The parallel haze constraint module integrates the extracted haze information through a dual-branch approach to realize strong constraints on haze features. Experimental results indicate that our approach notably enhances the clarity of remote sensing images that suffer from cloud cover and haze, and possesses more perfect haze removal effect and robustness than state-of-the-art dehazing approaches.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10665990","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10665990/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
When imaging the Earth's surface, space-based optical imaging sensors are inevitably interfered by scattering media, such as clouds and haze, resulting in serious degradation of remote sensing images they capture. To enhance the quality of remote sensing images and mitigate the influence of clouds, haze, and other media, we construct a novel approach called the inverse cognitive learning network. The network mainly consists of multiscale inverse cognitive learning blocks that we designed. It has the capability to extract image features at multiple scales, adaptively focus on the global information and location-related local information, and effectively constrain the haze. In the multiscale inverse cognitive learning block, we embed the designed inverse cognitive learning module and parallel haze constraint module. The inverse cognitive learning module simulates the inverse process of human brain cognitive image, and gradually learns the haze information from the depth, moderate, and breadth channel features. The parallel haze constraint module integrates the extracted haze information through a dual-branch approach to realize strong constraints on haze features. Experimental results indicate that our approach notably enhances the clarity of remote sensing images that suffer from cloud cover and haze, and possesses more perfect haze removal effect and robustness than state-of-the-art dehazing approaches.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.