{"title":"Visible-to-Infrared Image Translation for Matching Tasks","authors":"Decao Ma;Shaopeng Li;Juan Su;Yong Xian;Tao Zhang","doi":"10.1109/JSTARS.2024.3468456","DOIUrl":null,"url":null,"abstract":"Visible-to-infrared image translation is an important way to enrich infrared data. However, the reliability of the data generated by image translation in downstream tasks has always been controversial. This article proposes a method that integrates visible-to-infrared image translation tasks and multimodal template matching tasks. The image generation network is based on a generative adversarial networks (GANs), and network training is supervised by L1 loss, GANs loss, and match loss, where the matching loss includes normalized cross-correlation (NCC) loss and match patch (MP) loss. NCC loss is constructed based on the NCC matching algorithm. MP loss is calculated by modeling template matching as a contrastive learning problem. In experiments on the KAIST, VEDAI, and AVIID datasets, this method outperforms state-of-the-art methods in terms of image generation quality and template matching accuracy. Our method incorporates the image matching process into image-to-image translation, demonstrating the usability of GANs-based image generation for critical downstream tasks. This research resolves the practical controversy of generating images based on GANs and provides a theoretical reference for image generation for tasks, such as multisource image object detection and data association.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694789","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/10694789/","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
Visible-to-infrared image translation is an important way to enrich infrared data. However, the reliability of the data generated by image translation in downstream tasks has always been controversial. This article proposes a method that integrates visible-to-infrared image translation tasks and multimodal template matching tasks. The image generation network is based on a generative adversarial networks (GANs), and network training is supervised by L1 loss, GANs loss, and match loss, where the matching loss includes normalized cross-correlation (NCC) loss and match patch (MP) loss. NCC loss is constructed based on the NCC matching algorithm. MP loss is calculated by modeling template matching as a contrastive learning problem. In experiments on the KAIST, VEDAI, and AVIID datasets, this method outperforms state-of-the-art methods in terms of image generation quality and template matching accuracy. Our method incorporates the image matching process into image-to-image translation, demonstrating the usability of GANs-based image generation for critical downstream tasks. This research resolves the practical controversy of generating images based on GANs and provides a theoretical reference for image generation for tasks, such as multisource image object detection and data association.
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.