{"title":"Multibranch Network for Addressing Intraclass Variation in Remote Sensing Building Detection","authors":"Ryuhei Hamaguchi","doi":"10.1109/JSTARS.2024.3454110","DOIUrl":null,"url":null,"abstract":"This article presents multibranch network architecture for addressing the problem of large intraclass variation in building detection task. Previous methods solved the problem by learning single structured and shared feature space with regularization. However, we reveal that the feature sharing strategy is less advantageous at deeper layers. We have analyzed the channel-wise contribution of the deep features for recognizing individual buildings and find that the feature space is separated into several clusters, among which the discriminative features are not shared much. Based on the analysis, we propose a multibranch neural network that solves the problem by decomposing a building class into subclasses and learning specialized feature space for each subclass. The proposed model is demonstrated on two remote sensing building detection benchmarks, where the model outperforms the state-of-the-art segmentation models and the previous techniques for addressing the large intraclass variation.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663870","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/10663870/","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
This article presents multibranch network architecture for addressing the problem of large intraclass variation in building detection task. Previous methods solved the problem by learning single structured and shared feature space with regularization. However, we reveal that the feature sharing strategy is less advantageous at deeper layers. We have analyzed the channel-wise contribution of the deep features for recognizing individual buildings and find that the feature space is separated into several clusters, among which the discriminative features are not shared much. Based on the analysis, we propose a multibranch neural network that solves the problem by decomposing a building class into subclasses and learning specialized feature space for each subclass. The proposed model is demonstrated on two remote sensing building detection benchmarks, where the model outperforms the state-of-the-art segmentation models and the previous techniques for addressing the large intraclass variation.
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.