Yuqian Zhu;Weitao Chen;Wenxi He;Ruizhen Wang;Xianju Li;Lizhe Wang
{"title":"CUG_MISDataset:用于改进广域高精度采矿占地识别的遥感实例分割数据集","authors":"Yuqian Zhu;Weitao Chen;Wenxi He;Ruizhen Wang;Xianju Li;Lizhe Wang","doi":"10.1109/JSTARS.2024.3454333","DOIUrl":null,"url":null,"abstract":"The effective and rapid acquisition of wide-area mine occupation information is crucial for ecological geo-environmental protection and sustainable development. Remote sensing instance segmentation technology based on deep learning is a promising solution. However, there are two significant challenges including insufficient training datasets and unsuitable segmentation models. To overcome these issues, this study provides a large-scale remote sensing instance segmentation dataset for mining land occupation (CUG_MISDataset). The CUG_MISDataset comprises 1426 image blocks and more than 3000 instances, covering all 150 types of mines found in China's Hubei province. It features multiple mine types, various land occupations, and complex instance scales. First, this study compares the performance of seven mainstream remote sensing instance segmentation models using the proposed CUG_MISDataset. The results show that all seven models achieve high segmentation accuracy. It indicates that the constructed CUG_MISDataset is robust and can serve as a valuable benchmark for remote sensing instance segmentation of mining areas. Second, aiming at the difficulty of large scale variation in this dataset, we propose a multiscale dilation feature pyramid network (MSD-FPN), which introduces a dynamic weight allocation mechanism to give more weight to important semantic information, while convolution with different dilation rates is used in the module to enhance the expression of mines’ multiscale features. The proposed MSD-FPN can achieve a 2.0% average precision improvement on the CUG_MISDataset.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675591","citationCount":"0","resultStr":"{\"title\":\"CUG_MISDataset: A Remote Sensing Instance Segmentation Dataset for Improved Wide-Area High-Precision Mining Land Occupation Recognition\",\"authors\":\"Yuqian Zhu;Weitao Chen;Wenxi He;Ruizhen Wang;Xianju Li;Lizhe Wang\",\"doi\":\"10.1109/JSTARS.2024.3454333\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The effective and rapid acquisition of wide-area mine occupation information is crucial for ecological geo-environmental protection and sustainable development. Remote sensing instance segmentation technology based on deep learning is a promising solution. However, there are two significant challenges including insufficient training datasets and unsuitable segmentation models. To overcome these issues, this study provides a large-scale remote sensing instance segmentation dataset for mining land occupation (CUG_MISDataset). The CUG_MISDataset comprises 1426 image blocks and more than 3000 instances, covering all 150 types of mines found in China's Hubei province. It features multiple mine types, various land occupations, and complex instance scales. First, this study compares the performance of seven mainstream remote sensing instance segmentation models using the proposed CUG_MISDataset. The results show that all seven models achieve high segmentation accuracy. It indicates that the constructed CUG_MISDataset is robust and can serve as a valuable benchmark for remote sensing instance segmentation of mining areas. Second, aiming at the difficulty of large scale variation in this dataset, we propose a multiscale dilation feature pyramid network (MSD-FPN), which introduces a dynamic weight allocation mechanism to give more weight to important semantic information, while convolution with different dilation rates is used in the module to enhance the expression of mines’ multiscale features. The proposed MSD-FPN can achieve a 2.0% average precision improvement on the CUG_MISDataset.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675591\",\"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/10675591/\",\"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}","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/10675591/","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}
CUG_MISDataset: A Remote Sensing Instance Segmentation Dataset for Improved Wide-Area High-Precision Mining Land Occupation Recognition
The effective and rapid acquisition of wide-area mine occupation information is crucial for ecological geo-environmental protection and sustainable development. Remote sensing instance segmentation technology based on deep learning is a promising solution. However, there are two significant challenges including insufficient training datasets and unsuitable segmentation models. To overcome these issues, this study provides a large-scale remote sensing instance segmentation dataset for mining land occupation (CUG_MISDataset). The CUG_MISDataset comprises 1426 image blocks and more than 3000 instances, covering all 150 types of mines found in China's Hubei province. It features multiple mine types, various land occupations, and complex instance scales. First, this study compares the performance of seven mainstream remote sensing instance segmentation models using the proposed CUG_MISDataset. The results show that all seven models achieve high segmentation accuracy. It indicates that the constructed CUG_MISDataset is robust and can serve as a valuable benchmark for remote sensing instance segmentation of mining areas. Second, aiming at the difficulty of large scale variation in this dataset, we propose a multiscale dilation feature pyramid network (MSD-FPN), which introduces a dynamic weight allocation mechanism to give more weight to important semantic information, while convolution with different dilation rates is used in the module to enhance the expression of mines’ multiscale features. The proposed MSD-FPN can achieve a 2.0% average precision improvement on the CUG_MISDataset.
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.