Gunjan Joshi;Celia A. Baumhoer;Andreas J. Dietz;Ryo Natsuaki;Akira Hirose
{"title":"利用可信度感知的可解释反映射神经网络进行多传感器冰川表面分类","authors":"Gunjan Joshi;Celia A. Baumhoer;Andreas J. Dietz;Ryo Natsuaki;Akira Hirose","doi":"10.1109/JSTARS.2024.3454789","DOIUrl":null,"url":null,"abstract":"Mapping snow cover at the end of the ablation season allows us to extract the snow line altitude (SLA). The SLA is an important proxy for the equilibrium line altitude of a glacier and an indicator of glacier health. With the increase in both active and passive remote sensing satellites, the accuracy and effectiveness of glacier monitoring can be enhanced, as the two sensors offer complementary information. In this article, we focus on the combination of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to perform glacial classification using an explainable neural network and thereafter determine SLA. In addition, confidence-aware inverse mapping dynamics is used to understand the result reliability and the individual sensor contributions. The proposed method is applied to the Great Aletsch Glacier in the European Alps, where an overall accuracy of 83% is observed compared to the ground truth data. We observe the glacier from 2015 to 2023, noting a retreat of the SLA to higher elevations by 36 to 133 m depending on the region. Apart from climate-related mass loss, the European Alps are also affected by dust deposited during Sahara dust events and contamination from algae. Thus, in this work, we assess the annual presence of contaminated snow on the glacier. The inverse mapping dynamics reveals the contributions of both SAR and optical sensor data in the classification. This multisensor approach is shown to mitigate the limitations of single-source data, providing a comprehensive understanding of glacier dynamics in the context of climate change.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666818","citationCount":"0","resultStr":"{\"title\":\"Multisensor Glacier Surface Classification Using Confidence-Aware Explainable Inverse-Mapping Neural Network\",\"authors\":\"Gunjan Joshi;Celia A. Baumhoer;Andreas J. Dietz;Ryo Natsuaki;Akira Hirose\",\"doi\":\"10.1109/JSTARS.2024.3454789\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mapping snow cover at the end of the ablation season allows us to extract the snow line altitude (SLA). The SLA is an important proxy for the equilibrium line altitude of a glacier and an indicator of glacier health. With the increase in both active and passive remote sensing satellites, the accuracy and effectiveness of glacier monitoring can be enhanced, as the two sensors offer complementary information. In this article, we focus on the combination of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to perform glacial classification using an explainable neural network and thereafter determine SLA. In addition, confidence-aware inverse mapping dynamics is used to understand the result reliability and the individual sensor contributions. The proposed method is applied to the Great Aletsch Glacier in the European Alps, where an overall accuracy of 83% is observed compared to the ground truth data. We observe the glacier from 2015 to 2023, noting a retreat of the SLA to higher elevations by 36 to 133 m depending on the region. Apart from climate-related mass loss, the European Alps are also affected by dust deposited during Sahara dust events and contamination from algae. Thus, in this work, we assess the annual presence of contaminated snow on the glacier. The inverse mapping dynamics reveals the contributions of both SAR and optical sensor data in the classification. This multisensor approach is shown to mitigate the limitations of single-source data, providing a comprehensive understanding of glacier dynamics in the context of climate change.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666818\",\"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/10666818/\",\"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/10666818/","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}
Multisensor Glacier Surface Classification Using Confidence-Aware Explainable Inverse-Mapping Neural Network
Mapping snow cover at the end of the ablation season allows us to extract the snow line altitude (SLA). The SLA is an important proxy for the equilibrium line altitude of a glacier and an indicator of glacier health. With the increase in both active and passive remote sensing satellites, the accuracy and effectiveness of glacier monitoring can be enhanced, as the two sensors offer complementary information. In this article, we focus on the combination of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to perform glacial classification using an explainable neural network and thereafter determine SLA. In addition, confidence-aware inverse mapping dynamics is used to understand the result reliability and the individual sensor contributions. The proposed method is applied to the Great Aletsch Glacier in the European Alps, where an overall accuracy of 83% is observed compared to the ground truth data. We observe the glacier from 2015 to 2023, noting a retreat of the SLA to higher elevations by 36 to 133 m depending on the region. Apart from climate-related mass loss, the European Alps are also affected by dust deposited during Sahara dust events and contamination from algae. Thus, in this work, we assess the annual presence of contaminated snow on the glacier. The inverse mapping dynamics reveals the contributions of both SAR and optical sensor data in the classification. This multisensor approach is shown to mitigate the limitations of single-source data, providing a comprehensive understanding of glacier dynamics in the context of climate change.
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.