IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium最新文献_第9页

Potential of Quad-Polarimetric SAR Data in Identifying Flat Areas Over Natural Geological Surfaces 四极化SAR数据在自然地质表面上识别平坦区域的潜力

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884538

Yao Gao, W. Hou, Xiuqing Liu, Yonghui Han, Chunle Wang, Robert Wang

引用次数: 0

Laplacian Regularized Spatial-Aware Collaborative Competitive Representation for Hyperspectral Dimensionality Reduction 高光谱降维的拉普拉斯正则化空间感知协同竞争表示

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883385

Chiranjibi Shah, Q. Du

引用次数: 0

SAR Focusing of Mobile Apres Surveys 移动Apres调查SAR聚焦

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883784

Sarina Kapai, D. Schroeder, A. Broome, T. J. Young, C. Stewart

引用次数: 0

Using Drone-Based Remote Sensing Products to Detect Land Surface Conditions in Drylands 利用无人机遥感产品探测旱地地表状况

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884804

Junzhe Zhang

引用次数: 0

SAR2SAR Denoise Method on Land Use and Land Cover in Malaysia Using Sentinel-1 Imagery 基于Sentinel-1影像的马来西亚土地利用和土地覆盖SAR2SAR降噪方法

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883760

Muhammad Azzam A. Wahab, Md Nazri Safar, S. Hashim

{"title":"SAR2SAR Denoise Method on Land Use and Land Cover in Malaysia Using Sentinel-1 Imagery","authors":"Muhammad Azzam A. Wahab, Md Nazri Safar, S. Hashim","doi":"10.1109/IGARSS46834.2022.9883760","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883760","url":null,"abstract":"Although synthetic aperture radar (SAR) images are often regarded as greyscale images, special care must be exercised when interpreting the images for land use and land cover (LULC). SAR has long been regarded as a viable alternative to optical images because it interacts with ground features in a variety of ways and is less influenced by weather conditions. Unlike optical images, SAR images suffer from speckle noise; therefore, accurate LULC mapping with SAR data is important. In this work, the recently proposed SAR2SAR denoise method has been employed. The self-supervision method is based on a deep learning model that can generate a speckle- free image with few references in a short amount of time. The proposed method has been applied to evaluate five categories of L ULC in Malaysia with ground range detected (GRD) Sentinel-l data: dense forests, paddy fields, urban areas, cleared lands, and water bodies. The results showed that the use of false-color-based denoised VV and VH composites from SAR2SAR denoise method significantly improved the visualizations of LULC classes as much as optical imagery.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123368832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bowie-M: Combining Heritage Operational Performance with Hyperspectral Capability 鲍伊- m:结合传统作战性能和高光谱能力

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883794

Sean Geiger, T. Pett

{"title":"Bowie-M: Combining Heritage Operational Performance with Hyperspectral Capability","authors":"Sean Geiger, T. Pett","doi":"10.1109/IGARSS46834.2022.9883794","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883794","url":null,"abstract":"The Ball Operational Weather Instrument Evolution - Microwave (BOWIE-M) is the first in the series of low size, weight and power (SWaP), remote sensing instruments designed by Ball Aerospace to satisfy NOAA's next generation weather architecture requirements. BOWIE-M is a total power microwave radiometer, optimized for atmospheric temperature and humidity sounding, and designed into a compact, low cost configuration compatible with SmallSat integration and rideshare volume constraints. BOWIE-M operates in a cross-track scanning mode using a single lightweight spinning reflector antenna and produces channels across the 24 - 183 GHz range. Retrievals of atmospheric temperature profile are enabled by channels in the 50 - 60 GHz V-band. Moisture profiles are derived from measurements near the 183.3 and 22 GHz water resonances. Instrument calibration is performed through the antenna aperture via measurements of cold space and of an internal blackbody target during each antenna rotation. The BOWIE-M design has been refined over several years in response to changing radiometric measurement needs in the weather community, as well responding to the results of several analyses performed to identify instrument architecture, orbital parameters, and channel characteristics to support those needs. In addition to many analog-derived channel measurements, the presence of a digital receiver in BOWIE-M is leveraged to provide hyperspectral narrowband channel measurement in the V-band region, allowing humidity measurements up to 0.01 mbar.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"72 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120843848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover 人类对于Sentinel-2的土地覆盖来说是很差的分类器

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884691

M. Rußwurm, Sherrie Wang, D. Tuia

{"title":"Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover","authors":"M. Rußwurm, Sherrie Wang, D. Tuia","doi":"10.1109/IGARSS46834.2022.9884691","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884691","url":null,"abstract":"Learning to predict accurately from a few data samples is a central challenge in modern data-hungry machine learning. On natural images, human vision typically outperforms deep learning approaches on few-shot learning. However, we hypothesize that aerial and satellite images are more challenging to the human eye. This applies particularly when the image resolution is comparatively low, as with the 10m ground sampling distance of Sentinel-2. In this study, we benchmark model-agnostic meta-learning (MAML) algorithms against human participants on few-shot land cover classification with Sentinel-2 imagery on the Sen12MS dataset. We find that categorization of land cover from globally distributed regions is a difficult task for the participants, who classified the given images less accurately than the MAML-trained model and with a highly variable success rate. This suggest that hand-labeling land cover directly on Sentinel-2 imagery is not optimal when tackling a new land cover classification problem. Labeling only a few images and employing a trained meta-learning model to this task may lead to more accurate and consistent solutions compared to hand labeling by multiple individuals.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121141665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Anechoic Chamber Polinsar Measurements of Rice Canopy 水稻冠层消声室偏振测量

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884500

Kun Li, Y. Shao, Jinning Wang, Zhiyong Wang, Xianyu Guo, Xiangcheng Liu, Xiulai Xiao, Zhiqu Liu, Xuexiao Wu, E. Hailin

引用次数: 0

Multi-Scale Context-Aware R-Cnn for Few-Shot Object Detection in Remote Sensing Images 基于多尺度上下文感知的R-Cnn遥感图像小目标检测

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883807

Haozheng Su, Yanan You, Gang Meng

引用次数: 2

The phase-only null beamforming synthesis via manifold optimization 基于流形优化的纯相位零波束形成合成

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883605

Yang Cong, Jinfeng Hu, Kai Zhong, Jie Wu

引用次数: 0