Remote Sensing Letters最新文献_第10页

A novel spatio-temporal fusion approach combining deep learning downscaling and FSDAF method 一种结合深度学习降尺度和FSDAF方法的时空融合新方法

IF 2.3 4区地球科学

Remote Sensing Letters Pub Date : 2023-11-27 DOI: 10.1080/2150704x.2023.2288068

Dunyue Cui, Zhichao Chen, Shidong Wang

引用次数: 0

Fusion of monocular height maps for 3D urban scene reconstruction from uncalibrated satellite images 基于未校准卫星图像的三维城市场景重建的单眼高度图融合

IF 2.3 4区地球科学

Remote Sensing Letters Pub Date : 2023-11-23 DOI: 10.1080/2150704x.2023.2283901

Soon-Yong Park, Chang-Min Son, DongUk Seo, Seung-Hae Baek

引用次数: 0

Nitrogen fertilization assessment in maize (Zea mays L.) using hyperspectral UV/VIS/NIR data 基于UV/VIS/NIR高光谱数据的玉米氮肥评价

IF 2.3 4区地球科学

Remote Sensing Letters Pub Date : 2023-11-22 DOI: 10.1080/2150704x.2023.2282400

Katarzyna Kubiak, Jan Kotlarz, Marcin Spiralski, Jakub Szymański

引用次数: 0

Peace-Athabasca Delta water surface elevations and slopes mapped from AirSWOT Ka-band InSAR 由AirSWOT ka波段InSAR绘制的和平-阿萨巴斯卡三角洲水面高程和坡度图

IF 2.3 4区地球科学

Remote Sensing Letters Pub Date : 2023-11-22 DOI: 10.1080/2150704x.2023.2280464

Laurence C. Smith, Jessica V. Fayne, Bo Wang, Ethan D. Kyzivat, Colin J. Gleason, Merritt E. Harlan, Theodore Langhorst, Dongmei Feng, Tamlin M. Pavelsky, Daniel L. Peters

引用次数: 0

An operational split-window algorithm for retrieving land surface temperature from FengYun-4A AGRI data 从风云- 4a AGRI数据中提取地表温度的可操作分窗算法

IF 2.3 4区地球科学

Remote Sensing Letters Pub Date : 2023-11-16 DOI: 10.1080/2150704x.2023.2282402

Xiangchen Meng, Weihan Liu, Jie Cheng, Hao Guo

引用次数: 0

A sparse representation and Cauchy distance combination graph for hyperspectral target detection 用于高光谱目标探测的稀疏表示和考奇距离组合图

IF 2.3 4区地球科学

Remote Sensing Letters Pub Date : 2023-11-02 DOI: 10.1080/2150704X.2023.2282399

Xiaobin Zhao, Mengmeng Zhang, Wei Li, Kun Gao, Ran Tao

引用次数: 0

A shaped collaborative representation-based detector for hyperspectral anomaly detection 基于形状协同表示的高光谱异常检测

4区地球科学

Remote Sensing Letters Pub Date : 2023-11-02 DOI: 10.1080/2150704x.2023.2275549

Maryam Imani

{"title":"A shaped collaborative representation-based detector for hyperspectral anomaly detection","authors":"Maryam Imani","doi":"10.1080/2150704x.2023.2275549","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2275549","url":null,"abstract":"ABSTRACTA modified version of the collaborative representation-based detector (CRD) is proposed for hyperspectral anomaly detection. In contrast to the conventional CRD, which uses a rectangular dual window, the shaped CRD (SCRD) selects the most appropriate neighbours from the dual window and discards the inappropriate ones. To this end, similarity of the neighbouring pixels to the centre is computed based on the cosine distance to utilize the local information. In addition, the low/high occurrence probability of anomalies/background exhibited in the histogram of the whole image is utilized as global information to find the closest neighbours to the background. The shaped dual window is used for linear approximation of pixels through the collaborative representation. SCRD improves the anomaly detection results with respect to some related works. Experiments on two hyperspectral images show that SCRD results in more accurate detection maps with a bit higher running time compared to CRD.KEYWORDS: collaborative representationdual windowhyperspectral imageanomaly detection Disclosure statementNo potential conflict of interest was reported by the author.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135975580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-view SAR target recognition method based on adaptive weighted decision fusion 基于自适应加权决策融合的多视点SAR目标识别方法

4区地球科学

Remote Sensing Letters Pub Date : 2023-11-02 DOI: 10.1080/2150704x.2023.2277157

Tingwei Zhang

引用次数: 0

An ai approach to ensuring consistency of albedo products from COMS/MI and GK-2A/AMI 确保COMS/MI和GK-2A/AMI反照率产品一致性的人工智能方法

4区地球科学

Remote Sensing Letters Pub Date : 2023-11-02 DOI: 10.1080/2150704x.2023.2277155

Jongho Woo, Noh-Hun Seong, Daeseong Jung, Suyoung Sim, Nayeon Kim, Sungwoo Park, Sungwon Choi, Eunha Sohn, Ki-Hong Park, Kyung-Soo Han

{"title":"An ai approach to ensuring consistency of albedo products from COMS/MI and GK-2A/AMI","authors":"Jongho Woo, Noh-Hun Seong, Daeseong Jung, Suyoung Sim, Nayeon Kim, Sungwoo Park, Sungwon Choi, Eunha Sohn, Ki-Hong Park, Kyung-Soo Han","doi":"10.1080/2150704x.2023.2277155","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2277155","url":null,"abstract":"ABSTRACTSatellite-based surface albedo data are widely used to monitor and analyse the global climate and environmental changes. Korea continuously retrieves surface albedo from the Communication, Ocean and Meteorological Satellite (COMS)/Meteorological Imager sensor (MI) and GEO-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager sensor (AMI). However, the quality of these surface albedo outputs differs due to differences in the algorithms, input data and resolution, which limits their long-term use as climate data. By analyzing errors in the surface albedo data from COMS/MI and GK-2A/AMI and applying corrections, continuous climate monitoring can be enhanced. This study developed a correction model based on machine learning using multiple linear regression (MLR), random forest (RF) and deep neural network (DNN) models to consider the albedo data error characteristics of each satellite. The best performing RF model was used for correction. The errors of the corrected RF COMS/MI data were reduced; when validated with in-situ data, the Root Mean Square Error (RMSE) of the COMS/MI improved from 0.056 to 0.023, similar to the RMSE of 0.019 of GK-2A/AMI. It also showed stability in the time series validation with GLASS satellite data, with a consistent mean RMSE of 0.036.KEYWORDS: Surface AlbedoGK-2A/AMICOMS/MIAERONETGLASSCorrectionMachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: (http://www.textcheck.com/certificate/iFW2k3)Additional informationFundingThis work was funded by the Korea Meteorological Administration’s Research and Development Program “Technical Development on Weather Forecast Support and Convergence Service using Meteorological Satellites” under Grant (KMA2020-00120).","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135975020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying morphological hotspots in large rivers by optimizing image enhancement 基于优化图像增强的大型河流形态热点识别

4区地球科学

Remote Sensing Letters Pub Date : 2023-11-02 DOI: 10.1080/2150704x.2023.2275550

Muhammad Usman, Ahmad Ali Gul, Sawaid Abbas, Umair Rabbani, Syed Muhammad Irteza

{"title":"Identifying morphological hotspots in large rivers by optimizing image enhancement","authors":"Muhammad Usman, Ahmad Ali Gul, Sawaid Abbas, Umair Rabbani, Syed Muhammad Irteza","doi":"10.1080/2150704x.2023.2275550","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2275550","url":null,"abstract":"ABSTRACTLimited access to observe in-situ sediment changes requires viable means for quantifying sediment transport in large rivers for effective management of changes in river channels. This study developed a remote sensing-based framework to identify erosion hotspots by magnifying sediment concentration from Sentinel-2 and Landsat-8/9 multispectral images of the Brahmaputra River and the Indus River. First, uncorrelated independent bands were produced to boost the spectral information using the Principal Component Analysis (PCA). The optimal band composite was then identified by applying the Optimum Index Factor (OIF) on the Principal Components (PCs). This approach determined a 3-PCs composite having the highest variance with the least correlation to highlight active morphological changes during flood times. The results of the study reaffirm the significance of the minor PCs (PC4, PC5 and PC6) to characterize the small variation in the data, whereas the main PCs depict the majority of the brightness values around means. The approach was applied to Sentinel-2 imagery acquired on September 2018 in the Brahmaputra River, and Landsat-8/9 images of 2015 and 2022 in the Indus River during flood time to enhance and identify active riverbank erosion hotspots. Precise and timely monitoring of erosion-prone areas can support the control of riverbank erosion and improve soil conservation practices. AcknowledgmentsThe authors are grateful for the contributions of Prof. Dr Atsuhiro Yorozuya, Mr Hiroshi Koseki, Prof. Dr Shoji Okada and Dr Tanjir Ahmed for the turbidity measurements carried out in the Brahmaputra River in 2018 which have been used in this study. This research was supported in part by a grant (University Research Projects Grants F.Y. 2021-22, 2022-23) from the University of the Punjab, Lahore, Pakistan.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135875380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0