Pub Date : 2024-03-01DOI: 10.14358/pers.23-00071r2
J. Dolloff, Henry Theiss, Brian Bollin
This article presents recommendations and corresponding detailed procedures for the assessment of a geolocation system's accuracy, as well as the specification of accuracy requirements and their subsequent validation when they are available. Applicable metrics and related processing are based on samples of corresponding geolocation errors. This article also presents similar recommendations for the predicted accuracy of a geolocation system, based on samples of geolocation error, as well as corresponding predicted error covariance matrices associated with the geolocations. Reliable error covariance matrices enable optimal use of a geolocation system's products, such as the optimal fusion of multiple geolocations or multiple products for higher confidence and increased accuracy. The recommendations presented in this article enable reliable estimates of accuracy and reliable predicted accuracies, both of which are critical to many geolocation-based applications. The recommendations associated with predicted accuracy are also relatively new and innovative.
{"title":"Assessment, Specification, and Validation of a Geolocation System's Accuracy and Predicted Accuracy","authors":"J. Dolloff, Henry Theiss, Brian Bollin","doi":"10.14358/pers.23-00071r2","DOIUrl":"https://doi.org/10.14358/pers.23-00071r2","url":null,"abstract":"This article presents recommendations and corresponding detailed procedures for the assessment of a geolocation system's accuracy, as well as the specification of accuracy requirements and their subsequent validation when they are available. Applicable metrics and related processing\u0000 are based on samples of corresponding geolocation errors. This article also presents similar recommendations for the predicted accuracy of a geolocation system, based on samples of geolocation error, as well as corresponding predicted error covariance matrices associated with the geolocations.\u0000 Reliable error covariance matrices enable optimal use of a geolocation system's products, such as the optimal fusion of multiple geolocations or multiple products for higher confidence and increased accuracy. The recommendations presented in this article enable reliable estimates of accuracy\u0000 and reliable predicted accuracies, both of which are critical to many geolocation-based applications. The recommendations associated with predicted accuracy are also relatively new and innovative.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":" February","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092693","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}
{"title":"GIS Tips &Tricks","authors":"Shira A. Ellenson, Alma M. Karlin","doi":"10.14358/pers.90.3.133","DOIUrl":"https://doi.org/10.14358/pers.90.3.133","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087450","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}
{"title":"Introduction to Pointcloudmetry by Mathias Lemmens","authors":"Toby M. Terpstra","doi":"10.14358/pers.90.2.81","DOIUrl":"https://doi.org/10.14358/pers.90.2.81","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"286 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139812539","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}
Pub Date : 2024-02-01DOI: 10.14358/pers.23-00052r2
Toshihiro Sakamoto
In terms of contribution to global food security, this study aimed to build a crop monitoring system for within-season yield prediction of US corn and soybeans by using the Moderate Resolution Imaging Spectroradiometer (time-series data, which consists of three essential core algorithms (crop phenology detection, early crop classification, and crop yield prediction methods)). Within-season predictions for 2018–2022 were then made to evaluate the perfor- mance of the proposed system by comparing it with the United States Department of Agriculture's (USDA's) monthly forecasts and the fixed statistical data. The absolute percentage errors of the proposed system for predicting national-level yield and production were less than 5% for all simulation years as of day of year (DOY) 279. The prediction accuracy as of DOY 247 and DOY 279 were comparable to the USDA's forecasts. The proposed system would enable us to make a comprehensive understanding about overview of US corn and soybean crop condition by visualizing detail spatial pattern of good- or poor harvest regions on a within-season basis.
{"title":"Crop Monitoring System Using MODIS Time-Series Data for Within-Season Prediction of Yield and Production of US Corn and Soybeans","authors":"Toshihiro Sakamoto","doi":"10.14358/pers.23-00052r2","DOIUrl":"https://doi.org/10.14358/pers.23-00052r2","url":null,"abstract":"In terms of contribution to global food security, this study aimed to build a crop monitoring system for within-season yield prediction of US corn and soybeans by using the Moderate Resolution Imaging Spectroradiometer (time-series data, which consists of three essential core algorithms\u0000 (crop phenology detection, early crop classification, and crop yield prediction methods)). Within-season predictions for 2018–2022 were then made to evaluate the perfor- mance of the proposed system by comparing it with the United States Department of Agriculture's (USDA's) monthly forecasts\u0000 and the fixed statistical data. The absolute percentage errors of the proposed system for predicting national-level yield and production were less than 5% for all simulation years as of day of year (DOY) 279. The prediction accuracy as of DOY 247 and DOY 279 were comparable to the USDA's forecasts.\u0000 The proposed system would enable us to make a comprehensive understanding about overview of US corn and soybean crop condition by visualizing detail spatial pattern of good- or poor harvest regions on a within-season basis.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"119 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139684710","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}
Pub Date : 2024-02-01DOI: 10.14358/pers.23-00067r2
Yuxuan Zhu, Erzhu Li, Zhigang Su, Wei Liu, A. Samat, Yu Liu
Few-shot scene classification methods aim to obtain classification discriminative ability from few labeled samples and has recently seen substantial advancements. However, the current few-shot learning approaches still suffer from overfitting due to the scarcity of labeled samples. To this end, a few-shot semi-supervised method is proposed to address this issue. Specifically, semi-supervised learning method is used to increase target domain samples; then we train multiple classification models using the augmented samples. Finally, we perform decision fusion of the results obtained from the multiple models to accomplish the image classification task. According to the experiments conducted on two real few-shot remote sensing scene datasets, our proposed method achieves significantly higher accuracy (approximately 1.70% to 4.33%) compared to existing counterparts.
{"title":"A Few-Shot Semi-Supervised Learning Method for Remote Sensing Image Scene Classification","authors":"Yuxuan Zhu, Erzhu Li, Zhigang Su, Wei Liu, A. Samat, Yu Liu","doi":"10.14358/pers.23-00067r2","DOIUrl":"https://doi.org/10.14358/pers.23-00067r2","url":null,"abstract":"Few-shot scene classification methods aim to obtain classification discriminative ability from few labeled samples and has recently seen substantial advancements. However, the current few-shot learning approaches still suffer from overfitting due to the scarcity of labeled samples.\u0000 To this end, a few-shot semi-supervised method is proposed to address this issue. Specifically, semi-supervised learning method is used to increase target domain samples; then we train multiple classification models using the augmented samples. Finally, we perform decision fusion of the results\u0000 obtained from the multiple models to accomplish the image classification task. According to the experiments conducted on two real few-shot remote sensing scene datasets, our proposed method achieves significantly higher accuracy (approximately 1.70% to 4.33%) compared to existing counterparts.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"15 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139817374","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}
Pub Date : 2024-02-01DOI: 10.14358/pers.23-00040r2
Aylin Tuzcu Kokal, Meltem Kaçıkoç, N. Musaoğlu, Aysegul Tanik
The advancements in space technology have facilitated water quality (WQ) monitoring of lake conditions at a spatial resolution of 10 m by freely accessible Sentinel-2 images. The main aim of this article was to elucidate the necessity of spatiotemporal WQ monitoring of the shrinking Lake Burdur in Türkiye by examining the relation between field and satellite data with a state-of-the-art machine learning- based regression algorithm. This study focuses on detection of algal blooms and WQ parameters, which are chlorophyll-a (Chl-a) and suspended solids (SS). Furthermore, this study leverages the advantage of geographic position of Lake Burdur, located at the overlap of two Sentinel-2 frames, which enables the acquisition of satellite images at a temporal resolution of 2–3 days. The findings enrich the understanding of the lake's dynamic structure by rapidly monitoring the occurrence of algal blooms. High accuracies were achieved for Chl-a (R-squared: 0.93) and SS (R-squared: 0.94) detection.
{"title":"Remote Sensing Application in Water Quality of Lake Burdur, Türkiye","authors":"Aylin Tuzcu Kokal, Meltem Kaçıkoç, N. Musaoğlu, Aysegul Tanik","doi":"10.14358/pers.23-00040r2","DOIUrl":"https://doi.org/10.14358/pers.23-00040r2","url":null,"abstract":"The advancements in space technology have facilitated water quality (WQ) monitoring of lake conditions at a spatial resolution of 10 m by freely accessible Sentinel-2 images. The main aim of this article was to elucidate the necessity of spatiotemporal WQ monitoring of the shrinking\u0000 Lake Burdur in Türkiye by examining the relation between field and satellite data with a state-of-the-art machine learning- based regression algorithm. This study focuses on detection of algal blooms and WQ parameters, which are chlorophyll-a (Chl-a) and suspended solids (SS). Furthermore,\u0000 this study leverages the advantage of geographic position of Lake Burdur, located at the overlap of two Sentinel-2 frames, which enables the acquisition of satellite images at a temporal resolution of 2–3 days. The findings enrich the understanding of the lake's dynamic structure by\u0000 rapidly monitoring the occurrence of algal blooms. High accuracies were achieved for Chl-a (R-squared: 0.93) and SS (R-squared: 0.94) detection.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"61 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139823892","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}
Robert Ryerson, Brian Huberty, Lauren McKinney-Wise, Hamdy Elsayed
{"title":"Sector Insight.org– The Value of Membership in the American Society for Photogrammetry and Remote Sensing","authors":"Robert Ryerson, Brian Huberty, Lauren McKinney-Wise, Hamdy Elsayed","doi":"10.14358/pers.90.2.79","DOIUrl":"https://doi.org/10.14358/pers.90.2.79","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"14 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139885950","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}
Robert Ryerson, Brian Huberty, Lauren McKinney-Wise, Hamdy Elsayed
{"title":"Sector Insight.org– The Value of Membership in the American Society for Photogrammetry and Remote Sensing","authors":"Robert Ryerson, Brian Huberty, Lauren McKinney-Wise, Hamdy Elsayed","doi":"10.14358/pers.90.2.79","DOIUrl":"https://doi.org/10.14358/pers.90.2.79","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"280 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139826028","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}
Pub Date : 2024-02-01DOI: 10.14358/pers.23-00067r2
Yuxuan Zhu, Erzhu Li, Zhigang Su, Wei Liu, A. Samat, Yu Liu
Few-shot scene classification methods aim to obtain classification discriminative ability from few labeled samples and has recently seen substantial advancements. However, the current few-shot learning approaches still suffer from overfitting due to the scarcity of labeled samples. To this end, a few-shot semi-supervised method is proposed to address this issue. Specifically, semi-supervised learning method is used to increase target domain samples; then we train multiple classification models using the augmented samples. Finally, we perform decision fusion of the results obtained from the multiple models to accomplish the image classification task. According to the experiments conducted on two real few-shot remote sensing scene datasets, our proposed method achieves significantly higher accuracy (approximately 1.70% to 4.33%) compared to existing counterparts.
{"title":"A Few-Shot Semi-Supervised Learning Method for Remote Sensing Image Scene Classification","authors":"Yuxuan Zhu, Erzhu Li, Zhigang Su, Wei Liu, A. Samat, Yu Liu","doi":"10.14358/pers.23-00067r2","DOIUrl":"https://doi.org/10.14358/pers.23-00067r2","url":null,"abstract":"Few-shot scene classification methods aim to obtain classification discriminative ability from few labeled samples and has recently seen substantial advancements. However, the current few-shot learning approaches still suffer from overfitting due to the scarcity of labeled samples.\u0000 To this end, a few-shot semi-supervised method is proposed to address this issue. Specifically, semi-supervised learning method is used to increase target domain samples; then we train multiple classification models using the augmented samples. Finally, we perform decision fusion of the results\u0000 obtained from the multiple models to accomplish the image classification task. According to the experiments conducted on two real few-shot remote sensing scene datasets, our proposed method achieves significantly higher accuracy (approximately 1.70% to 4.33%) compared to existing counterparts.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"315 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139876950","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}
Pub Date : 2024-02-01DOI: 10.14358/pers.23-00054r2
P. Dynowski, A. Źróbek-Sokolnik, Marta Czaplicka, Adam Senetra
The aim of the study is to identify factors affecting the sight-aesthetic value of the underwater landscapes of lakes for the purposes of exploration tourism. The reason for undertaking this topic is the lack of such studies for inland water bodies. The results will contribute to expanding and supplementing the knowledge on the assessment of the sight-aesthetic attractiveness of landscapes and fill gaps in knowledge about the underwater landscapes of lakes. The questionnaire survey implemented the direct comparison method described by Kendall (Kendall, M. G. 1970. Rank Correlation Methods. Charles Griffin and Co: Glasgow, Scotland). According to respondents, animals and submerged anthropogenic elements are the most visually attractive in an aquatic environment The results obtained are the reason for conducting further research and developing the methodology for the assessment of the sight-aesthetic value of inland bodies of water based on the experience of terrestrial landscape researchers.
本研究的目的是确定影响湖泊水下景观视觉美学价值的因素，以便开展探险旅游。开展这一课题的原因是缺乏对内陆水体的此类研究。研究结果将有助于扩展和补充景观视听吸引力评估方面的知识，并填补湖泊水下景观方面的知识空白。问卷调查采用了肯德尔（Kendall, M. G. 1970.Rank Correlation Methods.Charles Griffin and Co：苏格兰格拉斯哥）。受访者认为，在水生环境中，动物和水下人为元素最具视觉吸引力。所获得的结果是开展进一步研究并根据陆地景观研究人员的经验制定内陆水体视 觉美学价值评估方法的原因。
{"title":"The Sight-Aesthetic Value of the Underwater Landscapes of Lakes in the Context of Exploration Tourism","authors":"P. Dynowski, A. Źróbek-Sokolnik, Marta Czaplicka, Adam Senetra","doi":"10.14358/pers.23-00054r2","DOIUrl":"https://doi.org/10.14358/pers.23-00054r2","url":null,"abstract":"The aim of the study is to identify factors affecting the sight-aesthetic value of the underwater landscapes of lakes for the purposes of exploration tourism. The reason for undertaking this topic is the lack of such studies for inland water bodies. The results will contribute to expanding\u0000 and supplementing the knowledge on the assessment of the sight-aesthetic attractiveness of landscapes and fill gaps in knowledge about the underwater landscapes of lakes. The questionnaire survey implemented the direct comparison method described by Kendall (Kendall, M. G. 1970. Rank Correlation\u0000 Methods. Charles Griffin and Co: Glasgow, Scotland). According to respondents, animals and submerged anthropogenic elements are the most visually attractive in an aquatic environment The results obtained are the reason for conducting further research and developing the methodology for\u0000 the assessment of the sight-aesthetic value of inland bodies of water based on the experience of terrestrial landscape researchers.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139885148","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}
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