Pub Date : 2024-09-05DOI: 10.1109/lgrs.2024.3454629
Yuheng Yuan, Xuehong Chen, Kai Tang, Jin Chen
{"title":"A “Difference In Difference” based method for unsupervised change detection in season-varying images","authors":"Yuheng Yuan, Xuehong Chen, Kai Tang, Jin Chen","doi":"10.1109/lgrs.2024.3454629","DOIUrl":"https://doi.org/10.1109/lgrs.2024.3454629","url":null,"abstract":"","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"3 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.1109/lgrs.2024.3386311
Yueli Ding, Haojie Xu, Di Wang, Ke Li, Yumin Tian
{"title":"Visual Selection and Multi-Stage Reasoning for RSVG","authors":"Yueli Ding, Haojie Xu, Di Wang, Ke Li, Yumin Tian","doi":"10.1109/lgrs.2024.3386311","DOIUrl":"https://doi.org/10.1109/lgrs.2024.3386311","url":null,"abstract":"","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"206 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.1109/lgrs.2024.3385999
Qichang Guo, Xingdong Liang, Yanlei Li, Yujie Dai
{"title":"A Novel Method for Airborne Array-InSAR Tomography Based on Off-grid Target Modeling and Group Sparsity","authors":"Qichang Guo, Xingdong Liang, Yanlei Li, Yujie Dai","doi":"10.1109/lgrs.2024.3385999","DOIUrl":"https://doi.org/10.1109/lgrs.2024.3385999","url":null,"abstract":"","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"117 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01eCollection Date: 2023-01-01DOI: 10.3897/BDJ.11.e100942
Lucas Lamelas-López, Paulo A V Borges, Elisa Tarantino, Maria Manuela Juliano, Jose Carlos Fontes, Cristina Moules, Ricardo Rodrigues, Jessica Machado, José Adriano Mota, Beatriz Sousa, Helder Amaral, Maria da Conceição Filipe, David H Lopes
Background: The data we present are part of the CUARENTAGRI project, which involves all archipelagos of the Macaronesia (Azores, Madeira, Canary Islands and Cabo Verde). The project aims to: i) identify and evaluate the risks associated with the introduction of new arthropod pests; ii) study the population dynamics of selected arthropod pest species currently responsible for the damage of key target crops and iii) develop monitoring systems, based on prediction and/or population dynamics of the crop pests, creating warnings and a phytosanitary prevention system. In this contribution, we compile data for three Azorean Islands (Terceira, São Jorge and São Miguel Islands), where pheromone-baited traps were placed in pastures, potato fields and several orchards' types (apples, banana, chestnuts, olives, orange and strawberry), during three consecutive years (2020, 2021 and 2022).
New information: A total of 114,827 specimens of insects (Arthropoda, Insecta) were collected, belonging to four orders, six families and ten recorded pest species. A total of eight species are considered introduced (Cosmopolitessordidus (Germar, 1824), Drosophilasuzukii (Matsumura, 1931), Bactroceraoleae (Rossi, 1790), Ceratitiscapitata (Wiedemann, 1824), Phthorimaeaoperculella (Zeller, 1873), Cydiapomonella (Linnaeus, 1758), Cydiasplendana (Hübner, 1799) and Grapholitamolesta (Busck, 1916); n = 84,986 specimens) and two native non-endemic (Mythimnaunipuncta (Haworth, 1809) and Spodopteralittoralis (Boisduval, 1833); n = 17,465 specimens). This study intended to contribute to a better knowledge of the arthropods pests that can affect the Azorean crops and will serve as a baseline for future monitoring actions, pest risk assessments and prevention systems.
{"title":"Monitoring ten insect pests in selected orchards in three Azorean Islands: The project CUARENTAGRI.","authors":"Lucas Lamelas-López, Paulo A V Borges, Elisa Tarantino, Maria Manuela Juliano, Jose Carlos Fontes, Cristina Moules, Ricardo Rodrigues, Jessica Machado, José Adriano Mota, Beatriz Sousa, Helder Amaral, Maria da Conceição Filipe, David H Lopes","doi":"10.3897/BDJ.11.e100942","DOIUrl":"10.3897/BDJ.11.e100942","url":null,"abstract":"<p><strong>Background: </strong>The data we present are part of the CUARENTAGRI project, which involves all archipelagos of the Macaronesia (Azores, Madeira, Canary Islands and Cabo Verde). The project aims to: i) identify and evaluate the risks associated with the introduction of new arthropod pests; ii) study the population dynamics of selected arthropod pest species currently responsible for the damage of key target crops and iii) develop monitoring systems, based on prediction and/or population dynamics of the crop pests, creating warnings and a phytosanitary prevention system. In this contribution, we compile data for three Azorean Islands (Terceira, São Jorge and São Miguel Islands), where pheromone-baited traps were placed in pastures, potato fields and several orchards' types (apples, banana, chestnuts, olives, orange and strawberry), during three consecutive years (2020, 2021 and 2022).</p><p><strong>New information: </strong>A total of 114,827 specimens of insects (Arthropoda, Insecta) were collected, belonging to four orders, six families and ten recorded pest species. A total of eight species are considered introduced (<i>Cosmopolitessordidus</i> (Germar, 1824), <i>Drosophilasuzukii</i> (Matsumura, 1931), <i>Bactroceraoleae</i> (Rossi, 1790), <i>Ceratitiscapitata</i> (Wiedemann, 1824), <i>Phthorimaeaoperculella</i> (Zeller, 1873), <i>Cydiapomonella</i> (Linnaeus, 1758), <i>Cydiasplendana</i> (Hübner, 1799) and <i>Grapholitamolesta</i> (Busck, 1916); n = 84,986 specimens) and two native non-endemic (<i>Mythimnaunipuncta</i> (Haworth, 1809) and <i>Spodopteralittoralis</i> (Boisduval, 1833); n = 17,465 specimens). This study intended to contribute to a better knowledge of the arthropods pests that can affect the Azorean crops and will serve as a baseline for future monitoring actions, pest risk assessments and prevention systems.</p>","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"5 1","pages":"e100942"},"PeriodicalIF":1.3,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10848638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84455484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/LGRS.2023.3270929
Wenxin Xiong, H. So
The inadvertent incorporation of deviating samples into the measured indirect and direct path delays is generally unavoidable in the practical implementation of passive elliptic localization. These outlying observations, however, can do great harm to the positioning performance if left untreated. Here, a robust statistics-based method is put forward as the solution to such a problem. The non-outlier-resistant $ell _{2}$ cost function in the traditional least squares (LS) formulation is replaced by a certain differentiable error measure that possesses resistance to the presence of abnormally large fitting errors. A globally optimized hybrid quasi-Newton and particle swarm optimization (PSO) algorithm is then developed for an efficient realization of the robust estimator. The strong capability of the presented approach to deal with outliers and its applicability to typical adverse localization environments are demonstrated via simulations.
{"title":"Outlier-Robust Passive Elliptic Target Localization","authors":"Wenxin Xiong, H. So","doi":"10.1109/LGRS.2023.3270929","DOIUrl":"https://doi.org/10.1109/LGRS.2023.3270929","url":null,"abstract":"The inadvertent incorporation of deviating samples into the measured indirect and direct path delays is generally unavoidable in the practical implementation of passive elliptic localization. These outlying observations, however, can do great harm to the positioning performance if left untreated. Here, a robust statistics-based method is put forward as the solution to such a problem. The non-outlier-resistant $ell _{2}$ cost function in the traditional least squares (LS) formulation is replaced by a certain differentiable error measure that possesses resistance to the presence of abnormally large fitting errors. A globally optimized hybrid quasi-Newton and particle swarm optimization (PSO) algorithm is then developed for an efficient realization of the robust estimator. The strong capability of the presented approach to deal with outliers and its applicability to typical adverse localization environments are demonstrated via simulations.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"20 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62503900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/LGRS.2023.3268647
Luyi Qiu, Dayu Yu, Chenxiao Zhang, Xiaofeng Zhang
Road extraction from remote sensing (RS) images in very high resolution is important for autonomous driving and road planning. Compared with large-scale objects, roads are smaller, winding, and likely to be covered by buildings’ shadows, causing deep convolutional neural networks (DCNNs) to be difficult to identify roads. The letter proposes a semantics-geometry framework (SGNet) with a two-branch backbone, i.e., semantics-dominant branch and geometry-dominant branch. The semantics-dominant branch inputs images to predict dense semantic features, and the geometry-dominant branch takes images to generate sparse boundary features. Then, dense semantic features and boundary details generated by two branches are adaptively fused. Further, by utilizing affinity between neighborhood pixels, a feature refinement module (FRM) is proposed to refine textures and road details. We evaluate the SGNet on the Ottawa road dataset. Experiments show that the SGNet outperforms other competitors on the road extraction task. Codes is available at https://github.com/qiuluyi/SGNet.
{"title":"A Semantics-Geometry Framework for Road Extraction From Remote Sensing Images","authors":"Luyi Qiu, Dayu Yu, Chenxiao Zhang, Xiaofeng Zhang","doi":"10.1109/LGRS.2023.3268647","DOIUrl":"https://doi.org/10.1109/LGRS.2023.3268647","url":null,"abstract":"Road extraction from remote sensing (RS) images in very high resolution is important for autonomous driving and road planning. Compared with large-scale objects, roads are smaller, winding, and likely to be covered by buildings’ shadows, causing deep convolutional neural networks (DCNNs) to be difficult to identify roads. The letter proposes a semantics-geometry framework (SGNet) with a two-branch backbone, i.e., semantics-dominant branch and geometry-dominant branch. The semantics-dominant branch inputs images to predict dense semantic features, and the geometry-dominant branch takes images to generate sparse boundary features. Then, dense semantic features and boundary details generated by two branches are adaptively fused. Further, by utilizing affinity between neighborhood pixels, a feature refinement module (FRM) is proposed to refine textures and road details. We evaluate the SGNet on the Ottawa road dataset. Experiments show that the SGNet outperforms other competitors on the road extraction task. Codes is available at https://github.com/qiuluyi/SGNet.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"20 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62503715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.23977/geors.2023.060101
In order to correctly understand the characteristics and distribution law of geological hazards in Xindu District, the types of geological hazards are analyzed according to the geological environmental conditions and the development characteristics of geological hazards. In this paper, four types of geological hazards, landslide, collapse, mudslide and ground collapse are analyzed and the characteristics and distribution rules of geological hazards are studied, and it is concluded that the density of geological hazards in the plain area and the middle mountainous area is small, and the distribution density of geological hazards in the low mountainous area is the largest. Human engineering activities are the main triggering factors for the formation of geological hazards, and rainfall is the aggravating factor for geological hazards.
{"title":"Analysis of Geological Hazard Types and Distribution Pattern in Xindu District","authors":"","doi":"10.23977/geors.2023.060101","DOIUrl":"https://doi.org/10.23977/geors.2023.060101","url":null,"abstract":"In order to correctly understand the characteristics and distribution law of geological hazards in Xindu District, the types of geological hazards are analyzed according to the geological environmental conditions and the development characteristics of geological hazards. In this paper, four types of geological hazards, landslide, collapse, mudslide and ground collapse are analyzed and the characteristics and distribution rules of geological hazards are studied, and it is concluded that the density of geological hazards in the plain area and the middle mountainous area is small, and the distribution density of geological hazards in the low mountainous area is the largest. Human engineering activities are the main triggering factors for the formation of geological hazards, and rainfall is the aggravating factor for geological hazards.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135496355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.23977/geors.2023.060102
Since ancient times, human beings have never stopped exploring outer space. As one of the eight planets, Mars has naturally become one of our key research objects. This paper mainly studies the characteristics and influencing factors of the Martian space environment. By using MAVEN's observation data for many years, we found that the intensity of the induced magnetic field is enhanced under the condition of high solar wind pressure, and this phenomenon is explained by the analysis method based on MHD equation.
{"title":"Solar Wind Influences on the Induced Magnetic Field of Mars: MAVEN Observations","authors":"","doi":"10.23977/geors.2023.060102","DOIUrl":"https://doi.org/10.23977/geors.2023.060102","url":null,"abstract":"Since ancient times, human beings have never stopped exploring outer space. As one of the eight planets, Mars has naturally become one of our key research objects. This paper mainly studies the characteristics and influencing factors of the Martian space environment. By using MAVEN's observation data for many years, we found that the intensity of the induced magnetic field is enhanced under the condition of high solar wind pressure, and this phenomenon is explained by the analysis method based on MHD equation.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135596311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1109/LGRS.2023.3277518
Xuefeng Wu, Huixing Zhang, Bing-Shout He
As an effective time–frequency (TF) analysis method, S-transform (ST) has an extensive application in signal processing. However, for broadband seismic signals, the peaks of the frequency distribution in the TF spectrum of ST biases the actual Fourier spectrum. Besides, the TF resolution of ST is affected by the relatively fixed window function and the Heisenberg uncertainty principle. In order to correct the frequency bias and improve the TF resolution of ST, and at the same time, to increase the flexibility of the window function, we propose a new TF analysis method, called the deconvolutive frequency corrected three-parameter ST (DFC-TPST). The DFC-TPST includes two steps: modifying the window function to achieve the frequency-corrected three-parameter ST (FC-TPST) and deconvoluting FC-TPST to achieve DFC-TPST. The synthetic example proves the superiority of the method in characterizing seismic signals. Through comparison of field data testing, we find that the proposed method can be well applied to hydrocarbon detection in tight sandstone reservoirs.
{"title":"Deconvolutive Frequency Corrected Three-Parameter S-Transform and Its Application in Tight Sandstone Reservoir","authors":"Xuefeng Wu, Huixing Zhang, Bing-Shout He","doi":"10.1109/LGRS.2023.3277518","DOIUrl":"https://doi.org/10.1109/LGRS.2023.3277518","url":null,"abstract":"As an effective time–frequency (TF) analysis method, S-transform (ST) has an extensive application in signal processing. However, for broadband seismic signals, the peaks of the frequency distribution in the TF spectrum of ST biases the actual Fourier spectrum. Besides, the TF resolution of ST is affected by the relatively fixed window function and the Heisenberg uncertainty principle. In order to correct the frequency bias and improve the TF resolution of ST, and at the same time, to increase the flexibility of the window function, we propose a new TF analysis method, called the deconvolutive frequency corrected three-parameter ST (DFC-TPST). The DFC-TPST includes two steps: modifying the window function to achieve the frequency-corrected three-parameter ST (FC-TPST) and deconvoluting FC-TPST to achieve DFC-TPST. The synthetic example proves the superiority of the method in characterizing seismic signals. Through comparison of field data testing, we find that the proposed method can be well applied to hydrocarbon detection in tight sandstone reservoirs.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"20 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62505127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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