Remote Sensing Letters最新文献

{"title":"Imbalanced learning of remotely sensed data for bioenergy source identification in a forest in the Wallacea region of Indonesia","authors":"Ibnu F. Kurniawan, Fei He, Iswan Dunggio, Marini S. Hamidun, Zulham Sirajuddin, Muhammad Aziz, A. Taufiq Asyhari","doi":"10.1080/2150704x.2023.2270107","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2270107","url":null,"abstract":"ABSTRACTRemote sensing technologies have been increasingly crucial to support policy-makers in achieving their ecological strategies. The data provided by such technology can estimate the bioenergy source production rate and monitor deforestation. This work participates in the cause by contributing an aerial dataset and developing an intelligent tree-detection system usable for counting trees with the bioenergy potential. Low-altitude flying units have been vastly used for such a purpose due to their ability to capture high-quality data from distant locations. Despite these potentials, collected images that compose a dataset are often characterized by imbalanced distribution among classes. The class disproportion can affect the overall model performance, as it severely deprives key features of under-represented classes. This study proposes data-level approaches that adopt and extend prior sampling algorithms for object detection problems. The devised techniques try to reduce the number of redundant outputs obtained from sampling methods and reduce the iteration required to achieve the target imbalance ratio by employing a systematic flow. In such a process, the class distribution of an original dataset is used as a guideline for selecting candidates for subsequent processes. Our results show that the modified dataset can reduce the length of a training process shown by fewer iterations required to achieve the final metrics of its original dataset version and lower training losses in each iteration. Additionally, the modified dataset can improve the F-score (F1) and precision metric of object detection algorithm by up to 6%.KEYWORDS: Aerial surveillanceUrban forestryRemote monitoringClass imbalancedObject detectionMachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported in part by the British Council COP26 Trilateral Research Initiative grant under the project ”Scaling-up Indonesian Bioenergy Potential through Assessment of Wallacea’s Plant Species: Data-Driven Energy Harvesting and Community-Centred Approach”. Ibnu F. Kurniawan acknowledged the support from the Directorate General of Higher Education, Research, and Technology, Indonesia.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135412695","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}

{"title":"A lightweight skip-connected expansion inception network for remote sensing scene classification","authors":"Aiye Shi, Ziqi Li, Xin Wang","doi":"10.1080/2150704x.2023.2266118","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2266118","url":null,"abstract":"ABSTRACTRemote sensing image (RSI) scene classification is a hot topic in the field of remote sensing and has garnered a lot of attention. The key issue in image classification is effectively understanding semantic content. Convolutional neural networks (CNNs) are generally recognized to significantly improve classification performance due to their powerful feature extraction capabilities. However, the overall structure of the model is complicated and has a large number of parameters, making it difficult to extract more efficient features. To address these problems, in this paper, we propose a lightweight skip-connected expansion Inception network called SEINet. To capture characteristics at a more granular level, we create a new lightweight backbone network with fewer parameters based on the existing network architecture. Additionally, the paper introduces a skip-connected expansion Inception (SEI) module for extracting context-dependent relationships. The ablation experiments verify the effectiveness of our proposed module. Experiment findings on two public datasets demonstrate that our method has advantages in classification accuracy and execution efficiency over state-of-the-art (SOTA) methods.KEYWORDS: Remote sensingscene classificationconvolution neural network (CNN)skip-connected expansion Inception Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695514","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}

{"title":"Semi-empirical models for estimating canopy chlorophyll content: the importance of prior information","authors":"Dong Li, Hengbiao Zheng, Xia Yao, Yan Zhu, Weixing Cao, Tao Cheng","doi":"10.1080/2150704x.2023.2266119","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2266119","url":null,"abstract":"ABSTRACTThe canopy chlorophyll content (CCC) provides valuable information about the crop growth status. CCC can be estimated using remote sensing techniques, such as through the red-edge-based chlorophyll index (CIRE). The empirical model between CCC and CIRE calibrated using the measured dataset lacks generality. Therefore, the semi-empirical model is a better choice, which is calibrated on the physical model simulations. However, the effect of parameter settings of physical models on semi-empirical models is not clear. This study first investigated the effects of dry matter content (LMA) and mesophyll structural coefficient (Ns) on the CCC-CIRE relationships and then evaluated CCC estimation using the CIRE-based semi-empirical model calibrated on simulated datasets with different ranges of LMA and Ns. The results showed that the relationships between CCC and CIRE were sensitive to Ns and LMA. Therefore, after considering the prior information of Ns (1.0–1.5) and LMA (20–80 g m−2) for the crop, the best estimation of CCC was obtained with an R2 of 0.82 and an RMSE of 0.36 g m−2, which were substantially better than the model without considering the prior information (R2 = 0.40 and RMSE = 0.67 g m−2). These findings improved our understanding of CCC estimation using the semi-empirical model and would facilitate the accurate mapping of CCC for agricultural management.KEYWORDS: canopy chlorophyll contentvegetation indexsemi-empirical modelprior information Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data used in this study are available upon request.Additional informationFundingThis work was supported by grants from the National Natural Science Foundation of China (42101360, 32021004), the Jiangsu Funding Program for Excellent Postdoctoral Talent (2022ZB333), the Fellowship of China Postdoctoral Science Foundation (2022M710070), and Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry. We are grateful to the reviewers for their suggestions and comments, which significantly improved the quality of this paper.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135739363","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}

{"title":"Closed-form expressions of <i>P</i> _FA of mean level CFAR detectors for multiple-pulse gamma-distributed radar clutter","authors":"Mohamed Baadeche, Faouzi Soltani","doi":"10.1080/2150704x.2023.2264491","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2264491","url":null,"abstract":"ABSTRACTIn a radar detection system, multiple pulse (MP) transmission is used to improve detection performance compared to the single pulse case by integrating the echoes of pulses at reception. In this paper, we derive closed-form expressions of the probability of false alarm PFA of the cell averaging-constant false alarm rate, greatest-of CFAR, and smallest-of CFAR detectors considering a homogeneous gamma distributed radar clutter applied to the MP case. Expressions are given by analytical formulas for a positive real shape parameter which correspond to a real situation and are validated by comparing them in terms of the detection threshold calculated values T, to the results obtained by means of Monte Carlo simulations.KEYWORDS: Multiple pulsesCA-CFARGO-CFARSO-CFARgamma distributed clutter Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136279775","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}

{"title":"The impact of rice cultivation in green and blue water on the economic productivity of the valley region of Manipur, India","authors":"N Bidyarani Chanu, Bakimchandra Oinam","doi":"10.1080/2150704x.2023.2264494","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2264494","url":null,"abstract":"ABSTRACTRice is a staple food for the vast majority of the world’s population and one of the world’s largest consumers of freshwater. Unfortunately, climate change will further worsen the demand for blue water demand, particularly for rice cultivation needs to be closely monitored. Our study assessed the spatial water footprint (WF) of rice for the valley region of Manipur using Moderate Resolution Imaging Spectro-radiometer Evapotranspiration (MOD16) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS) datasets. In addition, rice’s economic water productivity of green and blue water was evaluated. Results showed an average spatial WF ranging from 772.14 to 1456.23 m3/tonne. According to data comparing the national average, 90% of the valley area has a lower WF value than the country as a whole. The green and blue WF of rice ranges from 596.62 m3/tonne to 673.42 m3/tonne and 65.79 m3/tonne to 767.65 m3/tonne, respectively. The spatial variation of the blue WF is due to the amount of rainfall and irrigation application within the study area. The green economic water productivity is getting lower than the blue economic water productivity due to green water’s lesser economic scarcity than blue water. This study can help plan crop allocation in favour of water availability by water management authorities on economic value calculations.KEYWORDS: MOD16CHIRPSricewater footprint AcknowledgmentsWe thank the Department of Agriculture, Manipur, and the Directorate of Environment and Climate Change, Manipur, for providing crop-related and weather data for running this project. We also thank NASA and CHIRPS for providing the dataset through the respective archives. We will be grateful to the Ministry of Human Resource Development, the Government of India and the National Institute of Technology Manipur for PhD fellowship.Disclosure statementNo potential conflict of interest was reported by the authors.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136341623","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}

{"title":"Barlow twin self-supervised pre-training for remote sensing change detection","authors":"Wenqing Feng, Jihui Tu, Chenhao Sun, Wei Xu","doi":"10.1080/2150704x.2023.2264493","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2264493","url":null,"abstract":"ABSTRACTRemote sensing change detection (CD) methods that rely on supervised deep convolutional neural networks require large-scale labelled data, which is time-consuming and laborious to collect and label, especially for bi-temporal samples containing changed areas. Conversely, acquiring a large volume of unannotated images is relatively easy. Recently, self-supervised contrastive learning has emerged as a promising method for learning from unannotated images, thereby reducing the need for annotation. However, most existing methods employ random values or ImageNet pre-trained models to initialize their encoders and lack prior knowledge tailored to the demands of CD tasks, thus constraining the performance of CD models. To address these challenges, we propose a novel Barlow Twins self-supervised pre-training method for CD (BTSCD), which uses absolute feature differences to directly learn distinct representations associated with changed regions from unlabelled bi-temporal remote sensing images in a self-supervised manner. Experimental results obtained using two publicly available CD datasets demonstrate that our proposed approach exhibits competitive quantitative performance. Moreover, the proposed method achieved final results superior to those of existing state-of-the-art methods. Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grant Nos. 42101358.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136341616","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}

{"title":"Motion error parameter estimation based on vortex echo data","authors":"Xiao Tang, Chenlu Li, Yongxing Du, Ling Qin, Baoshan Li","doi":"10.1080/2150704x.2023.2264492","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2264492","url":null,"abstract":"ABSTRACTThe carrier platform in synthetic aperture radar (SAR) imaging may deviate from the correct trajectory due to end currents in the atmosphere, causing motion errors and ultimately degrading the quality of the radar image. Techniques for motion compensation can reduce the impact of motion errors on the results of the imaging process. Motion error parameters are necessary for motion compensation algorithms. In this paper, the line of sight (LOS) error of the carrier platform is estimated based on the vortex-selected SAR imaging system by analysing the vortex SAR echo data using the relationship between the Bessel magnitude term, the phase term, and the motion error. The validity of the method is verified through numerical simulations.KEYWORDS: Electromagnetic vortexmotion error estimationSAR imaging Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingNational Natural Science Foundation of China (61961033).","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136279602","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}

{"title":"Dempster–Shafer and LSTM based analysis and forecasting of total ozone data","authors":"Rashmi Rekha Devi, Soumya Banerjee, Surajit Chattopadhyay","doi":"10.1080/2150704x.2023.2258466","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2258466","url":null,"abstract":"ABSTRACTTotal ozone time series in a metropolis in India were examined in the current study. The data explored are obtained through Brewer spectrophotometer, which counts photons with a photomultiplier to calculate UV irradiance in the spectrum and measures total ozone when the relative route of photons through the ozone layer (air mass) is 3.5 or less. The total ozone time series’ uncertainty was thoroughly examined using the Dempster–Shafer method, and the association was also depicted using three-dimensional graphs. Finally, the Adam Optimisation Algorithm and the Rectified Linear Unit were used to demonstrate the prediction capability of the single layer Long Short-Term Memory model.KEYWORDS: Total ozoneDempster–Shafer theoryfuzzy setjoint belief measureLSTM Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe World Ozone and Ultraviolet Radiation Data Centre (WOUDC) website was used to collect the observational Total Ozone monthly data for the region of New Delhi, India, for the years 2017 to 2021. The data can be accessed at the following link: https://woudc.org/data/explore.php#.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135967378","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}

{"title":"Water quality indication of spectral probability distribution (SPD): correlation between SPD and Forel-Ule index in closed, connected water bodies","authors":"Zhixuan Zhou, Weining Zhu","doi":"10.1080/2150704x.2023.2261150","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2261150","url":null,"abstract":"ABSTRACTThe spectral probability distributions (SPD) of water bodies in satellite images have demonstrated the potential for indicating the geographical and environmental features of their watersheds. This implies that SPDs also have the potential for indicating water quality features, but so far there have been no further studies on their correlations. In this study, 690 SPDs of global closed connected water bodies, mainly including lakes and reservoirs, were extracted from Landsat-8 images. These SPDs were classified into seven types, and the entropy of each SPD diagram was calculated. The correlation between the SPD diagram’s entropy and Forel-Ule index (FUI) is relatively good with R2 = 0.5651 – indicating that water bodies with better water quality are usually found to have smaller entropy in their SPD diagrams. This study demonstrates that SPD is a good indicator for not only the aquatic environment but also water quality monitoring.KEYWORDS: Forel-Ule index (FUI)spectral probability distribution (SPD)Landsat-8remote sensingwater quality AcknowledgmentsThis research was funded by the National Natural Science Foundation of China (No. 41971373) and the Science Foundation of Donghai Laboratory (No. DH-2022KF01009).Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the National Natural Science Foundation of China [41971373]; Science Foundation of Donghai Laboratory [DH-2022KF01009].","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136130533","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}

{"title":"Predicting Larch Casebearer damage with confidence using Yolo network models and conformal prediction","authors":"Ulf Norinder, Stephanie Lowry","doi":"10.1080/2150704x.2023.2258460","DOIUrl":"https://doi.org/10.1080/2150704x.2023.2258460","url":null,"abstract":"This investigation shows that successful forecasting models for monitoring forest health status with respect to Larch Casebearer damages can be derived using a combination of a confidence predictor framework (Conformal Prediction) in combination with a deep learning architecture (Yolo v5). A confidence predictor framework can predict the current types of diseases used to develop the model and also provide indication of new, unseen, types or degrees of disease. The user of the models is also, at the same time, provided with reliable predictions and a well-established applicability domain for the model where such reliable predictions can and cannot be expected. Furthermore, the framework gracefully handles class imbalances without explicit over- or under-sampling or category weighting which may be of crucial importance in cases of highly imbalanced datasets. The present approach also provides indication of when insufficient information has been provided as input to the model at the level of accuracy (reliability) need by the user to make subsequent decisions based on the model predictions.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235358","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}