Journal of Applied Remote Sensing最新文献

{"title":"Feasibility of remote estimation of optical turbulence via quick response code imaging","authors":"Burton Neuner III, Skylar D. Lilledahl, Kyle R. Drexler","doi":"10.1117/1.jrs.18.014505","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014505","url":null,"abstract":"Turbulence estimation theory is presented and demonstrated by imaging a series of spatially encoded quick response (QR) codes in ambient radiation through atmospheric scintillation. This remote sensing concept was verified though preliminary feasibility experiments and detailed MATLAB simulations using QR codes displayed on a low-power digital e-ink screen. Of note, knowledge of propagation range and QR code dimensions are not required ahead of time, as each code contains information detailing its block size and overall physical size, enabling automated calculations of spatial resolution and target range. Estimation algorithms leverage the extracted resolution and range information to determine path-integrated optical turbulence, as quantified by the Fried parameter, r0. The estimation criterion is obtained by cycling a series of QR code sizes on an e-ink screen and determining the transition point at which the QR code can no longer be read, resulting in a system capable of automatically estimating path-integrated optical turbulence.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139501516","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":"Exploring impacts of aerosol on convective clouds using satellite remote sensing and machine learning","authors":"Jiaqin Mi, Yuanjian Yang, Shuxue Zhou, Xiaoyan Ma, Siying Wei","doi":"10.1117/1.jrs.18.012007","DOIUrl":"https://doi.org/10.1117/1.jrs.18.012007","url":null,"abstract":"Aerosol–cloud–precipitation interaction is currently a research hotspot that is challenging but also one of the most prominent sources of uncertainty affecting climate change. We have identified 1082 mesoscale convective systems (MCSs) over eastern China from April to September in 2016 and 2017. Overall, the occurrence frequency and MCS area increased when altitude increased, as demonstrated by the t-test at 95% confidence. More MCSs appeared and matured fully, although they moved slowly, in a selected urban agglomeration area compared to a selected rural area, owing to the urbanization impact. With an increase in the concentration of particulate matter with particle size below 10 μm (PM10) averaged by the first 3 h of MCS initiations, the cloud top brightness temperature and MCS area decreased, resulting in weakened precipitation intensity and a smaller MCS area. The t-test was passed with 90% confidence, confirming this finding. In addition, high-humidity circumstances can produce enough water vapor to support the creation of many higher and deeper MCSs.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139464031","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":"Man-made object segmentation around reservoirs by an end-to-end two-phase deep learning-based workflow","authors":"Nayereh Hamidishad, Roberto Marcondes Cesar Jr.","doi":"10.1117/1.jrs.18.018502","DOIUrl":"https://doi.org/10.1117/1.jrs.18.018502","url":null,"abstract":"Reservoirs are fundamental infrastructures for the management of water resources. Constructions around them can negatively impact their water quality. Such constructions can be detected by segmenting man-made objects around reservoirs in the remote sensing (RS) images. Deep learning (DL) has attracted considerable attention in recent years as a method for segmenting the RS imagery into different land covers/uses and has achieved remarkable success. We develop an approach based on DL and image processing techniques for man-made object segmentation around the reservoirs. In order to segment man-made objects around the reservoirs in an end-to-end procedure, segmenting reservoirs and identifying the region of interest (RoI) around them are essential. In the proposed two-phase workflow, the reservoir is initially segmented using a DL model, and a postprocessing stage is proposed to remove errors, such as floating vegetation in the generated reservoir map. In the second phase, the RoI around the reservoir (RoIaR) is extracted using the proposed image processing techniques. Finally, the man-made objects in the RoIaR are segmented using a DL model. To illustrate the proposed approach, our task of interest is segmenting man-made objects around some of the most important reservoirs in Brazil. Therefore, we trained the proposed workflow using collected Google Earth images of eight reservoirs in Brazil over two different years. The U-Net-based and SegNet-based architectures are trained to segment the reservoirs. To segment man-made objects in the RoIaR, we trained and evaluated four architectures: U-Net, feature pyramid network, LinkNet, and pyramid scene parsing network. Although the collected data are highly diverse (for example, they belong to different states, seasons, resolutions, etc.), we achieved good performances in both phases. The F1-score of phase-1 and phase-2 highest performance models in segmenting test sets are 96.53% and 90.32%, respectively. Furthermore, applying the proposed postprocessing to the output of reservoir segmentation improves the precision in all studied reservoirs except two cases. We validated the prepared workflow with a reservoir dataset outside the training reservoirs. The F1-scores of the phase-1 segmentation stage, postprocessing stage, and phase-2 segmentation stage are 92.54%, 94.68%, and 88.11%, respectively, which show high generalization ability of the prepared workflow.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139554459","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":"CBTA: a CNN-BiGRU method with triple attention for winter wheat yield prediction","authors":"Wenzheng Ye, Tinghuai Ma, Zilong Jin, Huan Rong, Benjamin Kwapong Osibo, Mohamed Magdy Abdel Wahab, Yuming Su, Bright Bediako-Kyeremeh","doi":"10.1117/1.jrs.18.014507","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014507","url":null,"abstract":"Timely and accurate prediction of winter wheat yield contributes to ensuring national food security. We propose a CNN- bidirectional gated recurrent unit method with triple attention for winter wheat yield prediction, named CBTA. This deep learning model uses convolutional neural networks to mine the spatial spectral information in hyperspectral remote sensing images. Furthermore, the bidirectional gated recurrent unit is used to adaptively learn the time dependence between the various stages of winter wheat growth. Data from Henan Province, China, is used in this study to train the model and also verify its prediction performance and stability. The results from our experiment show that our proposed model has an excellent effect on yield prediction in the county, with root-mean-square-error, mean absolute error, and R2 of 0.469 t/ha, 0.336 t/ha, and 0.827, respectively. Moreover, our findings suggested that the precision of our model using the data from sowing to heading-flowering stage was very close to that from sowing to ripening stage, which proves that the CBTA model can accurately predict the yield of winter wheat 1 to 2 months in advance.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578001","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":"Extraction of pine wilt disease based on a two-stage unmanned aerial vehicle deep learning method","authors":"Xin Huang, Weilin Gang, Jiayi Li, Zhili Wang, Qun Wang, Yuegang Liang","doi":"10.1117/1.jrs.18.014503","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014503","url":null,"abstract":"Forestry pests pose a significant threat to forest health, making precise extraction of infested trees a vital aspect of forest protection. In recent years, deep learning has achieved substantial success in detecting infestations. However, when applying existing deep learning methods to infested tree detection, challenges arise, such as limited training samples and confusion between forest areas and artificial structures. To address these issues, this work proposes a two-stage hierarchical semi-supervised deep learning approach based on unmanned aerial vehicle visible images to achieve the individual extraction of each pine wilt disease (PWD). The approach can automatically detect the positions and crown extents of each infested tree. The comprehensive framework includes the following key steps: (a) considering the disparities in global image representation between forest areas and artificial structures, a scene classification network named MobileNetV3 is trained to effectively differentiate between forested regions and other artificial structures. (b) Considering the high cost of manually annotating and incomplete labeling of infested tree samples, a semi-supervised infested tree samples mining method is introduced, significantly reducing the workload of sample annotation. Ultimately, this method is integrated into the YOLOv7 object detection network, enabling rapid and reliable detection of infested trees. Experimental results demonstrate that, with a confidence threshold of 0.15 and using the semi-supervised sample mining framework, the number of samples increases from 53,046 to 93,544. Precision evaluation metrics indicate a 5.8% improvement in recall and a 2.6% increase in mean average precision@.5. The final test area prediction achieves an overall accuracy of over 80% and the recall rate of over 90%, indicating the effectiveness of the proposed method in PWD detection.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139103035","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":"Two-dimensional compact variational mode decomposition for effective feature extraction and data classification in hyperspectral imaging","authors":"Renxiong Zhuo, Yunfei Guo, Baofeng Guo, Baoyang Liu, Fan Dai","doi":"10.1117/1.jrs.17.044517","DOIUrl":"https://doi.org/10.1117/1.jrs.17.044517","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138963675","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":"Inverse synthetic aperture radar imaging technology based on multiple repeated subpulses of frequency diversity array","authors":"Zhenbo Wang, Ningbo Xie, Kefei Liao, Qinlin Li","doi":"10.1117/1.jrs.17.046511","DOIUrl":"https://doi.org/10.1117/1.jrs.17.046511","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138971675","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":"Academic development and space operations of a multispectral imaging payload for 1U CubeSats","authors":"Luis Zea, Aldo Aguilar-Nadalini, Marvin Martínez, Johan Birnie, Emilio Miranda, Fredy España, Kuk Chung, Dan Álvarez, J. Bagur, Carlo Estrada, Rony Herrarte, V. Ayerdi","doi":"10.1117/1.jrs.17.047501","DOIUrl":"https://doi.org/10.1117/1.jrs.17.047501","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138596247","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":"Variational pansharpening based on high-pass injection fidelity with local dual-scale coefficient estimation","authors":"Lingxin GongYe, Kyongson Jon, Jianhua Guo","doi":"10.1117/1.jrs.17.046510","DOIUrl":"https://doi.org/10.1117/1.jrs.17.046510","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138595569","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":"Two-stage outlier removal strategy for correspondence-based point cloud registration","authors":"Shaodong Li, Yongzheng Chen, Peiyuan Gao","doi":"10.1117/1.jrs.17.044516","DOIUrl":"https://doi.org/10.1117/1.jrs.17.044516","url":null,"abstract":"","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138601105","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}