Journal of Applied Remote Sensing最新文献_第6页

Exploring impacts of aerosol on convective clouds using satellite remote sensing and machine learning 利用卫星遥感和机器学习探索气溶胶对对流云的影响

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-01-01 DOI: 10.1117/1.jrs.18.012007

Jiaqin Mi, Yuanjian Yang, Shuxue Zhou, Xiaoyan Ma, Siying Wei

引用次数: 0

CBTA: a CNN-BiGRU method with triple attention for winter wheat yield prediction CBTA：用于冬小麦产量预测的三重关注 CNN-BiGRU 方法

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-01-01 DOI: 10.1117/1.jrs.18.014507

Wenzheng Ye, Tinghuai Ma, Zilong Jin, Huan Rong, Benjamin Kwapong Osibo, Mohamed Magdy Abdel Wahab, Yuming Su, Bright Bediako-Kyeremeh

{"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":"8 1","pages":""},"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}

引用次数: 0

Extraction of pine wilt disease based on a two-stage unmanned aerial vehicle deep learning method 基于两阶段无人飞行器深度学习方法的松树枯萎病提取方法

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2024-01-01 DOI: 10.1117/1.jrs.18.014503

Xin Huang, Weilin Gang, Jiayi Li, Zhili Wang, Qun Wang, Yuegang Liang

{"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":"72 1","pages":""},"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}

引用次数: 0

Two-dimensional compact variational mode decomposition for effective feature extraction and data classification in hyperspectral imaging 二维紧凑变模分解用于高光谱成像中的有效特征提取和数据分类

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2023-12-18 DOI: 10.1117/1.jrs.17.044517

Renxiong Zhuo, Yunfei Guo, Baofeng Guo, Baoyang Liu, Fan Dai

引用次数: 0

Inverse synthetic aperture radar imaging technology based on multiple repeated subpulses of frequency diversity array 基于频率分集阵列多个重复子脉冲的反合成孔径雷达成像技术

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2023-12-14 DOI: 10.1117/1.jrs.17.046511

Zhenbo Wang, Ningbo Xie, Kefei Liao, Qinlin Li

引用次数: 0

Academic development and space operations of a multispectral imaging payload for 1U CubeSats 1U 立方体卫星多光谱成像有效载荷的学术开发和空间运行

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2023-12-06 DOI: 10.1117/1.jrs.17.047501

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

引用次数: 0

Variational pansharpening based on high-pass injection fidelity with local dual-scale coefficient estimation 基于高通注入保真度和局部双尺度系数估算的变分平差技术

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2023-12-06 DOI: 10.1117/1.jrs.17.046510

Lingxin GongYe, Kyongson Jon, Jianhua Guo

引用次数: 0

Two-stage outlier removal strategy for correspondence-based point cloud registration 基于对应关系的点云注册两阶段离群值去除策略

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2023-12-05 DOI: 10.1117/1.jrs.17.044516

Shaodong Li, Yongzheng Chen, Peiyuan Gao

引用次数: 0

Loaded waveguide measurements of plastic explosives at V-band V 波段可塑炸药加载波导测量

IF 1.7 4区地球科学

Journal of Applied Remote Sensing Pub Date : 2023-12-01 DOI: 10.1117/1.jrs.18.014501

Zachary J. Landicini, Jeffrey Barber, James C. Weatherall, Duane C. Karns, Peter R. Smith, Joaquín Aparicio-Bolaño, Wendy Ruiz

{"title":"Loaded waveguide measurements of plastic explosives at V-band","authors":"Zachary J. Landicini, Jeffrey Barber, James C. Weatherall, Duane C. Karns, Peter R. Smith, Joaquín Aparicio-Bolaño, Wendy Ruiz","doi":"10.1117/1.jrs.18.014501","DOIUrl":"https://doi.org/10.1117/1.jrs.18.014501","url":null,"abstract":"Dielectric measurements of plastic explosives using a loaded waveguide technique via vector network analyzer and banded millimeter wave extender modules operating at V-band (50 to 75 GHz) are performed. A portion of an explosive sample is inserted into a waveguide shim 2 mm in length and trimmed flush with the faces of the shim. Two-port S-parameter measurements are conducted on the explosive; the empty shim is similarly characterized. Using standard waveguide equations and the measured length of the shim, the complex S-parameter data obtained with the filled shim is optimized to four free parameters—complex permittivity and distance offsets for the two sample faces relative to the calibration planes. Permittivity data obtained from measurements of the plastic explosives C-4, Primasheet 1000, Primasheet 2000 and Semtex 10 are presented. Results obtained for C-4 and Primasheet 1000 are comparable to other data in the literature, and the data on Primasheet 2000 and Semtex 10 are the first known published permittivity values in this range. Excellent agreement between the experiment and the fit is obtained using a constant permittivity across the waveguide band, indicating that dispersion is not significant for these materials.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"7 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063547","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

Redo Fenestrated-Branched Endovascular Aortic Repair (F-BEVAR) for Failed F-BEVAR. 对F-BEVAR失败的患者重做开窗-分支血管内主动脉修复(F-BEVAR)。

4区地球科学

Journal of Applied Remote Sensing Pub Date : 2023-12-01 Epub Date: 2022-06-06 DOI: 10.1177/15266028221098707

Anna L Driessen, Carla K Scott, Gerardo G Guardiola, Mirza S Baig, Melissa L Kirkwood, Carlos H Timaran

引用次数: 1