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IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium最新文献

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Grapevine Varieties Identification Using Vision Transformers 利用视觉变压器识别葡萄品种
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883286
G. Carneiro, L. Pádua, Emanuel Peres, R. Morais, J. Sousa, António Cunha
{"title":"Grapevine Varieties Identification Using Vision Transformers","authors":"G. Carneiro, L. Pádua, Emanuel Peres, R. Morais, J. Sousa, António Cunha","doi":"10.1109/IGARSS46834.2022.9883286","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883286","url":null,"abstract":"The grape variety plays an important role in the wine production chain, thus identifying it is crucial for production control. Ampelographers, professionals who identify grape varieties through plant visual analysis, are scarce, and molecular markers are expansive to identify grape varieties on a large scale. In this context, Deep Learning models become an effective way to handle ampelographers scarcity. In this work, we explore the benefit of using deep learning vision transformers architecture relative to conventional CNN to identify 12 grapevine varieties using leaf-centred RGB images acquired in the field. We train an Xception model as a baseline and four different configurations of the ViT_B model. The best model achieved 0.96 of Fl-score, outperforming the state-of-the-art convolutional-based model in the used dataset.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126838921","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}
引用次数: 1
SAR Image Change Detection Via UR-ISTA SAR图像变化检测通过UR-ISTA
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884312
Che Chen, Yuanfan Zheng, Xue Jiang, Xingzhao Liu
{"title":"SAR Image Change Detection Via UR-ISTA","authors":"Che Chen, Yuanfan Zheng, Xue Jiang, Xingzhao Liu","doi":"10.1109/IGARSS46834.2022.9884312","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884312","url":null,"abstract":"In this paper, we propose a novel dictionary learning model based on the idea of deep unrolling to deal with the synthetic aperture radar (SAR) image change detection problem. Deep unrolling aims at unrolling the iterative algorithm into a trainable neural network. In our proposed method, the idea of unrolling is applied to the Iterative Shrinkage Threshold Algorithm (ISTA), which is one of classic algorithms for dictionary learning. Then, the proposed Unrolling Iterative Shrinkage Threshold Algorithm (UR-ISTA), is utilized to obtain the sparse codes of the difference results. Finally, the change map is computed by k-means clustering algorithm. The advantage of UR-ISTA method is relatively low time cost, which makes it possible to add dictionary updating step to calculate specific feature vectors. Experimental results show that the proposed approach has superior accuracy and precision compared to several well-known change detection techniques. The proposed UR-ISTA algorithm shows more robustness than another sparse representation algorithm.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115046040","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}
引用次数: 0
Visualwind: a Novel Video Dataset for Cameras to Sense the Wind Visualwind:用于摄像机感知风的新颖视频数据集
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884030
Qin Zhang, Jialang Xu, Matthew Crane, Chunbo Luo
{"title":"Visualwind: a Novel Video Dataset for Cameras to Sense the Wind","authors":"Qin Zhang, Jialang Xu, Matthew Crane, Chunbo Luo","doi":"10.1109/IGARSS46834.2022.9884030","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884030","url":null,"abstract":"The goal of this paper is to empower cameras to sense the wind from videos by capturing the motion information using optical flow and machine learning models, to potentially revolutionise the spatiotemporal resolution of existing professional wind records that are often at the city scale. To this end, we build a novel video dataset of over 6000 labeled video clips, covering eleven wind classes of the Beaufort scale. The videos are collected from social media, public cameras, and self-recording. Every video clip has a fixed 10 seconds length with varied frame rates, and contains scenes of various trees swaying in different scales of wind. We describe the key statistics of the dataset, how it was collected and annotated, and evaluate both one-stage and two-stage models trained and tested for wind scale estimation on this dataset to give some baseline performance figures. The dataset is publicly accessible11https://sme.uds.exeter.ac.uk/folders/48caf5102d6196b9645fab1f46e494ec. Please contact the authors to get the access key due to the server protection policy..","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"503 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115489801","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}
引用次数: 2
On the Cutoff Wavenumber in the Geometrical Optics Theory of Near Specular Scattering from the Sea Surface 海面近镜面散射几何光学理论中的截止波数
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884153
J. Johnson, Ethan Raines, J. Toporkov, P. Hwang, J. Ouellette
{"title":"On the Cutoff Wavenumber in the Geometrical Optics Theory of Near Specular Scattering from the Sea Surface","authors":"J. Johnson, Ethan Raines, J. Toporkov, P. Hwang, J. Ouellette","doi":"10.1109/IGARSS46834.2022.9884153","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884153","url":null,"abstract":"A method for determining the cutoff wavenumber used in the geometrical optics theory of scattering from a random rough surface is presented. The method is based on the “alpha stable distribution” approach described in previous studies, and yields a cutoff wavenumber that depends on the spectrum model applied, the angle of incidence, and the frequency of interest. Use of the cutoff wavenumber so determined is found to improve agreement between predictions of the geometrical optics and physical optics theories of near specular scattering from the sea surface in some situations.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115491472","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}
引用次数: 2
Modeling The Impact of Temporal Decorrelation on Insar Ground Cancellation Techniques in the Frame of Tropical Forest Characterization at P Band P波段热带森林特征框架下时间去相关对Insar地面对消技术的影响
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884243
L. Ferro-Famil, M. M. d’Alessandro, S. Tebaldini, Yue Huang
{"title":"Modeling The Impact of Temporal Decorrelation on Insar Ground Cancellation Techniques in the Frame of Tropical Forest Characterization at P Band","authors":"L. Ferro-Famil, M. M. d’Alessandro, S. Tebaldini, Yue Huang","doi":"10.1109/IGARSS46834.2022.9884243","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884243","url":null,"abstract":"3-D imaging using SAR tomography is a well-recognized technique for the characterization of forested areas. Studies revealed that the intensity of radar echoes originating from specific locations within the canopy of forest could be used to estimate its above ground biomass. Moreover, a recent work proposed an estimation technique using a pair of interferometric SAR images only. The images are combined in order to cancel contributions from the ground, and to roughly estimate the volume reflectivity. This paper proposes to study the influence of temporal decorrelation of this minimalist approach, which relies on the hypothesis of perfectly correlated signals. A model, based on second order statistics, is proposed and is used to predict the influence of temporal decorrelation of the relative error of the above ground biomass estimation over tropical forests measured at $mathrm{P}$ band.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115502345","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}
引用次数: 0
Generative-Network Based Multimedia Super-Resolution for Uav Remote Sensing 基于生成网络的无人机遥感多媒体超分辨率
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884486
Yash Turkar, C. Aluckal, S. De, V. Turkar, Y. Agarwadkar
{"title":"Generative-Network Based Multimedia Super-Resolution for Uav Remote Sensing","authors":"Yash Turkar, C. Aluckal, S. De, V. Turkar, Y. Agarwadkar","doi":"10.1109/IGARSS46834.2022.9884486","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884486","url":null,"abstract":"Unmanned Aerial Vehicle (UAV) based aerial mapping has taken over the surveying industry thanks to low costs and ease of use. Although these UAVs have relatively high-resolution imaging systems, there exists a near exponential relationship between the ground sampling distance (GSD) and the number of images required - which is a function of flight altitude. To tackle this, we use a generative network based super-resolution approach to increase the GSD of images which effectively reduces flight time. In this paper we test the efficiency and efficacy of this approach using two multimedia super-resolution implementations. We also provide quantitative results comparing the two using various image processing metrics.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116210141","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}
引用次数: 0
The Spatial Response Function of CSCAT Backscatter Measurements CSCAT后向散射测量的空间响应函数
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883660
Liling Liu, Yongli Li, Xiaolong Dong, Wenming Lin, Fei Zhao
{"title":"The Spatial Response Function of CSCAT Backscatter Measurements","authors":"Liling Liu, Yongli Li, Xiaolong Dong, Wenming Lin, Fei Zhao","doi":"10.1109/IGARSS46834.2022.9883660","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883660","url":null,"abstract":"The China-France Oceanography Satellite (CFOSAT) scatterometer (CSCAT) is a rotating range-gated fan-beam scatterometer which is designed to measure the normalized radar backscatter. Because of its high spatial sampling density, CSCAT data is suited for land and ice applications facilitated by resolution enhancement algorithms. Generally, such algorithms require accurate knowledge of the spatial response function (SRF) for the individual measurement. Standard CSCAT L1B products include the locations of the backscatter measurements, but not the SRF. In the paper, the mathematical expression of CSCAT slice SRF is derived, and the shape of the SRF is investigated. It has been found that the SRF can be approximated as a binary function that is 1 inside the −3dB contour of the SRF and 0 outside. The shapes of CSCAT SRF varies with antenna rotation angle and orbit position.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116391903","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}
引用次数: 0
Target Detection over Temperature Profiles 目标检测温度曲线
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884269
İlke Belenoğlu, Metehan Yalçin, S. E. Yüksel, A. Koz
{"title":"Target Detection over Temperature Profiles","authors":"İlke Belenoğlu, Metehan Yalçin, S. E. Yüksel, A. Koz","doi":"10.1109/IGARSS46834.2022.9884269","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884269","url":null,"abstract":"This paper investigates the performance of hyperspectral target detection methods for target rediscovery on temperature profiles extracted from longwave infrared (LWIR) hyperspectral images (HSI). The targets in the experiments are selected as the three military vehicles in the captured scenes at different times. These targets are placed in the scene with different angles, at 0, 90, 135 degrees, respectively. The scope of the performed experiments includes the investigation and comparison of target detection performances, (i) with respect to sampling period in a day, (ii) time and temperature difference between reference and test days (iii) time window in a day. We utilized adaptive coherence estimator (ACE) target detection method on temperature profiles extracted over HSI. The experiments indicated that the sampling period during the day should be less than 1 hour for a robust target detection. Secondly, the target detection performance decreases as the distance between the days increases, but can be increased dramatically if similar weather temperatures are observed despite a long duration between the data recordings. Third, it is observed that the time zones with intense temperature changes such as sunrise and sunset are suitable for target detection.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116408978","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}
引用次数: 0
Hyperspectral Salient Object Detection Using Extended Morphology with CNN 基于CNN扩展形态学的高光谱显著目标检测
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883107
Koushikey Chhapariya, K. Buddhiraju, Adarsh Kumar
{"title":"Hyperspectral Salient Object Detection Using Extended Morphology with CNN","authors":"Koushikey Chhapariya, K. Buddhiraju, Adarsh Kumar","doi":"10.1109/IGARSS46834.2022.9883107","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883107","url":null,"abstract":"Salient object detection using hyperspectral images is crucial for various image processing and computer vision applications. Many studies considering spectral information have been developed, extracting only low-level features from a hy-perspectral image. In this research work, a dataset specifically developed for salient object detection called HS-SOD is considered exploiting both spatial and spectral information equally. To include spatial information, Extended Morpho-logical Profile (EMP) has been considered. EMP incorpo-rates spatial characteristics by including nearby pixel information. A convolution neural network (CNN) is integrated with extended morphology to extract high-level features. It detect objects of multiple spatial scales and ratios, preserving boundary edges. We observed an improvement of 5 % in overall accuracy while using EMP with CNN compared to that of using EMP without CNN. Thus, the experimental re-sults demonstrate the effectiveness of EMP with CNN on the hyperspectral datasets.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122391880","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}
引用次数: 0
Cycle GAN Based Heterogeneous Spatial-Spectral Fusion for Soil Moisture Downscaling 基于循环氮化镓的非均匀空间光谱融合土壤水分降尺度研究
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884702
Menghui Jiang, Huanfeng Shen, Jie Li
{"title":"Cycle GAN Based Heterogeneous Spatial-Spectral Fusion for Soil Moisture Downscaling","authors":"Menghui Jiang, Huanfeng Shen, Jie Li","doi":"10.1109/IGARSS46834.2022.9884702","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884702","url":null,"abstract":"Soil moisture (SM) downscaling aims to solve the coarse resolution problem of passive microwave SM products. On the basis of SMAP SM products and related MODIS products, this study develops a deep residual cycle generative adversarial network (GAN) based heterogeneous spatial-spectral fusion method to downscale SMAP SM from 36km to 9km. On the one hand, the proposed method creatively regards the MODIS products that can reflect the SM state as the spectral features of SM in a broad sense and performs the heterogeneous spatial-spectral fusion between the low-resolution (LR) SM product and high-resolution (HR) MODIS products. On the other hand, considering the spatial correlation of SM, the proposed method utilizes a deep residual cycle generative adversarial network (GAN) to extract and fuse features of heterogeneous images through convolutions. Both qualitative and quantitative evaluation of experimental results shows that the proposed method can generate high accuracy SM products.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122743120","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}
引用次数: 0
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