2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)最新文献

Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images 多模态前哨图像在草地割草事件检测中的挑战

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-05 DOI: 10.1109/Multi-Temp.2019.8866914

A. Garioud, S. Giordano, S. Valero, C. Mallet

引用次数: 6

Building Change Detection using Object-Oriented LBP Feature Map in Very High Spatial Resolution Imagery 基于面向对象LBP特征映射的高空间分辨率图像建筑变化检测

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866919

Lan Zhang, B. Zhong, A. Yang

{"title":"Building Change Detection using Object-Oriented LBP Feature Map in Very High Spatial Resolution Imagery","authors":"Lan Zhang, B. Zhong, A. Yang","doi":"10.1109/Multi-Temp.2019.8866919","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866919","url":null,"abstract":"Building change detection has always been a popular direction in field of remote sensing. The building detection method for very high spatial resolution(VHR) imagery proposed in this paper is based on the classical local binary algorithm. First, histogram equalization and bilateral filtering are used on high-resolution remote sensing images to enhance the contrast and the building edge, which is beneficial to the next process. In the first process, the low-density feature map is obtained through the classical local binary patterns(LBP) algorithm, and then the ground objects are divided into objects by mean shift based on the feature map. This method can accurately segment the boundary of buildings. In the second process, a new rotation uniform invariant local binary pattern algorithm is applied to obtain OOLBP features. Finally, support vector machine classifier (SVM) is adopted for classification. Last, the change types of buildings were identified, including the newly added buildings, building disappeance and building reconstruction. the results show that the overall accuracy and recall ratio exceeds 94%.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121561745","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: 6

Unsupervised Change Detection in VHR Images Based on Morphological Profiles and Automated Training Sample Extraction 基于形态轮廓和自动训练样本提取的VHR图像无监督变化检测

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866929

Xin Wang, Peijun Du, Sicong Liu, Yaping Meng, Cong Lin

引用次数: 0

Sensor-specific Transfer Learning for Hyperspectral Image Processing 用于高光谱图像处理的特定传感器迁移学习

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866896

Shaohui Mei, Xiao Liu, Ge Zhang, Q. Du

{"title":"Sensor-specific Transfer Learning for Hyperspectral Image Processing","authors":"Shaohui Mei, Xiao Liu, Ge Zhang, Q. Du","doi":"10.1109/Multi-Temp.2019.8866896","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866896","url":null,"abstract":"Transfer learning (TL) has shown its great advantage to solve small-training-sample problems using knowledge learned from existing large data with deep learning techniques, which can be used for hyperspectral image intelligent processing in which labeled data is very difficult and even impossible to be obtained. However, the mismatch of hyperspectral sensors results in lots of difficulty for transfer learning to be used in hyperspectral image (HSI) processing. In this paper, sensor-specific based transfer learning is proposed for hyperspectral images acquired from same sensors, in which knowledge learn from hyperspectral images, e.g., the network structure and parameters of a deep neural network, are limited to transfer to images of the same sensor only. Specifically, the validity of sensor-specific transfer learning is evaluated using three deep learning based tasks, including feature learning, super-resolution, and image denoising. Experimental results from two benchmark datasets from the well-known ROSIS sensor, i.e., Pavia Centre and Pavia University, have demonstrated that sensor-specific based transfer learning can achieve satisfying performance even without fine-tune by small-training-samples on the target scene.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124097386","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: 3

Vehicle Detection and Tracking in Remote Sensing Satellite Vidio based on Dynamic Association 基于动态关联的遥感卫星视频车辆检测与跟踪

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866890

Jinyue Zhang, Xiangrong Zhang, Xu Tang, Zhongjian Huang, L. Jiao

{"title":"Vehicle Detection and Tracking in Remote Sensing Satellite Vidio based on Dynamic Association","authors":"Jinyue Zhang, Xiangrong Zhang, Xu Tang, Zhongjian Huang, L. Jiao","doi":"10.1109/Multi-Temp.2019.8866890","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866890","url":null,"abstract":"Since remote sensing video satellites can continuously observe a certain target area and obtain multitemporal remote sensing images, it makes the surveillance of thousands of moving objects on the wide area possible. Vehicles are a kind of important and typical objects for remote sensing detection and tracking. In the paper, we propose an efficient method to detect and track vehicles in multi-temporal remote sensing images including two stages: Vehicle detection stage and tracking stage. In the vehicle detection stage, we use background subtraction and combine road prior information to improve accuracy and efficiency and reduce search space. In the tracking stage, we improve the traditional association matching method, which apply more dynamic association methods and more practical state judgment rule. In addition, we divide tracking objects into groups to further improve the accuracy. Our method is evaluated on remote sensing video dataset. According to experiment result, the proposed method can detect and tracking vehicle objects and correct the misdirected objects by the dynamic association structure. In the stable tracking stage, tracking quality is 96%. The experimental results show effectiveness and robustness of the proposed method in detection and tracking of vehicle objects from multi-temporal remote sensing images.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124124890","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

Multitemporal InSAR Coherence Change Analysis: Application to Volcanic Eruption Monitoring 多时相InSAR相干性变化分析:在火山喷发监测中的应用

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866922

Thu Trang Lê, J. Froger, Alexis Hrysiewicz, Ha-Thai Pham

引用次数: 3

Low-Rank Representation Based Domain Adaptation for Classification of Remote Sensing Images 基于低秩表示的遥感图像领域自适应分类

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866935

Wen Wang, Li Ma

引用次数: 0

Production of historical classification products based on existing land cover classification products and Google Earth Engine platform 基于现有土地覆盖分类产品和Google Earth Engine平台，制作历史分类产品

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866841

Kunsheng Jue, B. Zhong, A. Yang

{"title":"Production of historical classification products based on existing land cover classification products and Google Earth Engine platform","authors":"Kunsheng Jue, B. Zhong, A. Yang","doi":"10.1109/Multi-Temp.2019.8866841","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866841","url":null,"abstract":"Land cover classification plays a crucial role in the detection of changes (such as cities, forests, vegetation changes) and the extraction and analysis of information. However, traditional land cover classification methods always spend a lot of time on data processing, which leads to inefficient production of classified products. Furthermore, most of the existing methods rarely consider the application of the sample in time, especially the lack of basic data for building long time series. In this study, we used historical high-precision classification data to create a sample set to train the classifier on the Google Earth Engine, we applied the classifier to other years in the same region. To some extent, the classifier's expansion in time is realized. The classified data mainly comes from the Landsat satellite series data available by Google Earth Engine. The classification effect is evaluated by the confusion matrix, and the results show that the overall accuracy exceeds 93.0% and kappa coefficient is 0.9 the accuracy of other years is also higher than 85%, it shows that we have a better and stable realization of sample migration and the expansion of classifier in time.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133328004","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

A Study for Hyperspectral Anomaly Change Detection on “Viareggio 2013 Trial” Dataset “Viareggio 2013 Trial”数据集高光谱异常变化检测研究

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866969

Chen Wu, Yukun Lin, Bo Du, Liangpei Zhang

引用次数: 4

Using Sentinel-2 Image Time Series to map the State of Victoria, Australia 利用Sentinel-2图像时间序列绘制澳大利亚维多利亚州地图

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866921

Charlotte Pelletier, Zehui Ji, O. Hagolle, E. Morse-McNabb, K. Sheffield, Geoffrey I. Webb, F. Petitjean

引用次数: 5