2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)最新文献_第2页

PMCMA: Pattern Mining in SAR Time Series by Change Matrix Analysis 基于变化矩阵分析的SAR时间序列模式挖掘

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866977

Dongqing Peng, Ting Pan, Wen Yang, Hengchao Li

引用次数: 1

Ideal Regularized Kernel Subspace Alignment for Unsupervised Domain Adaptation in Hyperspectral Image Classification 高光谱图像分类中无监督域自适应的理想正则化核子空间对准

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866985

Wenqi Fan, Tianhui Wei, Jiangtao Peng, Weiwei Sun

引用次数: 2

Feasibility analysis of airfield selection based on multi-temporal and multi-mission remote sensing data 基于多时相多任务遥感数据的机场选择可行性分析

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866931

Jiaxin Liu, X. Cui, Yixiang Tian, Bo Sun, A. Markov, D. Lv, G. Hai, G. Qiao, T. Feng, Rongxing Li

引用次数: 0

Analyzing spatio-temporal characteristics of urban LULC and LST over Shanghai during 2009-2019 2009-2019年上海城市土地利用和地表温度时空特征分析

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866897

Yongjie Zheng, Sicong Liu, Z. Song, X. Tong, Huan Xie

{"title":"Analyzing spatio-temporal characteristics of urban LULC and LST over Shanghai during 2009-2019","authors":"Yongjie Zheng, Sicong Liu, Z. Song, X. Tong, Huan Xie","doi":"10.1109/Multi-Temp.2019.8866897","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866897","url":null,"abstract":"The expansion of urban areas is always accompanied by land use/land cover (LULC) changes and associated with the distribution of urban heat islands (UHI) simultaneously. In particular, in the metropolis such as Shanghai, the land cover transitions between the impervious surface (IS) and vegetation areas highly influence the ecological quality. This study focused on the monitoring and analysis on the LULC and land surface temperature (LST) spatio-temporal changes in Shanghai during the period between 2009 and 2019. We evaluated LULC and LST changes based on the multitemporal Landsat images acquired over five individual years (i.e., 2009, 2012, 2015, 2017 and 2019). In particular, the Fully Constrained Least Squares (FCLS) spectral unmixing and several derived spectral indices are used for generating the LULC maps, and Radiative Transfer Equation (RTE) temperature inversion method is utilized for estimating the LST. In order to compare the dominance of different land classes, then we analyzed the classification maps from different landscape levels. Experimental results demonstrate that during the past ten years the IS of Shanghai continually expanded from the city center mainly towards the Pudong, Songjiang and Jiading three districts directions. Vegetation especially the cropland in the urban fringe areas were replaced by the newly appeared IS, which lead to the rising temperature in the corresponding areas. This may further accelerate the urban heat island phenomenon.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"9 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":"127652345","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

Deep Canonical Correlation Analysis Network for Scene Change Detection of Multi-Temporal VHR Imagery 多时相VHR图像场景变化检测的深度典型相关分析网络

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866943

Lixiang Ru, Chen Wu, Bo Du, Liangpei Zhang

{"title":"Deep Canonical Correlation Analysis Network for Scene Change Detection of Multi-Temporal VHR Imagery","authors":"Lixiang Ru, Chen Wu, Bo Du, Liangpei Zhang","doi":"10.1109/Multi-Temp.2019.8866943","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866943","url":null,"abstract":"Change detection at semantic scene level has now been an important topic of high spatial resolution remote sensing imagery analysis. In this paper, combining with Deep Canonical Correlation Analysis (DCCA), we proposed an end-to-end network (DCCA-Net) for scene change detection. DCCA-Net firstly utilizes a pretrained Convolutional Neural Network (CNN) to extract high-dimensional features of the input scene pairs. Then, the DCCA module is deployed to project the extracted features into a new feature space and maximize the correlation of the projected feature pairs. Finally, based on the transformed feature vectors, the semantic label of each scene image could be obtained by a softmax classifier. The binary scene changes could be obtained using a binary classifier based on the transformed features. The objective function of DCCA is integrated into the loss function of classification and binary change detection, so that they could be optimized simultaneously. We implemented the proposed network and performed experiments on a VHR remote sensing imagery dataset (Multi-temporal Scene - Wuhan, Mts-WH) for scene change detection. The experimental results have shown the effect of the DCCA-Net, and our method could outperform other conventional scene change detection methods and the baseline methods based on CNN.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"786 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":"116132205","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: 5

Investigating GF-5 Hyperspectral and GF-1 Multispectral Data Fusion Methods for Multitemporal Change Analysis 研究GF-5高光谱和GF-1多光谱数据融合方法在多时间变化分析中的应用

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866908

Weiwei Sun, Kai Ren, Gang Yang, Xiangchao Meng, Yinnian Liu

{"title":"Investigating GF-5 Hyperspectral and GF-1 Multispectral Data Fusion Methods for Multitemporal Change Analysis","authors":"Weiwei Sun, Kai Ren, Gang Yang, Xiangchao Meng, Yinnian Liu","doi":"10.1109/Multi-Temp.2019.8866908","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866908","url":null,"abstract":"Multitemporal change analysis is one of the essential purposes for discovering knowledge from various remote sensing terrestrial earth observation techniques. Particularly, the China Gaofen-5 (GF-5) hyperspectral imager provides a new data source for multitemporal change analysis. Its 330 bands, 60 km swath width and 5–10 nm spectrum resolutions make it captures subtle changes in spectrum responses of ground objects across different images. Unfortunately, its 30 spatial resolution still hinders its accurate geospatial location in some specific applications. Therefore, we explore state-of-the-art data fusion methods and seek an appropriate fusing method of GF-5 hyperspectral and GF-1 multispectral data to benefit multitemporal change analysis. We utilize four image fusion methods and implement six evaluation criteria to holistically evaluate the performance of different methods. Experimental results on three datasets of Taihu Lake and Poyang Lake in China show that the Modulation transfer functions-generalized Laplacian pyramid (MTF-GLP) has smaller spectral distortion, better spatial fidelity and requires moderate computational time than the other three methods. It accordingly can be a good choice for fusing GF-5 and GF-1 remote sensing data in both classification and multitemporal change analysis.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"31 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":"125550740","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

Building Change Detection using Coherent and Incoherent Features from Multitemporal SAR Images 基于多时相SAR图像相干和非相干特征的建筑变化检测

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866968

Hao Feng, Lu Zhang, M. Liao

引用次数: 0

Estimation of Maize Yield in Yitong County based on Multi-source Remote Sensing Data from 2007 to 2017 基于多源遥感数据的伊通县2007 - 2017年玉米产量估算

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866845

Yibo Wang, Xue Wang, Kun Tan, Yu Chen, Kailei Xu

{"title":"Estimation of Maize Yield in Yitong County based on Multi-source Remote Sensing Data from 2007 to 2017","authors":"Yibo Wang, Xue Wang, Kun Tan, Yu Chen, Kailei Xu","doi":"10.1109/Multi-Temp.2019.8866845","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866845","url":null,"abstract":"With the development of remote sensing technology, the utilizations of multi-spatial and multispectral resolution remote images have proved to be very important in monitoring the growth and estimating the yield of agricultural crops. The light energy utilization models using remote sensing have got the wide application because of its simple data acquisition, less parameters and capabilities for time series analysis. In this research, the yield estimation has been carried out using the net primary productivity (NPP) and the contents of soil organic matter which are obtained by Carnegie-Ames-Stanford approach (CASA) model and our proposed approach respectively. More specifically, NPP of maize in the study area from 2007 to 2017 was estimated using CASA model, and the characters of spatio-temporal variation were explored. After that, the retrieval model of the soil organic matter content was established based on the relationship analyzation between the soil organic content and NPP. The characters of spatio-temporal variation also have been explored. Then the yield of spring maize in Yitong County from 2007 to 2017 was estimated using an improved yield estimation model. Moreover, the maize harvest index and the yield of maize per unit area in the study area were obtained. Finally, the growth and development information of maize in Yitong County were comprehensively evaluated combining with these mentioned data.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"9 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120972484","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

Unsupervised deep learning based change detection in Sentinel-2 images 基于无监督深度学习的Sentinel-2图像变化检测

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866899

Sudipan Saha, Yady Tatiana Solano Correa, F. Bovolo, L. Bruzzone

{"title":"Unsupervised deep learning based change detection in Sentinel-2 images","authors":"Sudipan Saha, Yady Tatiana Solano Correa, F. Bovolo, L. Bruzzone","doi":"10.1109/Multi-Temp.2019.8866899","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866899","url":null,"abstract":"Change Detection (CD) is an important application of remote sensing. Recent technological evolution resulted in the availability of optical multispectral sensors that provide High spatial Resolution (HR) images with many spectral bands. Such characteristics allow for new applications of CD, however present new challenges on the proper exploitation of the information. HR multitemporal data processing is challenging due to spatial correlation of pixels and spatial context information needs to be exploited to benefit from multitemporal HR images. Moreover most of the state-of-the-art CD methods exploit single or couple of spectral channels from the optical sensors to derive CD map. To overcome these challenges, this paper presents a novel unsupervised deep-learning based method that can effectively model contextual information and handle all the bands in multispectral images. In particular, we focus on the Sentinel-2 images provided by the European Space Agency (ESA) that provides both higher spatial and temporal resolution optical images with 13 spectral bands with respect to previous generation sensors. Experimental results on the urban Onera satellite CD (OSCD) dataset and on agricultural multitemporal images from Barrax, Spain confirms the effectiveness of the proposed method.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"63 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":"124578880","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: 7

Exploring Data Quantity Requirements for Domain Adaptation in the Classification of Satellite Image Time Series 探索卫星图像时间序列分类中领域自适应的数据量需求

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) Pub Date : 2019-08-01 DOI: 10.1109/Multi-Temp.2019.8866898

Benjamin Lucas, Charlotte Pelletier, J. Inglada, Daniel F. Schmidt, Geoffrey I. Webb, F. Petitjean

{"title":"Exploring Data Quantity Requirements for Domain Adaptation in the Classification of Satellite Image Time Series","authors":"Benjamin Lucas, Charlotte Pelletier, J. Inglada, Daniel F. Schmidt, Geoffrey I. Webb, F. Petitjean","doi":"10.1109/Multi-Temp.2019.8866898","DOIUrl":"https://doi.org/10.1109/Multi-Temp.2019.8866898","url":null,"abstract":"Land cover maps are a vital input variable in all types of environmental research and management. However the modern state-of-the-art machine learning techniques used to create them require substantial training data to produce optimal accuracy. Domain Adaptation is one technique researchers might use when labelled training data are unavailable or scarce. This paper looks at the result of training a convolutional neural network model on a region where data are available (source domain), and then adapting this model to another region (target domain) by retraining it on the available labelled data, and in particular how these results change with increasing data availability. Our experiments performing domain adaptation on satellite image time series, draw three interesting conclusions: (1) a model trained only on data from the source domain delivers 73.0% test accuracy on the target domain; (2) when all of the weights are retrained on the target data, over 16,000 instances were required to improve upon the accuracy of the source-only model; and (3) even if sufficient data is available in the target domain, using a model pretrained on a source domain will result in better overall test accuracy compared to a model trained on target domain data only—88.9% versus 84.7%.","PeriodicalId":106790,"journal":{"name":"2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","volume":"70 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":"117127729","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: 4