2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)最新文献_第7页

Analysis of NOAA/AVHRR multitemporal images, climate conditions and cultivated land of sugarcane fields applied to agricultural monitoring 用于农业监测的NOAA/AVHRR多时相影像、气候条件和甘蔗田耕地分析

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005090

R. R. V. Gonçalves, J. Zullo, C. S. Ferraresso, E. P. M. Sousa, L. A. Romani, A. J. Traina

引用次数: 4

Greenland inland ice melt-off: Analysis of global gravity data from the GRACE satellites 格陵兰内陆冰融化:GRACE卫星全球重力数据分析

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005074

A. Nielsen, O. Andersen, P. Svendsen

引用次数: 1

A method for change detection with multi-temporal satellite images based on Principal Component Analysis 基于主成分分析的多时相卫星图像变化检测方法

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005082

C. Bustos, Osvaldo Campanella, K. Kpalma, F. Magnago, J. Ronsin

引用次数: 12

Land cover classification by using multi-temporal COSMO-SkyMed data 基于cosmos - skymed多时相数据的土地覆盖分类

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005036

G. Satalino, D. Impedovo, A. Balenzano, F. Mattia

{"title":"Land cover classification by using multi-temporal COSMO-SkyMed data","authors":"G. Satalino, D. Impedovo, A. Balenzano, F. Mattia","doi":"10.1109/MULTI-TEMP.2011.6005036","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005036","url":null,"abstract":"The objective of this paper is to report on the crop classification activities carried out during the first year of the Italian project “Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites” (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains. In particular, the objective of the classification activities is to assess the potential of multi-temporal series of X-band COSMO-SkyMed SAR data for crop classification. The selected agricultural site is located in the Capitanata plain close to the Foggia town (Puglia region, Southern Italy). Over this area, 8 Stripmap PingPong COSMO Sky-Med images at HH/HV polarization and at low incidence angle were acquired from April to August 2010. In the paper, a classification scheme based on the Maximum Likelihood algorithm is applied to the multi-temporal data set and its accuracy is assessed with respect to a reference map obtained by means of SPOT data.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858200","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: 10

Analytical description of pseudo-invariant features (PIFs) 伪不变特征(pif)的解析描述

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005046

W. Philpot, T. Ansty

引用次数: 7

Monitoring environmental change in the Andes based on SPOT-VGT and NOAA-AVHRR time series analysis 基于SPOT-VGT和NOAA-AVHRR时间序列分析的安第斯山脉环境变化监测

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005100

C. Toté, Katia Beringhs, E. Swinnen, Gerard Govers

引用次数: 5

A multilevel approach to change detection for port surveillance with very high resolution SAR images 基于高分辨率SAR图像的港口监视变化检测多级方法

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005034

F. Bovolo, C. Marín, L. Bruzzone

引用次数: 4

Classification of dynamic evolutions from satellitar image time series based on similarity measures 基于相似性测度的卫星影像时间序列动态演化分类

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005068

C. Vaduva, T. Costachioiu, C. Patrascu, I. Gavat, V. Lazarescu, M. Datcu

引用次数: 2

Comparison of two remote sensing time series analysis methods for monitoring forest decline 森林衰退监测的两种遥感时间序列分析方法比较

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005056

J. Lambert, A. Jacquin, J. Denux, V. Chéret

引用次数: 9

Effect of the learning algorithm on the accuracy of sub-pixel land use classifications with multilayer perceptrons 学习算法对多层感知器亚像元土地利用分类精度的影响

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005081

Stien Heremans, J. Van Orshoven

{"title":"Effect of the learning algorithm on the accuracy of sub-pixel land use classifications with multilayer perceptrons","authors":"Stien Heremans, J. Van Orshoven","doi":"10.1109/MULTI-TEMP.2011.6005081","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005081","url":null,"abstract":"Timely and accurate information on the location and the extent of land use types is high up the agenda of several governmental and scientific organizations. Remote sensing, through image classification at the sub-pixel level, is an attractive source of this type of information. The remote sensing community has recognized the multilayer perceptron (MLP) as a popular machine learning technique for performing land use classifications, both at the pixel and at the sub-pixel level. However, theoretical advances in the machine learning community are not easily adopted by the classification practice. An example is the continued use of the gradient descent algorithm for MLP training. In this paper, the accuracy of this standard first order learning algorithm was compared to that of five alternative, second order learning algorithms for performing a sub-pixel classification of land use in Flanders. The result are clear: all second order algorithms perform markedly better than gradient descent, thereby illustrating the importance of translating theoretical advances in MLP training to the classification practice.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127178718","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