基于遥感大数据的信息提取技术综述

刘伟权; 王程; 臧彧; 胡倩; 于尚书; 赖柏锜

doi:10.11959/j.issn.2096-0271.2022014

您当前的位置：

首页 >

文章列表页 >

基于遥感大数据的信息提取技术综述

专题：空天大数据 | 更新时间：2024-06-03

- 基于遥感大数据的信息提取技术综述
- A survey on information extraction technology based on remote sensing big data
- 大数据 2022年8卷第2期页码：28-57
- 作者机构：
  
  1. 厦门大学信息学院，福建厦门 361005
  2. 福建省智慧城市感知与计算重点实验室，福建厦门 361005
- 作者简介：
  
  [ "刘伟权（1990- ），男，博士，厦门大学信息学院博士后、特任副研究员，主要研究方向为三维视觉、空间数据科学、增强现实、遥感数据处理" ]
  [ "王程（1975- ），男，博士，厦门大学信息学院教授、博士生导师，主要研究方向为三维视觉、空间大数据分析、激光雷达、虚拟/增强现实、遥感数据处理" ]
  [ "臧彧（1985- ），男，博士，厦门大学信息学院副教授，主要研究方向为空间感知与智能计算、三维视觉、人工智能、遥感数据处理" ]
  [ "胡倩（1997- ），女，厦门大学信息学院硕士生，主要研究方向为三维视觉" ]
  [ "于尚书（1993- ），男，厦门大学信息学院博士生，主要研究方向为三维视觉" ]
  [ "赖柏锜（1997- ），男，厦门大学信息学院硕士生，主要研究方向为三维视觉" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61971363);国家自然科学基金资助项目;The National Natural Science Foundation of China(U1605254);中国博士后科学基金资助项目;China Postdoctoral Science Foundation(2021M690094)
- DOI：10.11959/j.issn.2096-0271.2022014
  中图分类号： TP75
- 网络首发：2022-03，
  
  纸质出版：2022-03-15
- 稿件说明：
移动端阅览
刘伟权, 王程, 臧彧, 等. 基于遥感大数据的信息提取技术综述[J]. 大数据, 2022,8(2):28-57.

Weiquan LIU, Cheng WANG, Yu ZANG, et al. A survey on information extraction technology based on remote sensing big data[J]. Big data research, 2022, 8(2): 28-57.
刘伟权, 王程, 臧彧, 等. 基于遥感大数据的信息提取技术综述[J]. 大数据, 2022,8(2):28-57. DOI： 10.11959/j.issn.2096-0271.2022014.

Weiquan LIU, Cheng WANG, Yu ZANG, et al. A survey on information extraction technology based on remote sensing big data[J]. Big data research, 2022, 8(2): 28-57. DOI： 10.11959/j.issn.2096-0271.2022014.

摘要

随着遥感技术的快速发展，我国已建立了比较完善的航天遥感和灵活多样的航空遥感数据获取体系。遥感大数据以海量遥感数据为主，综合了其他多源遥感数据，并运用大数据思维与手段，发掘海量数据中的知识规律和高价值信息。回顾了近年来基于遥感大数据的信息提取技术研究工作，从遥感目标检测、遥感地物分割、遥感变化检测三方面阐述了遥感信息提取技术的发展历程，对各个发展阶段及代表性方法进行了梳理与归纳，并对基于遥感大数据的信息提取技术进行了展望。

Abstract

With the rapid development of remote sensing technology

our country has established a relatively complete space remote sensing and flexible and diverse aerial remote sensing data acquisition system.Remote sensing big data is mainly based on massive remote sensing data

integrating other multi-source remote sensing data

using big data thinking and methods

and discovering knowledge laws and high-value information in massive data.Firstly

the research work of information extraction technology based on remote sensing big data was reviewed in recent years.Secondly

the development history of remote sensing information extraction technology was expounded from three aspects: remote sensing target detection

remote sensing surface object segmentation

and remote sensing change detection.Finally

the information extraction technology based on remote sensing big data was sorted out

summarized and prospected.

关键词

Keywords

references

张兵 . 遥感大数据时代与智能信息提取 [J ] . 武汉大学学报·信息科学版 , 2018 , 43 ( 12 ): 1861 - 1871 .

ZHANG B . Remotely sensed big data era and intelligent information extraction [J ] . Geomatics and Information Science of Wuhan University , 2018 , 43 ( 12 ): 1861 - 1871 .

邹同元 , 丁火平 , 王玮哲 , 等 . 天基遥感大数据人工智能应用探讨 [J ] . 卫星应用 , 2019 ( 6 ): 38 - 44 .

ZOU T Y , DING H P , WANG W Z , et al . Discussion on artificial intelligence application of space-based remote sensing big data [J ] . Satellite Application , 2019 ( 6 ): 38 - 44 .

ZHANG B , CHEN Z C , PENG D L , et al . Remotely sensed big data:evolution in model development for information extraction[point of view] [J ] . Proceedings of the IEEE , 2019 , 107 ( 12 ): 2294 - 2301 .

SWAIN P H , DAVIS S M . Remote sensing:the quantitative approach [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 1981 , PAMI-3 ( 6 ): 713 - 714 .

BENZ U C , HOFMANN P , WILLHAUCK G , et al . Multi-resolution,object-oriented fuzzy analysis of remote sensing data for GIS-ready information [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2004 , 58 ( 3/4 ): 239 - 258 .

DUBES R C , JAIN A K . Random field models in image analysis [J ] . Journal of Applied Statistics , 1989 , 16 ( 2 ): 131 - 164 .

FRIEDL M A , BRODLEY C E . Decision tree classification of land cover from remotely sensed data [J ] . Remote Sensing of Environment , 1997 , 61 ( 3 ): 399 - 409 .

谭玉敏 , 槐建柱 , 唐中实 . 一种边界引导的多尺度高分辨率遥感图像分割方法 [J ] . 红外与毫米波学报 , 2010 , 29 ( 4 ): 312 - 315 .

TAN Y M , HUAI J Z , TANG Z S . Edgeguided segmentation method for multiscale and high resolution remote sensing image [J ] . Journal of Infrared and Millimeter Waves , 2010 , 29 ( 4 ): 312 - 315 .

HANG R L , LIU Q S , HONG D F , et al . Cascaded recurrent neural networks for hyperspectral image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 8 ): 5384 - 5394 .

王明常 , 朱春宇 , 陈学业 , 等 . 基于FPN ResUNet的高分辨率遥感影像建筑物变化检测 [J ] . 吉林大学学报(地球科学版) , 2021 , 51 ( 1 ): 296 - 306 .

WANG M C , ZHU C Y , CHEN X Y , et al . Building change detectionin high resolution remote sensing images based on FPN ResUNet [J ] . Journal of Jilin University (Earth Science Edition) , 2021 , 51 ( 1 ): 296 - 306 .

孙显 , 王智睿 , 孙元睿 , 等 . AIR-SARShip-1.0：高分辨率SAR舰船检测数据集 [J ] . 雷达学报 , 2019 , 8 ( 6 ): 852 - 862 .

SUN X , WANG Z R , SUN Y R , et al . AIRSARShip-1.0:high-resolution SAR ship detection dataset [J ] . Journal of Radars , 2019 , 8 ( 6 ): 852 - 862 .

LI K , WAN G , CHENG G , et al . Object detection in optical remote sensing images:a survey and a new benchmark [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 159 : 296 - 307 .

HE K M , SUN J , TANG X O . Single image haze removal using dark channel prior [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2011 , 33 ( 12 ): 2341 - 2353 .

LECUN Y , BOTTOU L , BENGIO Y , et al . Gradient-based learning applied to document recognition [J ] . Proceedings of the IEEE , 1998 , 86 ( 11 ): 2278 - 2324 .

MCKEOWN D M , DENLINGER J L . Cooperative methods for road tracking in aerial imagery [C ] // Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 1988 : 662 - 672 .

ZHOU J , BISCHOF W F , CAELLI T . Road tracking in aerial images based on human-computer interaction and Bayesian filtering [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2006 , 61 ( 2 ): 108 - 124 .

KIM T , PARK S R , KIM M G , et al . Tracking road centerlines from high resolution remote sensing images by least squares correlation matching [J ] . Photogrammetric Engineering ＆ Remote Sensing , 2004 , 70 ( 12 ): 1417 - 1422 .

ZHANG J X , LIN X G , LIU Z J , et al . Semiautomatic road tracking by template matching and distance transformation in urban areas [J ] . International Journal of Remote Sensing , 2011 , 32 ( 23 ): 8331 - 8347 .

FISCHLER M A , ELSCHLAGER R A . The representation and matching of pictorial structures [J ] . IEEE Transactions on Computers , 1973 , C-22 ( 1 ): 67 - 92 .

JAIN A K , ZHONG Y , DUBUISSONJOLLY M P . Deformable template models:a review [J ] . Signal Processing , 1998 , 71 ( 2 ): 109 - 129 .

PENG J , ZHANG D , LIU Y C . An improved snake model for building detection from urban aerial images [J ] . Pattern Recognition Letters , 2005 , 26 ( 5 ): 587 - 595 .

NIU X T . A semi-automatic framework for highway extraction and vehicle detection based on a geometric deformable model [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2006 , 61 ( 3/4 ): 170 - 186 .

LIU G , SUN X , FU K , et al . Interactive geospatial object extraction in high resolution remote sensing images using shape-based global minimization active contour model [J ] . Pattern Recognition Letters , 2013 , 34 ( 10 ): 1186 - 1195 .

XU C F , DUAN H B . Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for lowaltitude aircraft [J ] . Pattern Recognition Letters , 2010 , 31 ( 13 ): 1759 - 1772 .

邓志鹏 . 基于深度卷积神经网络的遥感图像目标检测方法研究 [D ] . 长沙:国防科技大学 , 2019 .

DENG Z P . Research on deep convolutional neural network based object detection methods in remote sensing images [D ] . Changsha:National University of Defense Technology , 2019 .

TRINDER J C , WANG Y D . Knowledgebased road interpretation in aerial images [Z ] . 1998 .

HUERTAS A , NEVATIA R . Detecting buildings in aerial images [J ] . Computer Vision,Graphics,and Image Processing , 1988 , 41 ( 2 ): 131 - 152 .

WEIDNER U , FÖRSTNER W , . Towards automatic building extraction from highresolution digital elevation models [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 1995 , 50 ( 4 ): 38 - 49 .

MCGLONE J C , SHUFELT J A . Projective and object space geometry for monocular building extraction [C ] // Proceedings of 1994 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 1994 : 54 - 61 .

IRVIN R B , MCKEOWN D M . Methods for exploiting the relationship between buildings and their shadows in aerial imagery [J ] . IEEE Transactions on Systems,Man,and Cybernetics , 1989 , 19 ( 6 ): 1564 - 1575 .

LIOW Y T , PAVLIDIS T . Use of shadows for extracting buildings in aerial images [J ] . Computer Vision,Graphics,and Image Processing , 1990 , 49 ( 2 ): 242 - 277 .

OK A O . Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2013 , 86 : 21 - 40 .

LIN C G , NEVATIA R . Building detection and description from a single intensity image [J ] . Computer Vision and Image Understanding , 1998 , 72 ( 2 ): 101 - 121 .

PENG J , LIU Y C . Model and contextdriven building extraction in dense urban aerial images [J ] . International Journal of Remote Sensing , 2005 , 26 ( 7 ): 1289 - 1307 .

DALAL N , TRIGGS B . Histograms of oriented gradients for human detection [C ] // Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2005 : 886 - 893 .

ZHANG W C , SUN X , FU K , et al . Object detection in high-resolution remote sensing images using rotation invariant parts based model [J ] . IEEE Geoscience and Remote Sensing Letters , 2014 , 11 ( 1 ): 74 - 78 .

SHI Z W , YU X R , JIANG Z G , et al . Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2014 , 52 ( 8 ): 4511 - 4523 .

ZHANG W C , SUN X , WANG H Q , et al . A generic discriminative part-based model for geospatial object detection in optical remote sensing images [J ] . I S PR S Jo u r n a l of Photogrammetry and Remote Sensing , 2015 , 99 : 30 - 44 .

FEI-FEI L , PERONA P . A Bayesian hierarchical model for learning natural scene categories [C ] // Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2005 : 524 - 531 .

LI Q P , MOU L C , XU Q Z , et al . R3-Net:a deep network for multioriented vehicle detection in aerial images and videos [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 7 ): 5028 - 5042 .

LOWE D G . Distinctive image features from scale-invariant keypoints [J ] . International Journal of Computer Vision , 2004 , 60 ( 2 ): 91 - 110 .

LAZEBNIK S , SCHMID C , PONCE J . Beyond bags of features:spatial pyramid matching for recognizing natural scene categories [C ] // Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2006 : 2169 - 2178 .

BAI X , ZHANG H G , ZHOU J . VHR object detection based on structural feature extraction and query expansion [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2014 , 52 ( 10 ): 6508 - 6520 .

GIRSHICK R , DONAHUE J , DARRELL T , et al . Rich feature hierarchies for accurate object detection and semantic segmentation [C ] // Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2014 : 580 - 587 .

CHENG G , ZHOU P C , HAN J W . Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 12 ): 7405 - 7415 .

CHENG G , HAN J W , ZHOU P C , et al . Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection [J ] . IEEE Transactions on Image Processing , 2019 , 28 ( 1 ): 265 - 278 .

LONG Y , GONG Y P , XIAO Z F , et al . Accurate object localization in remote sensing images based on convolutional neural networks [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2017 , 55 ( 5 ): 2486 - 2498 .

REN S Q , HE K M , GIRSHICK R , et al . Faster R-CNN:towards real-time object detection with region proposal networks [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 6 ): 1137 - 1149 .

DENG Z P , SUN H , ZHOU S L , et al . Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2017 , 10 ( 8 ): 3652 - 3664 .

DING J , XUE N , LONG Y , et al . Learning RoI Transformer for oriented object detection in aerial images [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2019 : 2844 - 2853 .

XU Y C , FU M T , WANG Q M , et al . Gliding vertex on the horizontal bounding box for multi-oriented object detection [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021 , 43 ( 4 ): 1452 - 1459 .

XIA G S , BAI X , DING J , et al . DOTA:a large-scale dataset for object detection in aerial images [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 3974 - 3983 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once:unified,real-time object detection [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 779 - 788 .

VAN ETTEN A . You only look twice:rapid multi-scale object detection in satellite imagery [J ] . arXiv preprint,2018,arXiv:1805.09512 .

YANG X , LIU Q Q , YAN J C , et al . R3Det:refined single-stage detector with feature refinement for rotating object [J ] . arXiv preprint,2019,arXiv:1908.05612 .

LIU W , ANGUELOV D , ERHAN D , et al . SSD:single shot multibox detector [C ] // Proceedings of 2016 European Conference on Computer Vision . Heidelberg:Springer , 2016 : 21 - 37 .

LIU L , PAN Z X , LEI B . Learning a rotation invariant detector with rotatable bounding box [J ] . arXiv preprint,2017,arXiv:1711.09405 .

KAISER P , WEGNER J D , LUCCHI A , et al . Learning aerial image segmentation from online maps [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2017 , 55 ( 11 ): 6054 - 6068 .

JI S P , WEI S Q , LU M . Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 1 ): 574 - 586 .

CHEN B , QIU F , WU B F , et al . Image segmentation based on constrained spectral variance difference and edge penalty [J ] . Remote Sensing , 2015 , 7 ( 5 ): 5980 - 6004 .

JAIN R C , KASTURI R , SCHUNCK B G . Machine vision [M ] . New York : McGrawHill,Inc. , 1995 .

FOSGATE C H , KRIM H , IRVING W W , et al . Multiscale segmentation and anomaly enhancement of SAR imagery [J ] . IEEE Transactions on Image Processing , 1997 , 6 ( 1 ): 7 - 20 .

VINCENT L , SOILLE P . Watersheds in digital spaces:an efficient algorithm based on immersion simulations [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 1991 , 13 ( 6 ): 583 - 598 .

肖鹏峰 , 冯学智 , 赵书河 , 等 . 基于相位一致的高分辨率遥感图像分割方法 [J ] . 测绘学报 , 2007 , 36 ( 2 ): 146 - 151 , 186 .

XIAO P F , FENG X Z , ZHAO S H , et al . Segmentation of high-resolution remotely sensed imagery based on phase congruency [J ] . Acta Geodaetica et Cartographica Sinica , 2007 , 36 ( 2 ): 146151 ,186.

DRǍGUŢ L , TIEDE D , LEVICK S R . ESP:a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data [J ] . International Journal of Geographical Information Science , 2010 , 24 ( 6 ): 859 - 871 .

陈忠 , 赵忠明 . 基于区域生长的多尺度遥感图像分割算法 [J ] . 计算机工程与应用 , 2005 , 41 ( 35 ): 7 - 9 .

CHEN Z , ZHAO Z M . A multi-scale remote sensing image segmentation algorithm based on region growing [J ] . Computer Engineering and Applications , 2005 , 41 ( 35 ): 7 - 9 .

WANG Z W , JENSEN J R , IM J . An automatic region-based image segmentation algorithm for remote sensing applications [J ] . Environmental Modelling＆ Software , 2010 , 25 ( 10 ): 1149 - 1165 .

NEUBERT M , HEROLD H . Assessment of remote sensing image segmentation quality [Z ] . 2008 .

MICHEL J , YOUSSEFI D , GRIZONNET M . Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2015 , 53 ( 2 ): 952 - 964 .

ZANOTTA D C , ZORTEA M , FERREIRA M P . A supervised approach for simultaneous segmentation and classification of remote sensing images [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2018 , 142 : 162 - 173 .

WANG M , LI R X . Segmentation of high spatial resolution remote sensing imagery based on hard-boundary constraint and two-stage merging [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2014 , 52 ( 9 ): 5712 - 5725 .

MUELLER M , SEGL K , KAUFMANN H . Edge- and region-based segmentation technique for the extraction of large,man-made objects in high-resolution satellite imagery [J ] . Pattern Recognition , 2004 , 37 ( 8 ): 1619 - 1628 .

WEIDNER U . Contribution to the assessment of segmentation quality for remote sensing applications [J ] . International Archives of Photogrammetry,Remote Sensing and Spatial Information Sciences , 2008 , 37 ( B7 ): 479 - 484 .

JOHNSON B , XIE Z X . Unsupervised image segmentation evaluation and refinement using a multi-scale approach [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2011 , 66 ( 4 ): 473 - 483 .

WANG Y J , MENG Q Y , QI Q W , et al . Region merging considering withinand between-segment heterogeneity:an improved hybrid remote-sensing image segmentation method [J ] . Remote Sensing , 2018 , 10 ( 5 ): 781 .

MITRA P , SHANKAR B U , PAL S K . Segmentation of multispectral remote sensing images using active support vector machines [J ] . Pattern Recognition Letters , 2004 , 25 ( 9 ): 1067 - 1074 .

KAMPFFMEYER M , SALBERG A B , JENSSEN R . Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops . Piscataway:IEEE Press , 2016 : 680 - 688 .

NOGUEIRA K , DALLA MURA M , CHANUSSOT J , et al . Dynamic multicontext segmentation of remote sensing images based on convolutional networks [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 10 ): 7503 - 7520 .

耿艳磊 , 陶超 , 沈靖 , 等 . 高分辨率遥感影像语义分割的半监督全卷积网络法 [J ] . 测绘学报 , 2020 , 49 ( 4 ): 499 - 508 .

GENG Y L , TAO C , SHEN J , et al . Highresolution remote sensing image semantic segmentation based on semi-supervised full convolution network method [J ] . Acta Geodaetica et Cartographica Sinica , 2020 , 49 ( 4 ): 499 - 508 .

苏健民 , 杨岚心 , 景维鹏 . 基于U-Net的高分辨率遥感图像语义分割方法 [J ] . 计算机工程与应用 , 2019 , 55 ( 7 ): 207 - 213 .

SU J M , YANG L X , JING W P . U-Net based semantic segmentation method for high resolution remote sensing image [J ] . Computer Engineering and Applications , 2019 , 55 ( 7 ): 207 - 213 .

SINGH A . Review article digital change detection techniques using remotelysensed data [J ] . International Journal of Remote Sensing , 1989 , 10 ( 6 ): 989 - 1003 .

ZELINSKI M E , HENDERSON J , SMITH M . Use of landsat 5 for change detection at 1998 Indian and Pakistani nuclear test sites [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2014 , 7 ( 8 ): 3453 - 3460 .

苏伟 , 朱德海 , 苏鸣宇 , 等 . 基于时序LAI的地块尺度玉米长势监测方法 [J ] . 资源科学 , 2019 , 41 ( 3 ): 601 - 611 .

SU W , ZHU D H , SU M Y , et al . Fieldscale corn growth monitoring using time series LAI [J ] . Resources Science , 2019 , 41 ( 3 ): 601 - 611 .

王利民 , 刘佳 , 姚保民 , 等 . 基于GF-1影像NDVI年度间相关分析的冬小麦面积变化监测 [J ] . 农业工程学报 , 2018 , 34 ( 8 ): 184 - 191 .

WANG L M , LIU J , YAO B M , et al . Area change monitoring of winter wheat based on relationship analysis of GF-1 NDVI among different years [J ] . Transactions of the Chinese Society of Agricultural Engineering , 2018 , 34 ( 8 ): 184 - 191 .

GANDHI G M , PARTHIBAN S , THUMMALU N , et al . NDVI:vegetation change detection using remote sensing and GIS - a case study of Vellore district [J ] . Procedia Computer Science , 2015 , 57 : 1199 - 1210 .

MUNYATI C . Wetland change detection on the Kafue Flats,Zambia,by classification of a multitemporal remote sensing image dataset [J ] . International Journal of Remote Sensing , 2000 , 21 ( 9 ): 1787 - 1806 .

张增 , 王兵 , 伍小洁 , 等 . 无人机森林火灾监测中火情检测方法研究 [J ] . 遥感信息 , 2015 , 30 ( 1 ): 107 - 110 , 124 .

ZHANG Z , WANG B , WU X J , et al . An algorithm of forest fire detection based on UAV remote sensing [J ] . Remote Sensing Information , 2015 , 30 ( 1 ): 107 - 110 , 124 .

付迎春 , 速云中 , 钟小君 . 基于MODIS遥感影像的森林火灾火点检测方法 [J ] . 华南师范大学学报(自然科学版) , 2008 , 40 ( 3 ): 112 - 118 .

FU Y C , SU Y Z , ZHONG X J . Automatic extraction of information on small cool fires based on MODIS imagery [J ] . Journal of South China Normal University (Natural Science Edition) , 2008 , 40 ( 3 ): 112 - 118 .

高新平 . 基于RS/GIS集成技术的洪水灾情估算研究 [J ] . 人民黄河 , 2011 , 33 ( 1 ): 3 - 5 .

GAO X P . Research on flood disaster estimation based on integrated technology [J ] . Yellow River , 2011 , 33 ( 1 ): 3 - 5 .

赵旦 , 张淼 , 于名召 , 等 . 汶川地震灾后农田和森林植被恢复遥感监测 [J ] . 遥感学报 , 2014 , 18 ( 4 ): 958 - 970 .

ZHAO D , ZHANG M , YU M Z , et al . Monitoring agriculture and forestry recovery after the Wenchuan Earthquake [J ] . Journal of Remote Sensing , 2014 , 18 ( 4 ): 958 - 970 .

LI H X , XIAO P F , FENG X Z , et al . Using land long-term data records to map land cover changes in China over 1981–2010 [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2017 , 10 ( 4 ): 1372 - 1389 .

赵敏 , 陈卫平 , 王海燕 . 基于遥感影像变化检测技术的地形图更新 [J ] . 测绘通报 , 2013 ( 4 ): 65 - 67 .

ZHAO M , CHEN W P , WANG H Y . Updating of topographic maps based on change detection for remote sensing image [J ] . Bulletin of Surveying and Mapping , 2013 ( 4 ): 65 - 67 .

CHEN H , ZHANG K , XIAO W , et al . Building change detection in very highresolution remote sensing image based on pseudo-orthorectification [J ] . International Journal of Remote Sensing , 2021 , 42 ( 7 ): 2686 - 2705 .

王民水 , 孔祥明 , 陈学业 , 等 . 基于随机补片和DeepLabV3+的建筑物遥感图像变化检测 [J ] . 吉林大学学报(地球科学版) , 2021 , 51 ( 6 ): 1932 - 1938 .

WANG M S , KONG X M , CHEN X Y , et al . Remote sensing image change detection based on random patches and DeepLabV3+ network [J ] . Journal of Jilin University (Earth Science Edition) , 2021 , 51 ( 6 ): 1932 - 1938 .

BENEDEK C , SZIRANYI T . Change detection in optical aerial images by a multilayer conditional mixed Markov model [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2009 , 47 ( 10 ): 3416 - 3430 .

DAUDT R C , LE SAUX B , BOULCH A , et al . Urban change detection for multispectral earth observation using convolutional neural networks [C ] // Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium . Piscataway:IEEE Press , 2018 : 2115 - 2118 .

马建文 , 田国良 , 王长耀 , 等 . 遥感变化检测技术发展综述 [J ] . 地球科学进展 , 2004 , 19 ( 2 ): 192 - 196 .

MA J W , TIAN G L , WANG C Y , et al . Review of the development of remote sensing change detection technology [J ] . Advance in Earth Sciences , 2004 , 19 ( 2 ): 192 - 196 .

吴柯 , 何坦 , 杨叶涛 . 基于混合像元分解与EM算法的中低分辨率遥感影像变化检测 [J ] . 武汉大学学报·信息科学版 , 2019 , 44 ( 4 ): 555 - 562 .

WU K , HE T , YANG Y T . Change detection method based on pixel unmixing and EM algorithm for low and medium resolution remote sensing imagery [J ] . Geomatics and Information Science of Wuhan University , 2019 , 44 ( 4 ): 555 - 562 .

USMAN M , LIEDL R , SHAHID M A , et al . Land use/land cover classification and its change detection using multi temporal MODIS NDVI data [J ] . Journal of Geographical Sciences , 2015 , 25 ( 12 ): 1479 - 1506 .

WEN D W , HUANG X , ZHANG L P , et al . A novel automatic change detection method for urban high-resolution remotely sensed imagery based on multiindex scene representation [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 1 ): 609 - 625 .

ZHOU Z J , MA L , FU T Y , et al . Change detection in coral reef environment using high-resolution images:comparison of object-based and pixel-based paradigms [J ] . ISPRS International Journal of Geo-Information , 2018 , 7 ( 11 ): 441 .

WANG Q , YUAN Z H , DU Q , et al . GETNET:a general end-to-end 2-D CNN framework for hyperspectral image change detection [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 1 ): 3 - 13 .

LÓPEZ-FANDIÑO J , GAREA A S , HERAS D B , et al . Stacked autoencoders for multiclass change detection in hyperspectral images [C ] // Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium . Piscataway:IEEE Press , 2018 : 1906 - 1909 .

ABD EL-KAWY O R , RØD J K , ISMAIL H A , et al . Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data [J ] . Applied Geography , 2011 , 31 ( 2 ): 483 - 494 .

YUAN F , SAWAYA K E , LOEFFELHOLZ B C , et al . Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing [J ] . Remote Sensing of Environment , 2005 , 98 ( 2/3 ): 317 - 328 .

胡蕾 , 江宇 , 李进 , 等 . 一种多尺度稀疏卷积的高分辨率遥感图像变化检测方法 [J ] . 小型微型计算机系统 , 2020 , 41 ( 11 ): 2365 - 2370 .

HU L , JIANG Y , LI J , et al . Change detection method for high-resolution remote sensing image based on multiscale and sparse convolution [J ] . Journal of Chinese Computer Systems , 2020 , 41 ( 11 ): 2365 - 2370 .

HAO M , ZHANG H , SHI W Z , et al . Unsupervised change detection using fuzzy c-means and MRF from remotely sensed images [J ] . Remote Sensing Letters , 2013 , 4 ( 12 ): 1185 - 1194 .

张翠军 , 安冉 , 马丽 . 改进U-Net的遥感图像中建筑物变化检测 [J ] . 计算机工程与应用 , 2021 , 57 ( 3 ): 239 - 246 .

ZHANG C J , AN R , MA L . Building change detection in remote sensing image based on improved U-Net [J ] . Computer Engineering and Applications , 2021 , 57 ( 3 ): 239 - 246 .

张兵 . 当代遥感科技发展的现状与未来展望 [J ] . 中国科学院院刊 , 2017 , 32 ( 7 ): 774 - 784 .

ZHANG B . Current status and future prospects of remote sensing [J ] . Bulletin of Chinese Academy of Sciences , 2017 , 32 ( 7 ): 774 - 784 .

方勇 , 张武 , 张丽 , 等 . 基于高光谱影像的地形图要素变化自动检测与更新方法研究 [J ] . 测绘通报 , 2007 ( 7 ): 51 - 53 .

FANG Y , ZHANG W , ZHANG L , et al . Technique for auto change detection and updating of topographic map using hyperspectral image [J ] . Bulletin of Surveying and Mapping , 2007 ( 7 ): 51 - 53 .

詹天明 , 宋博 , 孙乐 , 等 . 高光谱协同稀疏与非局部低秩张量变化检测 [J ] . 计算机科学与探索 , 2022 , 16 ( 2 ): 448 - 457 .

ZHAN T M , SONG B , SUN L , et al . Hyperspectral change detection using collaborative sparsity and nonlocal lowrank tensor [J ] . Journal of Frontiers of Computer Science and Technology , 2022 , 16 ( 2 ): 448 - 457 .

YUAN Z H , WANG Q , LI X L . ROBUST PCANet for hyperspectral image change detection [C ] // Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium . Piscataway:IEEE Press , 2018 : 4931 - 4934 .

LI X L , YUAN Z H , WANG Q . Unsupervised deep noise modeling for hyperspectral image change detection [J ] . Remote Sensing , 2019 , 11 ( 3 ): 258 .

赵春晖 , 张锦林 , 宿南 , 等 . 用于高光谱变化检测的多径卷积网络算法 [J ] . 哈尔滨工程大学学报 , 2020 , 41 ( 9 ): 1398 - 1404 .

ZHAO C H , ZHANG J L , SU N , et al . Multipath convolutional neural network algorithm for hyperspectral change detection [J ] . Journal of Harbin Engineering University , 2020 , 41 ( 9 ): 1398 - 1404 .

张良培 , 武辰 . 多时相遥感影像变化检测的现状与展望 [J ] . 测绘学报 , 2017 , 46 ( 10 ): 1447 - 1459 .

ZHANG L P , WU C . Advance and future development of change detection for multitemporal remote sensing imagery [J ] . Acta Geodaetica et Cartographica Sinica , 2017 , 46 ( 10 ): 1447 - 1459 .

浏览量

509

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

遥感大数据在公路交通领域中的应用与实践

我国海洋卫星遥感大数据及其应用服务

论大数据视角下的地球空间信息学的机遇与挑战