Big Data Research ›› 2022, Vol. 8 ›› Issue (2): 28-57.doi: 10.11959/j.issn.2096-0271.2022014
• TOPOC: AEROSPACE BIG DATA • Previous Articles Next Articles
Weiquan LIU1,2, Cheng WANG1,2, Yu ZANG1,2, Qian HU1,2, Shangshu YU1,2, Baiqi LAI1,2
Online:
2022-03-15
Published:
2022-03-01
Supported by:
CLC Number:
Weiquan LIU, Cheng WANG, Yu ZANG, Qian HU, Shangshu YU, Baiqi LAI. A survey on information extraction technology based on remote sensing big data[J]. Big Data Research, 2022, 8(2): 28-57.
[1] | 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. | |
[2] | 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. | |
[3] | 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. |
[4] | 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. |
[5] | 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. |
[6] | DUBES R C , JAIN A K . Random field models in image analysis[J]. Journal of Applied Statistics, 1989,16(2): 131-164. |
[7] | 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. |
[8] | 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. | |
[9] | 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. |
[10] | 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. | |
[11] | 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. | |
[12] | 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. |
[13] | 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. |
[14] | 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. |
[15] | 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. |
[16] | 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. |
[17] | 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. |
[18] | 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. |
[19] | FISCHLER M A , ELSCHLAGER R A . The representation and matching of pictorial structures[J]. IEEE Transactions on Computers, 1973,C-22(1): 67-92. |
[20] | JAIN A K , ZHONG Y , DUBUISSONJOLLY M P . Deformable template models:a review[J]. Signal Processing, 1998,71(2): 109-129. |
[21] | 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. |
[22] | 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. |
[23] | 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. |
[24] | 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. |
[25] | 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. | |
[26] | TRINDER J C , WANG Y D . Knowledgebased road interpretation in aerial images[Z]. 1998. |
[27] | HUERTAS A , NEVATIA R . Detecting buildings in aerial images[J]. Computer Vision,Graphics,and Image Processing, 1988,41(2): 131-152. |
[28] | WEIDNER U , F?RSTNER W , . Towards automatic building extraction from highresolution digital elevation models[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1995,50(4): 38-49. |
[29] | 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. |
[30] | 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. |
[31] | 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. |
[32] | 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. |
[33] | 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. |
[34] | 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. |
[35] | 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. |
[36] | 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. |
[37] | 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. |
[38] | 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. |
[39] | 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. |
[40] | 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. |
[41] | LOWE D G . Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004,60(2): 91-110. |
[42] | 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. |
[43] | 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. |
[44] | 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. |
[45] | 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. |
[46] | 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. |
[47] | 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. |
[48] | 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. |
[49] | 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. |
[50] | 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. |
[51] | 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. |
[52] | 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. |
[53] | 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. |
[54] | VAN ETTEN A . You only look twice:rapid multi-scale object detection in satellite imagery[J]. arXiv preprint,2018,arXiv:1805.09512. |
[55] | 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. |
[56] | 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. |
[57] | LIU L , PAN Z X , LEI B . Learning a rotation invariant detector with rotatable bounding box[J]. arXiv preprint,2017,arXiv:1711.09405. |
[58] | 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. |
[59] | 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. |
[60] | 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. |
[61] | JAIN R C , KASTURI R , SCHUNCK B G . Machine vision[M]. New York: McGrawHill,Inc., 1995. |
[62] | 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. |
[63] | 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. |
[64] | 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. | |
[65] | DRǎGU? 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. |
[66] | 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. | |
[67] | 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. |
[68] | NEUBERT M , HEROLD H . Assessment of remote sensing image segmentation quality[Z]. 2008. |
[69] | 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. |
[70] | 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. |
[71] | 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. |
[72] | 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. |
[73] | 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. |
[74] | 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. |
[75] | 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. |
[76] | 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. |
[77] | 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. |
[78] | 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. |
[79] | 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. | |
[80] | 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. | |
[81] | SINGH A . Review article digital change detection techniques using remotelysensed data[J]. International Journal of Remote Sensing, 1989,10(6): 989-1003. |
[82] | 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. |
[83] | 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. | |
[84] | 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. | |
[85] | 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. |
[86] | 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. |
[87] | 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. | |
[88] | 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. | |
[89] | 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. | |
[90] | 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. | |
[91] | 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. |
[92] | 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. | |
[93] | 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. |
[94] | 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. | |
[95] | 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. |
[96] | 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. |
[97] | 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. | |
[98] | 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. | |
[99] | 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. |
[100] | 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. |
[101] | 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. |
[102] | 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. |
[103] | LóPEZ-FANDI?O 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. |
[104] | ABD EL-KAWY O R , R?D 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. |
[105] | 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. |
[106] | 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. | |
[107] | 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. |
[108] | 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. | |
[109] | 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. | |
[110] | 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. | |
[111] | 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. | |
[112] | 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. |
[113] | LI X L , YUAN Z H , WANG Q . Unsupervised deep noise modeling for hyperspectral image change detection[J]. Remote Sensing, 2019,11(3): 258. |
[114] | 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. | |
[115] | 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. |
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