A comprehensive review of hyperspectral data fusion with lidar and sar data

被引:50
作者
Kahraman, Sevcan [1 ]
Bacher, Raphael [2 ]
机构
[1] Istanbul Gelisim Univ, Elect & Elect Engn, TR-34315 Istanbul, Turkey
[2] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France
关键词
hyperspectral (HS) image; Light Detection And Ranging (LiDAR); Synthetic Aperture Radar (SAR); multi-modal data fusion; review; LAND-COVER CLASSIFICATION; REMOTE-SENSING DATA; FEATURE-LEVEL FUSION; HIGH-RESOLUTION; WAVE-FORM; EXTINCTION PROFILES; FEATURE-EXTRACTION; DECISION FUSION; CLOUD-SHADOW; RGB DATA;
D O I
10.1016/j.arcontrol.2021.03.003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the development of remote sensing techniques, the fusion of multimodal data, particularly hyperspectralLight Detection And Ranging (HS-LiDAR) and hyperspectral-SAR, has become an important research field in numerous application areas. Multispectral, HS, LiDAR, and Synthetic Aperture Radar (SAR) images contain detailed information about the monitored surface that are complementary to each other. Thus, data fusion methods have become a promising solution to obtain high spatial resolution remote-sensing images. The main point of this review paper is to classify hyperspectral-LiDAR and hyperspectral-SAR data fusion with approaches. Moreover, recent achievements in the fusion of hyperspectral-LiDAR and hyperspectral-SAR data are highlighted in terms of faced challenges and applications. Most frequently used data fusion datasets that include IEEE GRSS Data Fusion Contests are also described.
引用
收藏
页码:236 / 253
页数:18
相关论文
共 143 条
[1]   FUSION OF HYPERSPECTRAL AND LIDAR DATA BASED ON DIMENSION REDUCTION AND MAXIMUM LIKELIHOOD [J].
Abbasi, B. ;
Arefi, H. ;
Bigdeli, B. ;
Motagh, M. ;
Roessner, S. .
36TH INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT, 2015, 47 (W3) :569-573
[2]   Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near-infrared and green signals [J].
Allouis, Tristan ;
Bailly, Jean-Stephane ;
Pastol, Yves ;
Le Roux, Catherine .
EARTH SURFACE PROCESSES AND LANDFORMS, 2010, 35 (06) :640-650
[3]   Urban tree species mapping using hyperspectral and lidar data fusion [J].
Alonzo, Michael ;
Bookhagen, Bodo ;
Roberts, Dar A. .
REMOTE SENSING OF ENVIRONMENT, 2014, 148 :70-83
[4]   Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S data-fusion contest [J].
Alparone, Luciano ;
Wald, Lucien ;
Chanussot, Jocelyn ;
Thomas, Claire ;
Gamba, Paolo ;
Bruce, Lori Mann .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2007, 45 (10) :3012-3021
[5]  
[Anonymous], 2005, P ISPRS JOINT C 3 IN
[6]  
Aytaylan H, 2016, INT GEOSCI REMOTE SE, P2522, DOI 10.1109/IGARSS.2016.7729651
[7]   Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest [J].
Berger, Christian ;
Voltersen, Michael ;
Eckardt, Robert ;
Eberle, Jonas ;
Heyer, Thomas ;
Salepci, Nesrin ;
Hese, Soeren ;
Schmullius, Christiane ;
Tao, Junyi ;
Auer, Stefan ;
Bamler, Richard ;
Ewald, Ken ;
Gartley, Michael ;
Jacobson, John ;
Buswell, Alan ;
Du, Qian ;
Pacifici, Fabio .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2013, 6 (03) :1324-1340
[8]   Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data [J].
Bhaskaran, Sunil ;
Paramananda, Shanka ;
Ramnarayan, Maria .
APPLIED GEOGRAPHY, 2010, 30 (04) :650-665
[9]  
Bhogall A., 2001, 2001 IEEE PAC RIM C
[10]   Hyperspectral Remote Sensing Data Analysis and Future Challenges [J].
Bioucas-Dias, Jose M. ;
Plaza, Antonio ;
Camps-Valls, Gustavo ;
Scheunders, Paul ;
Nasrabadi, Nasser M. ;
Chanussot, Jocelyn .
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, 2013, 1 (02) :6-36