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| 1 | Multimodal hyperspectral remote sensing:an overview and perspective显示文摘Since the advent of hyperspectral remote sensing in the 1980 s,it has made important achievements in aerospace and aviation field and been applied in many fields.Conventional hyperspectral imaging spectrometer extends the number of spectral bands to dozens or hundreds,and provides spatial distribution of the reflected solar radiation from the scene of observation at the same time.Nowadays,with the fast development of new technology in the fields of information and photoelectricity sensing,and the popularity of unmanned aerial vehicle,hyperspectral remote sensing imaging presents the new trends of multimodality and acquires integration information while keeping high or very-high spectral resolution,especially,high temporal even real time sensing and stereo sensing.Therefore,three important modes of hyperspectral imaging come into existence:(1)multitemporal hyperspectral imaging,which refers to the observation of same region at different dates;(2)hyperspectral video imaging,which captures full frame spectral images in real-time;(3)hyperspectral stereo imaging,which obtains the full dimension information(including 2D image,elevation,and spectra)of observed scene.Along this perspective,firstly,the current researches on hyperspectral remote sensing and image processing are briefly reviewed,and then,comprehensive descriptions of the aforementioned three main hyperspectral imaging modes are carried out from the following four aspects:fundamental principle of new mode of hyperspectral imaging,corresponding scientific data acquisition,data processing and application,and potential challenges in data representation,feature learning and interpretation.Through the analysis of development trend of hyperspectral imaging and current research situation,we hope to provide a direction for future research on multimodal hyperspectral remote sensing. | Yanfeng GU Tianzhu LIU Guoming GAO Guangbo REN Yi MA Jocelyn CHANUSSOT Xiuping JIA | 2021 | Science China(Information Sciences)2021，64，2： | 6 |
| 2 | Automatic detection of rivers in high- resolution SAR data 显示文摘 | KLEMENJAK S WASKE B VALERO S CHANUSSOT J | 2012 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2012，5，5： | 1 |
| 3 | Scalar Image filters for speckle reduction on synthetic aperture sonar images显示文摘 | Jocelyn Chanussot Frederic Mausssang | 2002 | IEEE transactions on image processing2002，，1： | 1 |
| 4 | Fuzzy fusion techniques for linear features detection in multi-temporal SAR images显示文摘 | Chanussot J Mauris G Lambert P | 1999 | IEEE Trans on Geosci and Remote Sensing1999，37，3： | 1 |
| 5 | Scalar image filters for speckle reduction on synthetic aperture sonar images显示文摘 | Frederic Mausssang | 2002 | IEEE2002，，1： | 1 |
| 6 | Kernel Principal Component Analysis for the Classification of Hyperspectral Remote-sensing Data over Urban Areas显示文摘 | Fauvel M Chanussot J Benediktsson J A | 2009 | Eurasip Journal on Advances in Signal Processing2009，10，1： | 1 |
| 7 | Morphological and statistical approaches to improve detection in the presence of reverberation显示文摘 | GINOLHAC G CHANUSSOT J | 2005 | IEEE Journal of Oceanic Engineering2005，30，4： | 1 |
| 8 | Detection of anomalies produced by buried archaeological structures using nonlinear principal component analysis applied to airborne hyperspectral image显示文摘 | Cavalli R M Licciardi G A Chanussot J | 2013 | IEEE Transactions on Selected Topics in Applied Earth Observations and Remote Sensing2013，6，2： | 1 |
| 9 | Scalar Image filters for speckle reduction on synthetic aperture sonar images显示文摘 | Frederic Mausssang | 2002 | IEEE2002，，1： | 1 |
| 10 | Comparison of pansharpening algorithms: outcome of the 2006 GRS-S data- fusion contest显示文摘 | Alparone L Wald L Chanussot J | 2007 | IEEE Transactions on Geoscience and Remote Sensing2007，45，10： | 1 |
| 11 | Foreword to the special issue on data fusion 显示文摘 | Gamba P Chanussot J | 2008 | IEEE Transactions on Geoscience and Remote Sensing2008，46，5： | 1 |
| 12 | SVM-and MRF- based method for accurate classification of hyperspeetral, images 显示文摘 | Tarabalka Y Fauvel M Chanussot J et | 2010 | IEEE Geoseienee and Remote Sensing Letters2010，7，4： | 1 |
| 13 | Segmentation and Classification of Hyperspectral Images Using Watershed Transformation显示文摘 | TARABALKA Y CHANUSSOT J BENEDIKTSSON J A | 2010 | Pattern Recognition2010，43，7： | 1 |
| 14 | SVM- and MRF-based method for accurate classification of hyperspectral images 显示文摘 | TARABALKA Y FAUVEL M CHANUSSOT J BENEDIKTSSON J A | 2010 | IEEE Gcoscience and Remote Sensing Letters2010，7，4： | 1 |
| 15 | Recent advances in techniques for hyperspectral image processing显示文摘 | Antonio Plaza Jon Atli Benediktsson Joseph W. Boardman Jason Brazile Lorenzo Bruzzone Gustavo Camps-Valls Jocelyn Chanussot Mathieu Fauvel Paolo Gamba Anthony Gualtieri Mattia Marconcini James C. Tilton Giovanna Trianni | 2009 | Remote Sensing of Environment2009，，： | 1 |
| 16 | Advanced Directional Mathematical Morphology for the Detection of the Road Network in Very High Resolution Remote Sensing Images显示文摘 | Valero S Chanussot J Benediktsson J A | 2010 | Pattern Recognition Letters2010，31，10： | 1 |
| 17 | Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas显示文摘 | Fauvel M Chanussot J Benediktsson J A | 2009 | EURASIP Journal on Advances in Signal Processing2009，，： | 1 |
| 18 | Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles显示文摘 | FAUVEL M BENEDIKTSSON J A CHANUSSOT J | 2008 | IEEE Transactions on Geoscience and Remote Sensing2008，46，11： | 1 |
| 19 | Ad- vanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images 显示文摘 | Valero S Chanussot J Benediktsson J A | 2010 | Pattern Recognition Let- ters2010，31，10： | 1 |
| 20 | Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles显示文摘 | FAUVEL M BENEDIKTSSON J A CHANUSSOT J | 2008 | IEEE Transactions on Geoscience and Remote Sensing2008，46，11： | 1 |
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