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19篇 您的检索式：作者名="Mountrakis"

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		题名	作者	年代	出处	被引量
	1	Support Vector Machines in Re- mote Sensing:A Review显示文摘	Mountrakis G Im J Ogole C	2011	ISPRS Journal of Photogramme try and Remote Sensing2011，66，3：	1
	2	Support Vector Machines in Remote Sensing:A Review显示文摘	MOUNTRAKIS G IM J OGOLE C	2011	ISPRS Journal of Photogrammetry and Remote Sensing2011，66，3：	1
	3	Support vector machines in remote sensing: A review显示文摘	Mountrakis G Im J Ogole C	2011	ISPRS Journal of Photogram- metry and Remote Sensing2011，，66：	1
	4	Support Vector Machines in Remote Sensing: A Review显示文摘	MOUNTRAKIS G IM J OGOLE C	2011	ISPRS Journal of Photogrammetry and Remote Sensing2011，6，3：	1
	5	Support VectorMa-chines in Remote Sensing: A Review显示文摘	Giorgos Mountrakis Jungho Im	2011	Jour-nal of Photogrammetry and Remote Sensing2011，66，：	1
	6	查看详情显示文摘	Giorgos Mountrakis Jungho I M Caesar Ogole		0，，：	1
	7	Capturing and Modeling Geographic Object Change:A SpatioTemporal Gazetteer Framework显示文摘	Agouris P Beard K Mountrakis G etc	2000	Photogrammetric Engineering & Remote Sensing2000，66，10：	1
	8	The seismic parameter b of the frequency-magnitude relation and its association with the geological zones in the area of Greece显示文摘	Hatzidimitriou P M Papadimitriou E E Mountrakis D M and Papazachos B C	1985	Tectonophysics1985，120，：	1
	9	查看详情显示文摘	Giorgos Mountrakis Jungho Im Caesar Ogole		0，，3：	1
	10	Support vec-tor machines in remote sensing:a review显示文摘	MOUNTRAKIS G IM J OGOLE C	2011	ISPRSJournal of Photogrammetry and Remote Sensing2011，66，3：	1
	11	Support veetor machines in re- mote sensing: a review显示文摘	MOUNTRAKIS G IM J OGOLE C	2011	ISPRS Journal of Photogrammetry and Remote Sensing2011，66，3：	1
	12	Support vector machines in remote sensing: A review 显示文摘	Mountrakis G Im J Ogole C	2011	Journal of Photogrammetry and Remote Sensing2011，66，3：	1
	13	Support vector machines in remote sensing:a review显示文摘	Mountrakis G Im J Ogole C		0，，03：	1
	14	Support vector machines in remote sensing: A review显示文摘	Mountrakis G Im J Ogole C	2011	ISPRS Journal of Photo- grammetry and Remote Sensing2011，66，3：	1
	15	Support vector machines in remote sensing:a review显示文摘	MOUNTRAKIS G IM J OGOLE C	2011	ISPRS Journal of Photogrammetry and Remote Sensing2011，66，3：	1
	16	Support vector machines in remote sensing:A review显示文摘	Mountrakis G Im J Ogole C	2011	ISPRS Journal of Photogrammetry and Remote Sensing2011，66，3：	1
	17	Support vector machines in remote sensing:a review显示文摘	Mountrakis G Im J Ogole C		0，，03：	1
	18	Support vector machines in remote sensing: A review显示文摘	Mountrakis G lm J Ogole C	2011	ISPRS Journal of Photogram- metry and Remote Sensing2011，66，：	1
	19	Ground peak identification in dense shrub areas using large footprint waveform LiDAR and Landsat images显示文摘Large footprint waveform LiDAR data have been widely used to extract tree heights.These heights are typically estimated by subtracting the top height from the ground.Compared to the top height detection,the identification of the ground peak in awaveform is more challenging.This is particularly evident in ground detection in shrubareas,where the reflection of the shrub canopy may significantly overlap with theground reflection.To tackle this problem,a novel method based on Partial Curve-Fitting(PCF)of the shrub peak was developed to detect the ground peak.Resultsindicated that the PCF method improves ground identification by 32-42%,comparedto existing methods.To offer further improvement,a Multi-Algorithm IntegrationClassifier(MAIC)was built to fuse multiple ground peak algorithms and selectivelyapply the best method for each waveform plot.The PCF ground peak identificationmethod along with the MAIC-based fusion is expected to significantly improve grounddetection and shrub height estimation,thus assisting biodiversity,forest succession,and carbon sequestration studies,while offering an early example of future multiplealgorithm integration.	Wei Zhuang Giorgos Mountrakis	2015	International Journal of Digital Earth2015，8，10：	0

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