Exploiting the Classification Performance of Support Vector Machines with Multi-Temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) Data in Areas of Agreement and Disagreement of Existing Land Cover Products
出版年份 2012 全文链接
标题
Exploiting the Classification Performance of Support Vector Machines with Multi-Temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) Data in Areas of Agreement and Disagreement of Existing Land Cover Products
作者
关键词
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出版物
Remote Sensing
Volume 4, Issue 10, Pages 3143-3167
出版商
MDPI AG
发表日期
2012-10-19
DOI
10.3390/rs4103143
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- (2012) Yang Shao et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology
- (2012) Peter M. Atkinson et al. REMOTE SENSING OF ENVIRONMENT
- Exploring the Use of MODIS NDVI-Based Phenology Indicators for Classifying Forest General Habitat Categories
- (2012) Nicola Clerici et al. Remote Sensing
- Highlighting continued uncertainty in global land cover maps for the user community
- (2011) Steffen Fritz et al. Environmental Research Letters
- Terrestrial ecosystems from space: a review of earth observation products for macroecology applications
- (2011) Marion Pfeifer et al. GLOBAL ECOLOGY AND BIOGEOGRAPHY
- A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America
- (2011) Clement Atzberger et al. International Journal of Digital Earth
- Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements
- (2011) Clement Atzberger et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Comparison and assessment of coarse resolution land cover maps for Northern Eurasia
- (2011) Dirk Pflugmacher et al. REMOTE SENSING OF ENVIRONMENT
- Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring
- (2011) Christian Hüttich et al. REMOTE SENSING OF ENVIRONMENT
- Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale
- (2010) Armel Thibaut Kaptué Tchuenté et al. International Journal of Applied Earth Observation and Geoinformation
- Support vector machines in remote sensing: A review
- (2010) Giorgos Mountrakis et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’
- (2010) Annemarie Schneider et al. REMOTE SENSING OF ENVIRONMENT
- Hypertemporal Classification of Large Areas Using Decision Fusion
- (2009) T. Udelhoven et al. IEEE Geoscience and Remote Sensing Letters
- A kernel functions analysis for support vector machines for land cover classification
- (2009) T. Kavzoglu et al. International Journal of Applied Earth Observation and Geoinformation
- MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
- (2009) Mark A. Friedl et al. REMOTE SENSING OF ENVIRONMENT
- Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications
- (2008) STEFFEN FRITZ et al. GLOBAL CHANGE BIOLOGY
- Multiclass and Binary SVM Classification: Implications for Training and Classification Users
- (2008) A. Mathur et al. IEEE Geoscience and Remote Sensing Letters
- Harshness in image classification accuracy assessment
- (2008) Giles M. Foody INTERNATIONAL JOURNAL OF REMOTE SENSING
- Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets
- (2008) M. Herold et al. REMOTE SENSING OF ENVIRONMENT
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