Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data
Published 2018 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data
Authors
Keywords
-
Journal
Earth Science Informatics
Volume -, Issue -, Pages -
Publisher
Springer Nature America, Inc
Online
2018-10-28
DOI
10.1007/s12145-018-0369-z
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data
- (2018) P. Kumar et al. Geocarto International
- Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information
- (2017) Varun Narayan Mishra et al. Journal of Applied Remote Sensing
- A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India
- (2016) Varun Narayan Mishra et al. Arabian Journal of Geosciences
- Dual-polarimetric C-band SAR data for land use/land cover classification by incorporating textural information
- (2016) Varun Narayan Mishra et al. Environmental Earth Sciences
- Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map
- (2016) Myroslava Lesiv et al. Remote Sensing
- Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data
- (2015) Pradeep Kumar et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- The roles of textural images in improving land-cover classification in the Brazilian Amazon
- (2014) Dengsheng Lu et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Continuous change detection and classification of land cover using all available Landsat data
- (2014) Zhe Zhu et al. REMOTE SENSING OF ENVIRONMENT
- Incorporation of texture information in a SVM method for classifying salt cedar in western China
- (2014) Le Wang et al. Remote Sensing Letters
- Retrieval of Forest Stand Age From SAR Image Texture for Varying Distance and Orientation Values of the Gray Level Co-Occurrence Matrix
- (2013) Isabelle Champion et al. IEEE Geoscience and Remote Sensing Letters
- A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon
- (2013) Dengsheng Lu et al. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
- Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity
- (2012) Jaime Paneque-Gálvez et al. International Journal of Applied Earth Observation and Geoinformation
- Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions
- (2012) M.E.J. Cutler et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region
- (2012) Guiying Li et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
- (2012) V.F. Rodriguez-Galiano et al. REMOTE SENSING OF ENVIRONMENT
- Study of land cover classification based on knowledge rules using high-resolution remote sensing images
- (2010) Rongqun Zhang et al. EXPERT SYSTEMS WITH APPLICATIONS
- Classifiers vs. input variables—The drivers in image classification for land cover mapping
- (2009) M. Heinl et al. International Journal of Applied Earth Observation and Geoinformation
- Assessing land-cover change and degradation in the Central Asian deserts using satellite image processing and geostatistical methods
- (2008) A. Karnieli et al. JOURNAL OF ARID ENVIRONMENTS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started