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Optimal Bayesian classifier for land cover classification using Landsat TM data
- Y. Zhu, Yunxin Zhao, K. Palaniappan, X. Zhou, X. Zhuang
- Environmental Science, MathematicsIGARSS . IEEE International Geoscience and…
- 24 July 2000
Preliminary results indicate that modeling the multispectral, multitemporal remotely sensed radiance data for land cover using a Gaussian mixture model is superior to using unimodal Gaussian distributions.
MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING
Abstract. Water quality is an important index of the ecological environment, which changes rapidly and needs to be monitored chronically. In urban ecological environment, water quality problem is not…
STUDY ON BIG DATABASE CONSTRUCTION AND ITS APPLICATION OF SAMPLE DATA COLLECTED IN CHINA'S FIRST NATIONAL GEOGRAPHIC CONDITIONS CENSUS BASED ON REMOTE SENSING IMAGES
The results verify that the method of database construction which is based on relational database with distributed file system is very useful and applicative for sample data’s searching, analyzing and promoted application.
Mapping the Riparian Vegetation Using Multiple Hyperspectral and Thermal Infrared Airborne Imagery over the Republican River, Nebraska
Application of spectral mixture modelling to the regional assessment of land degradation: A case study from basilicata, Italy
Mapping and monitoring of land degradation processes such as soil erosion has become an important task for both agricultural and environmental planners. The potential of using SPOT-HRV data for…
Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation
The RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas.
AN APPROACH FOR EVALUATING THE INFORMATION CONTENT OF REMOTE SENSING IMAGES
Experimental results indicate that the proposed approach can better characterize the spectrum features and spatial structural features contained in images and visual perception information and reflect the difference in the quality of different modality images, especially the effect for the images that contain clouds or poor lighting conditions, is better.