Implementation of machine-learning classification in remote sensing: An applied review
Machine learning offers the potential for effective and efficient classification of remotely
sensed imagery. The strengths of machine learning include the capacity to handle data of …
sensed imagery. The strengths of machine learning include the capacity to handle data of …
[HTML][HTML] Optical remotely sensed time series data for land cover classification: A review
Accurate land cover information is required for science, monitoring, and reporting. Land
cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring …
cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring …
An assessment of the effectiveness of a random forest classifier for land-cover classification
Land cover monitoring using remotely sensed data requires robust classification methods
which allow for the accurate map** of complex land cover and land use categories …
which allow for the accurate map** of complex land cover and land use categories …
A survey of image classification methods and techniques for improving classification performance
Image classification is a complex process that may be affected by many factors. This paper
examines current practices, problems, and prospects of image classification. The emphasis …
examines current practices, problems, and prospects of image classification. The emphasis …
Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
V Rodriguez-Galiano, M Sanchez-Castillo… - Ore Geology …, 2015 - Elsevier
Abstract Machine learning algorithms (MLAs) such us artificial neural networks (ANNs),
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
Random forest classifier for remote sensing classification
M Pal - International journal of remote sensing, 2005 - Taylor & Francis
Growing an ensemble of decision trees and allowing them to vote for the most popular class
produced a significant increase in classification accuracy for land cover classification. The …
produced a significant increase in classification accuracy for land cover classification. The …
[BOOK][B] Computer processing of remotely-sensed images
Computer Processing of Remotely-Sensed Images A thorough introduction to computer
processing of remotely-sensed images, processing methods, and applications Remote …
processing of remotely-sensed images, processing methods, and applications Remote …
Advanced spectral classifiers for hyperspectral images: A review
Hyperspectral image classification has been a vibrant area of research in recent years.
Given a set of observations, ie, pixel vectors in a hyperspectral image, classification …
Given a set of observations, ie, pixel vectors in a hyperspectral image, classification …
[BOOK][B] Classification methods for remotely sensed data
P Mather, B Tso - 2016 - taylorfrancis.com
Since the publishing of the first edition of Classification Methods for Remotely Sensed Data
in 2001, the field of pattern recognition has expanded in many new directions that make use …
in 2001, the field of pattern recognition has expanded in many new directions that make use …
Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends
Q Weng - Remote Sensing of Environment, 2012 - Elsevier
The knowledge of impervious surfaces, especially the magnitude, location, geometry, spatial
pattern of impervious surfaces and the perviousness–imperviousness ratio, is significant to a …
pattern of impervious surfaces and the perviousness–imperviousness ratio, is significant to a …