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 …

A wide range of methods for analysis of airborne-and satellite-derived imagery continues to
be proposed and assessed. In this paper, we review remote sensing implementations of …

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 …

Supervised machine learning and deep learning methods perform well in hyperspectral
image classification. However, hyperspectral images have few labeled samples, which …

Information about the Earth's surface is required in many wide-scale applications. Land
cover/use classification using remotely sensed images is one of the most common …

This study attempts to optimize the prediction accuracy of the compressive strength of high-
performance concrete (HPC) by comparing data-mining methods. Modeling the dynamics of …

Information on land use and land cover (LULC) including forest cover is important for the
development of strategies for land planning and management. Satellite remotely sensed …

Land use classification is an important part of many remote sensing applications. A lot of
research has gone into the application of statistical and neural network classifiers to remote …

Deep convolutional neural network (CNN) shows excellent effectiveness on hyperspectral
image (HSI) classification. However, the architecture design of CNN requires abundant …

Urbanization has become one of the most important human activities modifying the Earth's
land surfaces; and its impacts on tropical and subtropical cities (eg, in South/Southeast Asia) …