Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …

In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are
available for land-cover mapping. However, due to the complex information brought by the …

Object-based image classification for land-cover mapping purposes using remote-sensing
imagery has attracted significant attention in recent years. Numerous studies conducted over …

Aerial scene classification, which aims to automatically label an aerial image with a specific
semantic category, is a fundamental problem for understanding high-resolution remote …

Present paper tends to capture the impact of land use land cover (LULC) on land surface
temperature (LST) in English Bazar Municipality of Malda District using multi spectral and …

In this study, the relationship between land surface temperature (LST) and Land Use/Land
Cover (LULC) in Tehran Metropolitan City was studied using Landsat TM satellite image. For …

Tree species diversity is a key parameter to describe forest ecosystems. It is, for example,
important for issues such as wildlife habitat modeling and close-to-nature forest …

Pixel-based and object-based image analysis approaches for classifying broad land cover
classes over agricultural landscapes are compared using three supervised machine …

This study evaluates and compares the performance of four machine learning classifiers—
support vector machine (SVM), normal Bayes (NB), classification and regression tree …