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 …

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …

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 …

Central to the looming paradigm shift toward data-intensive science, machine-learning
techniques are becoming increasingly important. In particular, deep learning has proven to …

In this paper, we present a patch-based land use and land cover classification approach
using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely …

Remote sensing image scene classification plays an important role in a wide range of
applications and hence has been receiving remarkable attention. During the past years …

The rapid development of deep learning brings effective solutions for remote sensing image
interpretation. Training deep neural network models usually require a large number of …

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 …

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various
computer vision tasks, their potential for classification of multispectral remote sensing …

The blooming proliferation of aeronautics and astronautics platforms, together with the ever-
increasing remote sensing imaging sensors on these platforms, has led to the formation of …