Background: Deep Learning (DL) neural network methods have become a hotspot subject of
research in the remote sensing field. Classification of aerial satellite images depends on …

Our planet Earth comprises distinguished topologies based on temperature, location,
latitude, longitude, and altitude, which can be captured using Remote Sensing Satellites. In …

Image classification refers to segregating pictures based on their visual characteristics. This
is a crucial area of study in computer vision and is widely used in various applications such …

This paper is about Image classification. Land cover classification from Synthetic Aperture
Radar (SAR) is difficult because of the random and complicated spatial patterns present in …

Classification and analysis of high-resolution satellite images using conventional techniques
have been limited. This is due to the complex characteristics of the imagery. These images …

The processing of hyperspectral remote sensing images is a crucial field of study. As one of
the most important phases in image processing right now, the classification task has …

In this work, we are creating a system to classify satellite images in order to extract
information using image processing techniques. Classification of satellite images into used …

Satellite images are taken in different conditions that are affected by the weather, the
atmosphere, the noise they contain, and the difference in lighting. In addition, the images …

Remote sensing image classification has important research significance and application
value in image information extraction, ground object detection and identification, and is …

This paper presents a comparative study for using the deep classic convolution networks in
remote sensing images classification. There are four deep convolution models that used in …