Semantic segmentation is an extremely challenging task in high-resolution remote sensing
(HRRS) images as objects have complex spatial layouts and enormous variations in …

Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a
category to each pixel in remote sensing images, plays a vital role in a broad range of …

Traditional building and water segmentation methods are vulnerable to noise interference,
and hence, they could not avoid missed and false detections in the detection process …

Automatic land cover classification from high-resolution remote sensing (RS) images
remains challenging due to the complex composition of classes. Given the potential of a …

This work uses machine learning approaches to present semantic segmentation for land
cover classification in Gambella National Park (GNP). Land cover classification has become …

Deep learning (DL) algorithms have shown great potential in classifying satellite imagery but
require large amounts of labeled data to make accurate predictions. However, generating …

Semantic segmentation is a fundamental task in the field of remote sensing image intelligent
interpretation and computer vision. Multispectral remote sensing images have attracted …

Few-shot learning (FSL) is a paradigm that simulates the fast learning ability of human
beings, which can learn the feature differences between two groups of small-scale samples …

This study aims at improving the efficiency of remote sensing scene classification (RSSC)
through lightweight neural networks and to provide a possibility for large-scale, intelligent …

Abstract Hyperspectral Image Classification (HSIC) is a challenging task due to the high-
dimensional nature of Hyperspectral Imaging (HSI) data and the complex relationships …