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 …

Deep learning has become a popular method for studying the semantic segmentation of
high-resolution remote sensing images (HRRSIs). Existing methods have adopted …

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

Semantic segmentation is a crucial approach for remote sensing interpretation. High-
precision semantic segmentation results are obtained at the cost of manually collecting …

Currently, the most advanced high-resolution remote sensing image (HRRSI) semantic
labeling methods rely on deep neural networks. However, HRRSIs naturally have a serious …

Accurate segmentation and detection of rice seedlings is essential for precision agriculture
and high-yield cultivation. However, current methods suffer from high computational …

Abstract Semantic segmentation of Remote Sensing (RS) images involves the classification
of each pixel in a satellite image into distinct and non-overlapping regions or segments. This …

Supervised learning methods assume that training and test data are sampled from the same
distribution. However, this assumption is not always satisfied in practical situations of land …

With the advances in Unmanned Aerial Vehicles (UAVs) technology, aerial images with
huge variations in the appearance of objects and complex backgrounds have opened a new …

High-resolution remote-sensing images cover more feature information, including texture,
structure, shape, and other geometric details, while the relationships among target features …