Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives
Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …
classifying whether each pixel of the image is water or not, has become a hot issue in the …
Multistage attention ResU-Net for semantic segmentation of fine-resolution remote sensing images
The attention mechanism can refine the extracted feature maps and boost the classification
performance of the deep network, which has become an essential technique in computer …
performance of the deep network, which has become an essential technique in computer …
Remote sensing image retrieval in the past decade: Achievements, challenges, and future directions
Remote sensing image retrieval (RSIR) aims to search and retrieve the images of interest
from a large remote sensing image archive, which has remained to be a hot topic over the …
from a large remote sensing image archive, which has remained to be a hot topic over the …
Integrated remote sensing and GIS approach using Fuzzy-AHP to delineate and identify groundwater potential zones in semi-arid Shanxi Province, China
Abstract In Shanxi Province, China, groundwater is a major problem and exploration of
groundwater potential zones (GWPZs) is a great necessity. This paper contributed to …
groundwater potential zones (GWPZs) is a great necessity. This paper contributed to …
On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid
The past years have witnessed great progress on remote sensing (RS) image interpretation
and its wide applications. With RS images becoming more accessible than ever before …
and its wide applications. With RS images becoming more accessible than ever before …
Multilabel remote sensing image retrieval based on fully convolutional network
Conventional remote sensing image retrieval (RSIR) system usually performs single-label
retrieval where each image is annotated by a single label representing the most significant …
retrieval where each image is annotated by a single label representing the most significant …
Combining deep learning and ontology reasoning for remote sensing image semantic segmentation
Y Li, S Ouyang, Y Zhang - Knowledge-based systems, 2022 - Elsevier
Because of its wide potential applications, remote sensing (RS) image semantic
segmentation has attracted increasing research interest in recent years. Until now, deep …
segmentation has attracted increasing research interest in recent years. Until now, deep …
A deep semantic alignment network for the cross-modal image-text retrieval in remote sensing
Q Cheng, Y Zhou, P Fu, Y Xu… - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
Because of the rapid growth of multimodal data from the internet and social media, a cross-
modal retrieval has become an important and valuable task in recent years. The purpose of …
modal retrieval has become an important and valuable task in recent years. The purpose of …
Combining deep semantic segmentation network and graph convolutional neural network for semantic segmentation of remote sensing imagery
S Ouyang, Y Li - Remote Sensing, 2020 - mdpi.com
Although the deep semantic segmentation network (DSSN) has been widely used in remote
sensing (RS) image semantic segmentation, it still does not fully mind the spatial …
sensing (RS) image semantic segmentation, it still does not fully mind the spatial …
Land cover classification from remote sensing images based on multi-scale fully convolutional network
ABSTRACT Although the Convolutional Neural Network (CNN) has shown great potential
for land cover classification, the frequently used single-scale convolution kernel limits the …
for land cover classification, the frequently used single-scale convolution kernel limits the …