Real-Time Semantic Segmentation: A brief survey and comparative study in remote sensing
Real-time semantic segmentation of remote sensing imagery is a challenging task that
requires a tradeoff between effectiveness and efficiency. It has many applications, including …
requires a tradeoff between effectiveness and efficiency. It has many applications, including …
Optical remote sensing image cloud detection with self-attention and spatial pyramid pooling fusion
Cloud detection is a key step in optical remote sensing image processing, and the cloud-free
image is of great significance for land use classification, change detection, and long time …
image is of great significance for land use classification, change detection, and long time …
Leveraging physical rules for weakly supervised cloud detection in remote sensing images
Y Liu, Q Li, X Li, S He, F Liang, Z Yao… - … on Geoscience and …, 2023 - ieeexplore.ieee.org
Cloud detection plays a significant role in remote sensing (RS) image applications. Existing
deep learning-based cloud detection methods rely on massive precise pixelwise …
deep learning-based cloud detection methods rely on massive precise pixelwise …
Multi-directional Graph Learning-based Infrared Cirrus Detection with Local Texture Features
Infrared cirrus detection is extensively used in military and civil fields, but it poses several
challenges to existing methods. These challenges include the complex and diverse shapes …
challenges to existing methods. These challenges include the complex and diverse shapes …
Infrared cirrus detection using non-convex rank surrogates for spatial-temporal tensor
S Xiao, Z Peng, F Li - Remote Sensing, 2023 - mdpi.com
Infrared small target detection (ISTD) plays a significant role in earth observation infrared
systems. However, some high reflection areas have a grayscale similar to the target, which …
systems. However, some high reflection areas have a grayscale similar to the target, which …
Cloud detection in optical remote sensing images with deep semi-supervised and active learning
X Yao, Q Guo, A Li - IEEE Geoscience and Remote Sensing …, 2023 - ieeexplore.ieee.org
Clouds hinder the surface observation by optical remote sensing sensors. It is of great
significance to detect clouds and nonclouds in remote sensing images. Compared with the …
significance to detect clouds and nonclouds in remote sensing images. Compared with the …
Cross-supervised learning for cloud detection
We present a new learning paradigm, that is, cross-supervised learning, and explore its use
for cloud detection. The cross-supervised learning paradigm is characterized by both …
for cloud detection. The cross-supervised learning paradigm is characterized by both …
Methodology for Severe Convective Cloud Identification Using Lightweight Neural Network Model Ensembling
J Zhang, M He - Remote Sensing, 2024 - mdpi.com
This study introduces an advanced ensemble methodology employing lightweight neural
network models for identifying severe convective clouds from FY-4B geostationary …
network models for identifying severe convective clouds from FY-4B geostationary …
A Novel Method for Cloud and Cloud Shadow Detection Based on the Maximum and Minimum Values of Sentinel-2 Time Series Images
K Liang, G Yang, Y Zuo, J Chen, W Sun, X Meng… - Remote Sensing, 2024 - mdpi.com
Automatic and accurate detection of clouds and cloud shadows is a critical aspect of optical
remote sensing image preprocessing. This paper provides a time series maximum and …
remote sensing image preprocessing. This paper provides a time series maximum and …
ESF-YOLO: an accurate and universal object detector based on neural networks
W Tao, X Wang, T Yan, Z Liu, S Wan - Frontiers in Neuroscience, 2024 - frontiersin.org
As an excellent single-stage object detector based on neural networks, YOLOv5 has found
extensive applications in the industrial domain; however, it still exhibits certain design …
extensive applications in the industrial domain; however, it still exhibits certain design …