Advances and challenges in deep learning-based change detection for remote sensing images: A review through various learning paradigms
Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting
changes in the Earth's surface, finding wide applications in urban planning, disaster …
changes in the Earth's surface, finding wide applications in urban planning, disaster …
Rs-mamba for large remote sensing image dense prediction
The spatial resolution of remote sensing images is becoming increasingly higher, posing
challenges in handling large very-high-resolution (VHR) remote sensing images for dense …
challenges in handling large very-high-resolution (VHR) remote sensing images for dense …
UNet-Like Remote Sensing Change Detection: A review of current models and research directions
Recently, deep learning (DL) models have become the main focus for remote sensing
change detection tasks. Numerous publications on supervised and unsupervised DL-based …
change detection tasks. Numerous publications on supervised and unsupervised DL-based …
A new learning paradigm for foundation model-based remote-sensing change detection
Change detection (CD) is a critical task to observe and analyze dynamic processes of land
cover. Although numerous deep-learning (DL)-based CD models have performed …
cover. Although numerous deep-learning (DL)-based CD models have performed …
Minenetcd: A benchmark for global mining change detection on remote sensing imagery
Monitoring land changes triggered by mining activities is crucial for industrial control,
environmental management and regulatory compliance, yet it poses significant challenges …
environmental management and regulatory compliance, yet it poses significant challenges …
DiFormer: A difference transformer network for remote sensing change detection
Change detection (CD) is one of the most important methods for monitoring land surface
changes. Recently, transformer-based models have been employed for CD. However, most …
changes. Recently, transformer-based models have been employed for CD. However, most …
GeoFormer: A Geometric Representation Transformer for Change Detection
Deep representation learning has improved automatic remote sensing change detection
(RSCD) in recent years. Existing methods emphasize primarily convolutional neural …
(RSCD) in recent years. Existing methods emphasize primarily convolutional neural …
FDFF-Net: A Full-Scale Difference Feature Fusion Network for Change Detection in High-Resolution Remote Sensing Images
Deep-learning techniques have made significant advances in remote sensing change
detection task. However, it remains a great challenge to detect the details of changed areas …
detection task. However, it remains a great challenge to detect the details of changed areas …
Content-Guided Spatial-Spectral Integration Network for Change Detection in HR Remote Sensing Images
The integration of spatial and spectral information is beneficial to the improvement of change
detection (CD) performance. However, existing methods cannot efficiently suppress the …
detection (CD) performance. However, existing methods cannot efficiently suppress the …
SonarNet: Hybrid CNN-Transformer-HOG Framework and Multifeature Fusion Mechanism for Forward-Looking Sonar Image Segmentation
J He, J Chen, H Xu, Y Yu - IEEE Transactions on Geoscience …, 2024 - ieeexplore.ieee.org
Forward-looking sonar (FLS) image segmentation plays a significant role in ocean
engineering. However, the existing image segmentation algorithms present difficulties in …
engineering. However, the existing image segmentation algorithms present difficulties in …