[HTML][HTML] A review of deep-learning methods for change detection in multispectral remote sensing images
EJ Parelius - Remote Sensing, 2023 - mdpi.com
Remote sensing is a tool of interest for a large variety of applications. It is becoming
increasingly more useful with the growing amount of available remote sensing data …
increasingly more useful with the growing amount of available remote sensing data …
RingMo: A remote sensing foundation model with masked image modeling
Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …
[HTML][HTML] Change detection methods for remote sensing in the last decade: A comprehensive review
Change detection is an essential and widely utilized task in remote sensing that aims to
detect and analyze changes occurring in the same geographical area over time, which has …
detect and analyze changes occurring in the same geographical area over time, which has …
An empirical study of remote sensing pretraining
Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …
understanding and made a great success. Nevertheless, most of the existing deep models …
Changer: Feature interaction is what you need for change detection
S Fang, K Li, Z Li - IEEE Transactions on Geoscience and …, 2023 - ieeexplore.ieee.org
Change detection is an important tool for long-term Earth observation missions. It takes bi-
temporal images as input and predicts “where” the change has occurred. Different from other …
temporal images as input and predicts “where” the change has occurred. Different from other …
Hypertransformer: A textural and spectral feature fusion transformer for pansharpening
WGC Bandara, VM Patel - … of the IEEE/CVF conference on …, 2022 - openaccess.thecvf.com
Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a
low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high …
low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high …
Asymmetric cross-attention hierarchical network based on CNN and transformer for bitemporal remote sensing images change detection
X Zhang, S Cheng, L Wang, H Li - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
As an important task in the field of remote sensing (RS) image processing, RS image
change detection (CD) has made significant advances through the use of convolutional …
change detection (CD) has made significant advances through the use of convolutional …
An attention-based multiscale transformer network for remote sensing image change detection
The bi-temporal change detection (CD) is still challenging for high-resolution optical remote
sensing data analysis due to various factors such as complex textures, seasonal variations …
sensing data analysis due to various factors such as complex textures, seasonal variations …
Spectral–spatial–temporal transformers for hyperspectral image change detection
Convolutional neural networks (CNNs) with excellent spatial feature extraction abilities have
become popular in remote sensing (RS) image change detection (CD). However, CNNs …
become popular in remote sensing (RS) image change detection (CD). However, CNNs …
Ultralightweight spatial–spectral feature cooperation network for change detection in remote sensing images
Deep convolutional neural networks (CNNs) have achieved much success in remote
sensing image change detection (CD) but still suffer from two main problems. First, the …
sensing image change detection (CD) but still suffer from two main problems. First, the …