Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
A survey on deep learning-based change detection from high-resolution remote sensing images
H Jiang, M Peng, Y Zhong, H Xie, Z Hao, J Lin, X Ma… - Remote Sensing, 2022 - mdpi.com
Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …
remote sensing analysis, and it has been widely used in many areas, such as resources …
Remote sensing image change detection with transformers
Modern change detection (CD) has achieved remarkable success by the powerful
discriminative ability of deep convolutions. However, high-resolution remote sensing CD …
discriminative ability of deep convolutions. However, high-resolution remote sensing CD …
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 …
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 …
Deep building footprint update network: A semi-supervised method for updating existing building footprint from bi-temporal remote sensing images
Building footprint information is one foundation for understanding urban processes and
hence a program for environmentally sustainable urbanization. For most cities, municipal …
hence a program for environmentally sustainable urbanization. For most cities, municipal …
Iterative training sample augmentation for enhancing land cover change detection performance with deep learning neural network
Labeled samples are important in achieving land cover change detection (LCCD) tasks via
deep learning techniques with remote sensing images. However, labeling samples for …
deep learning techniques with remote sensing images. However, labeling samples for …
A coarse-to-fine boundary refinement network for building footprint extraction from remote sensing imagery
Extracting building footprints from remotely sensed imagery has long been a challenging
task and is not yet fully solved. Obstructions from nearby shadows or trees, varying shapes …
task and is not yet fully solved. Obstructions from nearby shadows or trees, varying shapes …
Transformer-based multistage enhancement for remote sensing image super-resolution
Convolutional neural networks have made a great breakthrough in recent remote sensing
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …