Change detection based on artificial intelligence: State-of-the-art and challenges
Change detection based on remote sensing (RS) data is an important method of detecting
changes on the Earth's surface and has a wide range of applications in urban planning …
changes on the Earth's surface and has a wide range of applications in urban planning …
Deep learning for change detection in remote sensing: a review
ABSTRACT A large number of publications have incorporated deep learning in the process
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …
End-to-end change detection for high resolution satellite images using improved UNet++
Change detection (CD) is essential to the accurate understanding of land surface changes
using available Earth observation data. Due to the great advantages in deep feature …
using available Earth observation data. Due to the great advantages in deep feature …
Change detection in multisource VHR images via deep siamese convolutional multiple-layers recurrent neural network
With the rapid development of Earth observation technology, very-high-resolution (VHR)
images from various satellite sensors are more available, which greatly enrich the data …
images from various satellite sensors are more available, which greatly enrich the data …
Change detection from very-high-spatial-resolution optical remote sensing images: Methods, applications, and future directions
Change detection is a vibrant area of research in remote sensing. Thanks to increases in the
spatial resolution of remote sensing images, subtle changes at a finer geometrical scale can …
spatial resolution of remote sensing images, subtle changes at a finer geometrical scale can …
Exchange means change: An unsupervised single-temporal change detection framework based on intra-and inter-image patch exchange
Change detection is a critical task in studying the dynamics of ecosystems and human
activities using multi-temporal remote sensing images. While deep learning has shown …
activities using multi-temporal remote sensing images. While deep learning has shown …
Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection
Deep learning for change detection is one of the current hot topics in the field of remote
sensing. However, most end-to-end networks are proposed for supervised change …
sensing. However, most end-to-end networks are proposed for supervised change …
A survey of change detection methods based on remote sensing images for multi-source and multi-objective scenarios
Y You, J Cao, W Zhou - Remote Sensing, 2020 - mdpi.com
Quantities of multi-temporal remote sensing (RS) images create favorable conditions for
exploring the urban change in the long term. However, diverse multi-source features and …
exploring the urban change in the long term. However, diverse multi-source features and …
Recent advances of generative adversarial networks in computer vision
YJ Cao, LL Jia, YX Chen, N Lin, C Yang, B Zhang… - IEEE …, 2018 - ieeexplore.ieee.org
The appearance of generative adversarial networks (GAN) provides a new approach and
framework for computer vision. Compared with traditional machine learning algorithms, GAN …
framework for computer vision. Compared with traditional machine learning algorithms, GAN …
From W-Net to CDGAN: Bitemporal change detection via deep learning techniques
Traditional change detection methods usually follow the image differencing, change feature
extraction, and classification framework, and their performance is limited by such simple …
extraction, and classification framework, and their performance is limited by such simple …