A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images

By Jia Liu, Maoguo Gong, Kai Qin, Puzhao Zhang. IEEE Transactions on Neural Networks and Learning Systems, 2018

452 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/3bd953f567e776c337f3484c95218af71140344c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Atmospheric Science

In Media Technology

Citations in the corpus

Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, methodology): “In [ 172 One interesting direction that is also new involves planetary CD, in which (according to our knowledge) only a few authors have conducted work.”
Fully Convolutional Siamese Networks for Change Detection (2018, methodology): “…began to be used in conjunction with simple machine learning techniques [6]; recently, more elaborate machine learning techniques (deep learning) are dominating most problems in the image analysis ﬁeld, and this evolution is slowly reaching the problem of change detection [7, 8, 9, 10, 11, 3, 12].”
DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images (2020, methodology): “Neural networks with FCN [27] structures are widely used in remote sensing change detection tasks [28]–[33].”
Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images (2017, methodology): “In [7], a symmetric convolutional coupling network (SCCN) is applied to detect changes between optical and SAR images.”
Changer: Feature Interaction is What You Need for Change Detection (2022, methodology): “In this feature space, the ultimate change map is obtained by the threshold algorithm [24].”
A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection (2020, methodology): “For the training process, to constrain the data representations, different loss functions have been explored in CD, such as contrastive loss [26,27,33] and triplet loss [28].”
Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model (2019, methodology): “[5] used a symmetric convolutional neural network to extract multitemporal image features and then transformed them into features in a consistent space, which also achieved good results.”
Deep learning for change detection in remote sensing: a review (2022, methodology): “A transformation function can be built based on the principle that unchanged pixels at the same position in two input images have similar representations (Liu et al. 2016a; Zhang et al. 2016b).”
Multitask learning for large-scale semantic change detection (2018, methodology): “Other methods use unsupervised learning approaches such as iterative training (Liu et al., 2016), autoencoders (Zhao et al.”
Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks (2019, background): “DL has been shown to be a great success in computer vision, natural language processing, and many other fields [40]–[46].”
Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network (2020, methodology): “In fact, even though DL has been applied to CD, there are currently only a few methods [30]–[32] that can be used for heterogeneous image CD not to mention a universal CD method that can perform well in both homogeneous and heterogeneous VHR images.”