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Found 6 papers, 2 papers with code
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces and reduce the impact of change pixels on the learning objective.
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection.
Unsupervised Image Regression for Heterogeneous Change Detection
First, our method quantifies the similarity of affinity matrices computed from co-located image patches in the two images.
Decision fusion with multiple spatial supports by conditional random fields
In this paper, we follow these two observations and encode them as priors in an energy minimization framework based on conditional random fields (CRFs), where classification results obtained at pixel and region levels are probabilistically fused.
Remote sensing image regression for heterogeneous change detection
In this paper we propose a framework, based on image regression, to perform change detection in heterogeneous multitemporal satellite images, which has become a main topic in remote sensing.
A clustering approach to heterogeneous change detection
Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation.
