Search Results for author:
Found 4 papers, 2 papers with code
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation
These results are consistent for experiments on above-ground biomass prediction in Tanzania and stem volume prediction in Norway, representing a diversity in parameters and forest types that emphasises the robustness of the approach.
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.
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.
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.
