Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model

In this article, we propose a novel and simple automatic model based on multimodal anomaly feature learning in a residual space, aiming at solving the binary classification problem of temporal change detection (CD) between pairs of heterogeneous remote sensing images. The model starts by learning fr...

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Bibliographic Details

Published in:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Main Authors:	Redha Touati, Max Mignotte, Mohamed Dahmane
Format:	Article
Language:	English
Published:	IEEE 2020-01-01
Subjects:	Anomalous patterns change detection (CD) deep learning feature space reconstruction heterogeneous remote sensing multimodal anomaly detector
Online Access:	https://ieeexplore.ieee.org/document/8966599/