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A Deep Learning Architecture for Unsupervised Feature Extraction from Multimission SAR Time Series | IEEE Conference Publication | IEEE Xplore

A Deep Learning Architecture for Unsupervised Feature Extraction from Multimission SAR Time Series


Abstract:

Feature learning algorithms that use deep neural networks have shown to outperform traditional hand-crafted feature extraction methods when applied to satellite image tim...Show More

Abstract:

Feature learning algorithms that use deep neural networks have shown to outperform traditional hand-crafted feature extraction methods when applied to satellite image time series. This learned features can be used in a multitude of applications such as classification, semantic segmentation, and change detection, among others. In this paper, we employ a feature learning technique to extract representative features from multimission polarimetric SAR (PolSAR) satellite image time series (SITS). To this end, we implement a formulation combining a 1-dimensional convolutional neural network and a stacked auto encoder. We performed experiments on a multimission and multitemporal PolSAR dataset and validated the extracted features through their utility as features in an unsupervised classification problem.
Date of Conference: 07-12 July 2024
Date Added to IEEE Xplore: 05 September 2024
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Conference Location: Athens, Greece

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