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 MoreMetadata
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
ISBN Information: