Deep learning-based unsupervised change detection (CD) methods compare a prechange
and a postchange image in deep feature space and require precise knowledge of the event …

In this paper, we propose a fast and efficient multitemporal despeckling method. The key
idea of the proposed approach is the use of the ratio image, provided by the ratio between …

Stacked autoencoder is effective in image denoising and classification when it is used for
synthetic aperture radar (SAR) change detection. However, the resulting features may not be …

In this paper, we propose a new test statistic for unsupervised change detection in
polarimetric radar images. We work with multilook complex covariance matrix data, whose …

PolSAR images provide rich polarimetric information, however, due to the limitations of the
imaging system, the spatial resolution decreases while the richer polarimetric information is …

Recently, the frequency of natural and environmental disasters has increased significantly,
causing constant changes on the Earth's surface. Synthetic Aperture Radar (SAR) data have …

We propose a despeckling algorithm for multitemporal synthetic aperture radar (SAR)
images based on the concepts of block-matching and collaborative filtering. It relies on the …

Monitoring and assessing eruption hazard at Merapi volcano are challenging due to steep
slopes, the harsh environment at the summit, and hazardous access during both volcanic …

The interpretation of multidimensional synthetic aperture radar (SAR) data often requires
expert knowledge. In fact, it requires to simultaneously consider several time series of …

Change detection has played an increasingly important role in multitemporal remote
sensing applications recently. Long time series analysis is providing new information of land …