Machine learning methods for synthetic aperture radar (SAR) image automatic target
recognition (ATR) can be divided into two main types: traditional methods and deep learning …

Remote sensing change detection (RSCD) focuses on identifying regions that have
undergone changes between two remote sensing images captured at different times …

Height estimation from a single Synthetic Aperture Radar (SAR) image has demonstrated a
great potential in real-time environmental monitoring and scene understanding. The …

Time-series PolSAR are capable for continuous change monitoring of natural resources and
urban land-covers regardless of weather and lighting conditions. However, in the big SAR …

Efficiently extracting features from satellite images is crucial for classification and post-
processing activities. Many feature representation models have been created for this …

The augmentation of hyperspectral images requires the design of high-density feature
analysis & band-fusion models that can generate multimodal imagery from limited …

Effective recognition of radar jamming is of great importance in improving radar system's anti-
jamming capability. Existing radar jamming recognition methods based on convolutional …

Change detection is a crucial remote sensing (RS) application because it can locate the
interesting changed regions and provide corresponding time-series information with …

Coherent change detection (CCD) is an approach for detecting changes in a region that is
based on interferometric-type processing of existing data or imagery. It is utilized in satellite …