In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble (EL). particular, Extreme Gradient Boosting (XGBoost) and Light Machine (LightGBM) methods attracted researchers’ attention in data science due to their superior results compared other commonly used ML algorithms. Despite popularity within the compu...

Although deep learning has achieved great success in image classification tasks, its performance is subject to the quantity and quality of training samples. For polarimetric synthetic aperture radar (PolSAR) images, it nearly impossible annotate images from visual interpretation. Therefore, urgent for remote sensing scientists develop new techniques PolSAR under condition very few In this lette...

The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER) works well on optical images. However, it may generate poor superpixels for Polarimetric synthetic aperture radar (PolSAR) images due to th...

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by ...

Using the context as a source of ancillary information in classification process provides a powerful tool to obtain better class discrimination. Modelling context using Markov Random Fields (MRFs) and combining with Bayesian approach, a context-based supervised classification method is proposed. In this framework, to have a full use of the statistical a priori knowledge of the data, the spatial...

Polarimetric calibration (PolCAL) of Synthetic Aperture Radar (SAR) images is a significant preprocessing for further applications. Since the reflection symmetry property of distributed objects can provide stable constraints for PolCAL. It is reasonable to extract these reference samples before calibration. The helix scattering generally appears in complex urban area and disappears for a natura...

New scattering models are constantly emerging to extract the detailed polarization information of ground targets. They contribute refined interpretation Polarimetric Synthetic Aperture Radar (PolSAR) images. However, an increasingly prominent problem lies ahead model decomposition methods when there similar for a scheme. It is difficult separate power mechanisms reasonably and robustly. Therefo...

PolSAR image classification using a semi-supervised classifier based on hypergraph learning Binghui Wei, Jun Yu, Cheng Wang, Hongyi Wu & Jonathan Li a Department of Computer Science, Xiamen University, Xiamen, China b School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China c Faculty of Environment, University of Waterloo, Waterloo, Canada Published online: 29 Apr ...