Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Central to the looming paradigm shift toward data-intensive science, machine-learning
techniques are becoming increasingly important. In particular, deep learning has proven to …

Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …

In recent years, unsupervised feature learning based on a neural network architecture has
become a hot new topic for research [1]-[4]. The revival of interest in such deep networks can …

Polarization can provide information largely uncorrelated with the spectrum and intensity.
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …

Synthetic aperture radar (SAR) imaging system is usually an observation of the earths'
surface. It means that rich structures exist in SAR images. Convolutional neural network …

Estimation of forest aboveground biomass (AGB) is crucial for various technical and
scientific applications, ranging from regional carbon and bioenergy policies to sustainable …

Aiming at improving the classification accuracy with limited numbers of labeled pixels in
polarimetric synthetic aperture radar (PolSAR) image classification task, this paper presents …

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great
importance in image processing for remote sensing applications. However, it is a …

The classification of large-scale high-resolution synthetic aperture radar (SAR) land cover
images acquired by satellites is a challenging task, facing several difficulties such as …