Despite the good performance of methods such as wavelet transform, Gabor filter bank, and
scale-invariant feature transform (SIFT) as the first step of image segmentation, they have …

This paper focuses on the high-resolution (HR) remote sensing images semantic
segmentation task, whose goal is to predict semantic labels in a pixel-wise manner. Due to …

Random ferns—as a less known example of ensemble learning—have been successfully
applied in many computer vision applications ranging from keypoint matching to object …

Semantic segmentation of very-high-resolution (VHR) remote sensing images plays an
important role in the intelligent interpretation of remote sensing since it predicts pixel-level …

Land cover classification is an important application for polarimetric synthetic aperture radar
(PolSAR). Target polarimetric response is strongly dependent on its orientation …

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and
more popular in recent years. As we all know, PolSAR image classification is actually a …

The deep convolutional neural network (CNN) has been extensively applied to polarimetric
synthetic radar (PolSAR) imagery classification. However, its success is greatly dependent …

Convolutional neural network (CNN) has achieved remarkable success in polarimetric
synthetic aperture radar (PolSAR) image classification. However, the PolSAR image …

This paper focuses on evaluating the ability and contribution of using backscatter intensity,
texture, coherence, and color features extracted from Sentinel-1A data for urban land cover …

The Grain to Green Program (GTGP) and eco-environmental emigration have been
employed to alleviate poverty and control rocky desertification in the Southwest China Karst …