Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

Domain adaptation techniques have been widely applied to the problem of cross-scene
hyperspectral image (HSI) classification. Most existing methods use convolutional neural …

Domain adaption (DA) is a challenging task that integrates knowledge from source domain
(SD) to perform data analysis for target domain. Most of the existing DA approaches only …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …

The success of the supervised classification of remotely sensed images acquired over large
geographical areas or at short time intervals strongly depends on the representativity of the …

For the classification of hyperspectral images (HSIs), this paper presents a novel framework
to effectively utilize the spectral-spatial information of superpixels via multiple kernels, which …

Cross-scene classification is one of the major challenges for hyperspectral image (HSI)
classification, especially for target scenes without label samples. Most traditional domain …

Land-cover mapping in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …