Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …

Crack detection plays a vital role in concrete surface maintenance. Deep-learning-based
methods have achieved state-of-the-art results. However, these methods have some …

While deep learning algorithms have achieved good results in hyperspectral image (HSI)
classification, several supervised classification algorithms rely on a large number of labeled …

Deep learning approaches have recently been widely applied to the classification of
hyperspectral images (HSIs) and achieve good capability. Deep learning can effectively …

Hyperspectral remote sensing image (HSI) has the unique advantages of spectral continuity
as well as synchronous acquisition of both image and spectra of objects, which can achieve …

Semisupervised deep learning methods (DLMs) can mitigate the dependence on large
amounts of labeled samples using a small number of labeled samples. However, for …

In this paper, superpixel features and extended multi-attribute profiles (EMAPs) are
embedded in a multiple kernel learning framework to simultaneously exploit the local and …

Hyperspectral image (HSI) classification is essential in remote sensing image analysis. The
classification methods based on deep learning have attracted more and more attention …

Deep subspace learning (DSL) plays an essential role in hyperspectral image (HSI)
classification, providing an effective solution tool to reduce the redundant information of HSI …

The integration of spectral and spatial information is crucial in remotely sensed
hyperspectral image classification. Some available approaches extract spatial features …