Yushi Chen

TL;DR: The concept of deep learning is introduced into hyperspectral data classification for the first time, and a new way of classifying with spatial-dominated information is proposed, which is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression.

TL;DR: This paper proposes a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery and reveals that the proposed models with sparse constraints provide competitive results to state-of-the-art methods.

TL;DR: A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN) and a novel deep architecture is proposed, which combines the spectral-spatial FE and classification together to get high classification accuracy.

TL;DR: In this paper, the authors present a systematic review of deep learning-based hyperspectral image classification literatures and compare several strategies for this topic, which can provide some guidelines for future studies on this topic.

TL;DR: The usefulness and effectiveness of GAN for classification of hyperspectral images (HSIs) are explored for the first time and the proposed models provide competitive results compared to the state-of-the-art methods.