Hyperspectral image (HSI) classification refers to identifying land-cover categories of pixels
based on spectral signatures and spatial information of HSIs. In recent deep learning-based …

With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …

Automated Facial Expression Recognition (FER) in the wild using deep neural networks is
still challenging due to intra-class variations and inter-class similarities in facial images …

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

With the development of hyperspectral imaging technology, hyperspectral images (HSIs)
have become important when analyzing the class of ground objects. In recent years …

Training a modern deep neural network on massive labeled samples is the main paradigm
in solving the scene classification problem for remote sensing, but learning from only a few …

Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-
scene classification, samples between source and target scenes are not drawn from the …

Multisource remote sensing data provide abundant and complementary information for land
cover classification. Existing classification methods mainly focus on designing a multistream …

Cross-scene hyperspectral image classification (HSIC) is a challenging topic in remote
sensing, especially when there are no labels in the target domain. Domain adaptation (DA) …

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are
increasing, and pixel-oriented classification has attracted much attention. Recently, graph …