Over the past three decades, significant developments have been made in hyperspectral
imaging due to which it has emerged as an effective tool in numerous civil, environmental …

Object detection in optical remote sensing images, being a fundamental but challenging
problem in the field of aerial and satellite image analysis, plays an important role for a wide …

As a list of remotely sensed data sources is available, how to efficiently exploit useful
information from multisource data for better Earth observation becomes an interesting but …

The deep convolutional neural network (CNN) is of great interest recently. It can provide
excellent performance in hyperspectral image classification when the number of training …

Anomaly detection of hyperspectral data has been gaining particular attention for its ability in
detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants …

A novel method for anomaly detection in hyperspectral images is proposed. The method is
based on two ideas. First, compared with the surrounding background, objects with …

Without any prior information, hyperspectral anomaly detection is devoted to locating targets
of interest within a specific scene by exploiting differences in spectral characteristics …

More recently, remote sensing image classification has been moving from pixel-level
interpretation to scene-level semantic understanding, which aims to label each scene image …

A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on
low-rank and sparse representation. The proposed method is based on the separation of the …

Due to the recent advances in satellite sensors, a large amount of high-resolution remote
sensing images is now being obtained each day. How to automatically recognize and …