Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …

Hyperspectral image (HSI) classification is one of the hotspots in remote sensing, and many
methods have been continuously proposed in recent years. However, it is still challenging to …

The fusion of multi-spectral and synthetic aperture radar (SAR) images could retain the
advantages of each data, hence benefiting accurate land cover classification. However …

In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for
the classification of hyperspectral images with limited training data. Our proposition is based …

Convolutional neural networks (CNNs) can automatically learn features from the
hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features …

Hyperspectral remote sensing has tremendous potential for monitoring land cover and water
bodies from the rich spatial and spectral information contained in the images. It is a time and …

Deep learning methods have been widely used in hyperspectral image classification. Most
existing CNN-based deep learning methods can extract spatial features by the local …

Convolutional neural networks (CNN) can automatically learn features from the
hyperspectral image data, which could avoid the difficulty of manually extracting features …

The classification of hyperspectral image (HSI) has become the focus of the remote sensing
field. However, limited training data, which makes the classification task face a major …

In this paper, a novel forest disaster detection method based on fuzzy c-means (FCM)
algorithm and genetic algorithm (GA) with neighborhood information (F-NGA) is proposed …