Class imbalance is a key issue affecting the performance of computer vision applications
such as medical image analysis, objection detection and recognition, image segmentation …

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 …

The automatic production of land use/land cover maps continues to be a challenging
problem, with important impacts on the ability to promote sustainability and good resource …

In recent years, hyperspectral image classification (HSIC) has achieved impressive progress
with emerging studies on deep learning models. However, the classification performance …

Hyperspectral image (HSI) classification often faces the problem of multiclass imbalance,
which is considered to be one of the major challenges in the field of remote sensing. In …

With the continuous progress of computer deep learning technology, convolutional neural
network (CNN), as a representative approach, provides a unique solution for hyperspectral …

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 …

Higher dimensionality, Hughes phenomenon, spatial resolution of image data, and
presence of mixed pixels are the main challenges in a multi-spectral image classification …

Due to the strong concealment of electricity theft and the limitation of inspection resources,
the number of power theft samples mastered by the power department is insufficient, which …

Land cover maps are a critical tool to support informed policy development, planning, and
resource management decisions. With significant upsides, the automatic production of Land …