Convolutional neural networks have made a great breakthrough in recent remote sensing
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …

Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their
corresponding abundance fractions from remotely sensed scenes. Most traditional …

Sea–land segmentation of remote sensing images is of great significance to the dynamic
monitoring of coastlines. However, the types of objects in the coastal zone are complex, and …

Spectral unmixing expresses the mixed pixels existing in hyperspectral images as the
product of endmembers and their corresponding fractional abundances, which has been …

Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing
technology, leveraging the availability of pre-existing endmember spectral libraries. In recent …

Deep-learning-based synthetic aperture radar (SAR) automatic target recognition (ATR)
algorithms have achieved outstanding performance under the condition of hundreds or …

Evolutionary multiobjective optimization is vigorous but not efficient in solving the
hyperspectral sparse unmixing problem, while most related algorithms suffer from high …

In recent years, deep learning (DL) has accelerated the development of hyperspectral image
(HSI) processing, expanding the range of applications further. As a typical model of …

Multiobjective optimization aims at addressing the conflicting objectives, which has been
introduced to improve the performance of sparse hyperspectral unmixing. Recently …

With the help of endmember spectral library, sparse unmixing techniques have been
successfully applied to hyperspectral image interpretation. The inclusion of spatial …