Modern hyperspectral imaging systems produce huge datasets potentially conveying a great
abundance of information; such a resource, however, poses many challenges in the …

Hyperspectral images (HSIs) have a cube form containing spatial information in two
dimensions and rich spectral information in the third one. The high volume of spectral bands …

Wetlands are one of the most important ecosystems on the Earth, and using hyperspectral
remote sensing (RS) technology for fine wetland map** is important for restoring and …

Due to the limitations of single-source data, joint classification using multisource remote
sensing data has received increasing attention. However, existing methods still have certain …

The monitoring of coastal wetlands is of great importance to the protection of marine and
terrestrial ecosystems. However, due to the complex environment, severe vegetation …

Currently, most existing methods using hyperspectral images to assist seed identification
only consider the spectral information but ignore the spatial information resulting in …

Deep learning has already been proved as a powerful state-of-the-art technique for many
image understanding tasks in computer vision and other applications including remote …

Various machine learning and deep learning techniques have been proposed for
classification purposes in the case of hyperspectral imaging. Among all the machine …

Along with wetland loss, the damming effect on hydrological modification in wetland is
another less debated and challenging topic, which needs to have urgent attention. The …

Map** different land cover types with satellite remote sensing data is significant for
restoring and protecting natural resources and ecological services in coastal wetlands. In …