Developments in space-based hyperspectral sensors, advanced remote sensing, and
machine learning can help crop yield measurement, modelling, prediction, and crop …

The deep learning method has achieved great success in hyperspectral image
classification, but the lack of labeled training samples still restricts the development and …

Abstract Chlorophyll-a (Chl-a) is a crucial pigment in algae and macrophytes, which makes
the concentration of total Chl-a in the water column (total Chl-a) an essential indicator for …

Low-rank and sparse representation (LRSR) model has gained popularity in hyperspectral
image (HSI) classification. However, most existing LRSR models are limited by the highly …

With the development of industry and the exploitation of mineral resources, a large amount
of heavy metal elements will leak into the natural environment, seriously affecting the health …

Natural gas is an important clean energy source that is mainly transported through buried
pipelines. However, slight leakage during transportation is inevitable and needs to be …

Insufficient prior knowledge of a captured hyperspectral image (HSI) scene may lead the
experts or the automatic labeling systems to offer incorrect labels or ambiguous labels (ie …

Forest ecosystem detection and assessment usually requires accurate spatial distribution
information of forest tree species. Remote sensing technology has been confirmed as the …

In recent years, the problem of heavy metal pollution in agriculture caused by industrial
development has been particularly prominent, directly affecting food and ecological …