Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …

Soil organic matter (SOM) is the main source of soil nutrients. Rapid determination of SOM
content is of great significance for guiding field management. The change of SOM content …

Convolutional neural networks (CNNs) have recently been demonstrated to be able to
substantially improve the land cover classification accuracy of hyperspectral images …

Recent advancements in hyperspectral remote sensing bring exciting opportunities for
various domains. Precision agriculture is one of the most widely-researched examples here …

Premium maize varieties are the focus of attention of farmers, breeders, food manufacturers,
and people in other industries. Maize seed fraud causes huge financial losses to these …

Harmful algal blooms have dangerous repercussions for biodiversity, the ecosystem, and
public health. Automatic identification based on remote sensing hyperspectral image …

Convolutional neural-network-based autoencoders, which can integrate the spatial
correlation between pixels well, have been broadly used for hyperspectral unmixing and …

摘要针对基于卷积自编码进行空-谱联合的高光谱解混方法中, 过度引入像元光谱之间的空间
相关性导致丰度过于平滑的现象, 提出一种结合注意力机制的双流卷积自编码高光谱解混方法 …

Spectral Unmixing is an important technique in remote sensing used to analyze
hyperspectral images to identify endmembers and estimate abundance maps. Over the past …

We propose a solution to the issue of hyperspectral unmixing methods that only consider
local spectral–spatial information of the pixel level or pixel block. The proposed method is a …