Accurate estimation of aboveground biomass of grasslands is key to sustainable grassland
utilization. However, most satellites cannot provide high temporal and spatial resolution …

Feature learning-based polarimetric synthetic aperture radar (PolSAR) classification model
will generally suffer from the challenge of deficient labeled pixels. In this paper, we propose …

Change detection is a topic of great interest in remote sensing. A good similarity metric to
compute the variations among the images is the key to high-quality change detection …

Change detection techniques play an essential role in Remote Sensing applications, such
as environmental monitoring, governmental planning, and studies of areas affected by …

Polarimetric synthetic aperture radar (PolSAR) image classification has played an important
role in PolSAR data application. Deep learning has achieved great success in PolSAR …

In remote sensing applications, image classification algorithms normally require parameter
optimization strategies to adapt to the complexities of the data and determine the model's …

This paper address unsupervised classification strategies applied to Polarimetric Synthetic
Aperture Radar (PolSAR) images. We analyze the performance of complex Wishart …

In recent decades, spatial feature extraction has greatly improved the performance of
hyperspectral image (HSI) classification. This paper presents an HSI classification method …

The Earth's environment is continually changing due to both human and natural factors.
Timely identification of the location and kind of change is of paramount importance in …

针对生鲜电商订单拆分情况频发, 冷链配送效率低且配送成本高的问题, 将两级订单分配与冷链
配送联合优化, 以最小化订单配送成本为目标构建非线性混合整数规划模型 …