In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-
effective technology to capture high spatial and temporal resolution remote sensing (RS) …

Hyperspectral imaging is an indispensable technology for many remote sensing
applications, yet expensive in terms of computing resources. It requires significant …

Data processing of low-altitude remote sensing visible images from UAVs is one of the hot
research topics in precision agriculture aviation. In order to solve the problems of large …

In recent years, convolutional neural network (CNN)-based algorithms have been widely
used in remote sensing image processing and show tremendous performance in a variety of …

Convolutional neural networks (CNN) have been extensively employed for image
classification due to their high accuracy. However, inference is a computationally-intensive …

Much research is still underway to achieve long-term and real-time monitoring using data
from vision-based sensors. A major challenge is handling and processing enormous amount …

Onboard data processing for on-the-fly decision-making applications has recently gained
momentum in the field of remote sensing. In this context, hyperspectral anomaly detection …

Real-time detection of satellite remote sensing images is one of the key technologies in the
field of remote sensing, which requires not only high-efficiency algorithms, but also low …

Performing remote sensing scene classification (RSSC) directly on satellites can alleviate
data downlink burdens and reduce latency. Compared to convolutional neural networks …

Lossless hyperspectral images have the advantage of reducing the data size, hence saving
on storage and transmission costs. This study presents a dynamic pipeline hardware design …