The precise classification of land covers with hyperspectral imagery (HSI) is a major
research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) …

Transformer has been widely applied in image processing tasks as a substitute for
convolutional neural networks (CNNs) for feature extraction due to its superiority in global …

Recently, deep learning-based hyperspectral image (HSI) classification techniques are
flourishing and exhibit good performance, where cross-domain information is usually utilized …

With the development of hyperspectral sensors, more and more hyperspectral images can
be acquired, and the pixel-oriented classification of hyperspectral images has attracted the …

This study proposes an improved algorithm based on the You Only Look Once v7 (YOLOv7)
to address the low accuracy of apple fruit target recognition caused by high fruit density …

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and
physiological states. In facility agriculture, it is vital to rapidly and accurately obtain the …

Airborne light detection and ranging (LiDAR) technology has been widely utilized for
collecting three-dimensional (3D) point cloud data on forest scenes, enabling the generation …

By fusing a low-resolution hyperspectral image (LrMSI) with an auxiliary high-resolution
multispectral image (HrMSI), hyperspectral image super-resolution (HISR) can generate a …

Water body extraction is a typical task in the semantic segmentation of remote sensing
images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional …

The deployment of convolutional neural networks (CNNs) to classify hyperspectral images is
extensively discussed in the research study. A number of different algorithms and …