The convolutional neural network (CNN) is widely used in the task of hyperspectral image
(HSI) classification. However, for the HSI of three-dimensional characteristics, the 2D CNN …

This paper presents a novel hyperspectral image (HSI) classification framework by
exploiting multi-scale spectral-spatial features via hierarchical recurrent neural networks …

Recently, deep learning methods based on the combination of spatial and spectral features
have been successfully applied in hyperspectral image (HSI) classification. To improve the …

Rapid and accurate prediction of crop nitrogen content is of great significance for guiding
precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was …

Recently, for the task of hyperspectral image classification, deep-learning-based methods
have revealed promising performance. However, the complex network structure and the time …

Due to the redundancy of imaging systems, multispectral and panchromatic images are of
higher spatial resolutions and characterized by different attributes, and are often fused …

Convolutional neural networks provide an ideal solution for hyperspectral image (HSI)
classification. However, the classification effect is not satisfactory when limited training …

There is a growing demand for the detection of endangered plant species through machine
learning approaches. Ziziphus lotus is an endangered deciduous plant species in the …

Remote sensing sensors-based image processing techniques have been widely applied in
non-destructive quality inspection systems of agricultural crops. Image processing and …

Data classification effectively classifies the data based on the labeled class distribution. To
classify the data using the imbalanced distribution poses a significant challenge in the class …