A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …

Convolutional neural networks (CNNs) exhibit good performance in image processing tasks,
pointing themselves as the current state-of-the-art of deep learning methods. However, the …

A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide
spectral range. Each band reflects the same scene, composed of various objects imaged at …

Band selection, by choosing a set of representative bands in a hyperspectral image, is an
effective method to reduce the redundant information without compromising the original …

Hyperspectral images (HSIs) are a powerful source of reliable data in various remote
sensing applications. But due to the large number of bands, HSI has information …

Unsupervised band selection aims to select informative spectral bands to preprocess
hyperspectral images (HSIs) without using labels. Traditional band selection methods only …

Several versions of convolutional neural network (CNN) were developed to classify
hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral …

Hyperspectral data usually consists of hundreds of narrow spectral bands and provides
more detailed spectral characteristics compared to commonly used multispectral data in …

Whether from the perspective of agricultural production or food safety, potato germination
detection is of great significance. Since the features (color, texture and context) of the …

In this letter, a band-selection approach based on the deep convolutional neural network
(CNN) and distance density (DD) is proposed. This method effectively mitigates the curse of …