Results 71 to 80 of about 4,082 (191)
Remote Sensing, 2019 Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI.
Hong Huang, Meili Chen, Yule Duan
doaj +1 more source
Journal of Microscopy, EarlyView.Abstract Brain surgery is a widely practised and effective treatment for brain tumours, but accurately identifying and classifying tumour boundaries is crucial to maximise resection and avoid neurological complications. This precision in classification is essential for guiding surgical decisions and subsequent treatment planning.
Neetu Sigger +2 more
wiley +1 more source
An Efficient Graph Convolutional RVFL Network for Hyperspectral Image Classification
, 2023 Graph convolutional networks (GCN) have emerged as a powerful alternative tool for analyzing hyperspectral images (HSIs). Despite their impressive performance, current works strive to make GCN more sophisticated through either elaborate architecture or ...
Min Zhang +4 more
core +1 more source
Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities
The Plant Phenome Journal, Volume 9, Issue 1, December 2026.Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang +12 more
wiley +1 more source
Multi-Scale CNN Based Garbage Detection of Airborne Hyperspectral Data
IEEE Access, 2019 Garbage detection is important for environmental monitoring in large areas. However, the manual patrol is time-consuming and labor-intensive. This paper proposes a method for monitoring garbage distribution in large areas with airborne hyperspectral data.
Dan Zeng +3 more
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 This article proposed a novel spectral-spatial classification framework for hyperspectral image (HSI) through combining collaborative representation (CR) and maximum margin projection (MMP).
Haoyang Yu +6 more
doaj +1 more source
eFood, Volume 7, Issue 3, June 2026.This review synthesizes AI advancements in food systems, leveraging machine learning, computer vision, robotics, and IoT for 96%–100% accurate quality inspection, 30% reduced downtime, and enhanced traceability from farm to fork. It highlights transformative potential in sustainability and SDGs while addressing data, ethical, and scalability challenges
Muhammad Waqar +9 more
wiley +1 more source
Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification
, 2019 Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying.
Qifan Yang +4 more
core +1 more source
eFood, Volume 7, Issue 3, June 2026.AI application can be very helpful in addressing different issues and shaping novel techniques in food production, food safety and quality, and food intake. AI application in food science, such as the food industry and processing, food safety and packaging, and nutrition.
Yaseen Galali +7 more
wiley +1 more source
Hyperspectral image classification with convolutional neural networks
, 2015 Hyperspectral image (HSI) classification is one of the most widely used methods for scene analysis from hyperspectral imagery. In the past, many different engineered features have been proposed for the HSI classification problem.
De Neve, Wesley +9 more
core +1 more source