Results 51 to 60 of about 1,727,336 (191)
Advanced Intelligent Systems, EarlyView.As food insecurity and global food demands surge, artificial intelligence (AI)‐based technologies offer promising opportunities to reduce food loss and waste. In this perspective, current AI adoption across the food supply chain is assessed using various academic, industry, and policy sources.
Akansha Prasad +5 more
wiley +1 more source
Object identification and characterization with hyperspectral imagery to identify structure and function of Natura 2000 habitats [PDF]
, 2010 Habitat monitoring of designated areas under the EU Habitats Directive requires every 6 years information on area, range, structure and function for the protected (Annex I) habitat types.
Spanhove, T. +7 more
core
Customizing kernel functions for SVM-based hyperspectral image classification [PDF]
, 2008 Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms.
Damper, R. I. +7 more
core +2 more sources
Remote Sensing to Characterize River Floodplain Structure and Function
Remote Sensing, 2022 Advancing understanding of the complexities and expansive spatial scales of river ecology can be enhanced through the application of remote sensing. We obtained satellite (Quickbird) and airborne (LIDAR, hyperspectral, multispectral, and thermal) imagery
F. Richard Hauer +2 more
doaj +1 more source
Advanced Intelligent Systems, EarlyView.A distributed wireless sensing framework enables noninvasive monitoring of plant salinity stress through leaf reflectance measurements acquired with portable optical sensors. The collected data are processed using TinyML‐based neural network models deployed at the edge, enabling accurate, real‐time, and low‐cost classification of salinity levels, while
Ali Ahmad +4 more
wiley +1 more source
Prediction and mapping of tobacco yield with fresh leaf mass using hyperspectral sensing data
Smart Agricultural TechnologyTobacco yield is important for agricultural management and policy making. Hyperspectral remote sensing has advantages in predicting and mapping crop yield, providing both yield values and the spatial distribution of yield.
Junying Li +6 more
doaj +1 more source
Advanced Intelligent Systems, EarlyView.This study assesses the suitability and performance of single‐image super‐resolution methods when applied directly, and without any fine‐tuning, to multispectral images of maize and sunflower crops. It evaluates not only super‐resolution performance, but also the preservation of spectral integrity and vegetation‐index reliability. Multispectral imagery
Abhiram Karukayil +4 more
wiley +1 more source
Validation and calibration of remote sensing data products on test site DEMMIN. [PDF]
, 2008 One of the main research objectives of the working group Thematic Processors and Validation (TPV) of the Remote Sensing Data Centre (DLR-DFD) is the automation of remote sensing data processing.
Fichtelmann, Bernd +7 more
core
Low‐Rank and Spectral‐Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery [PDF]
Wireless Communications and Mobile Computing, 2021 Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation.
openaire +1 more source
Archaeological Prospection, EarlyView.ABSTRACT We investigated the Písečný vrch (Czech Republic), a site exploited for quartz mining in both prehistoric times and the 20th century. Due to their morphological similarity across all mining periods, the differentiation between pits with archaeological potential and recent ones is challenging.
Jan Fišer +5 more
wiley +1 more source