Estimating Crop Seed Composition Using Machine Learning from Multisensory UAV Data
Remote Sensing, 2022 The pre-harvest estimation of seed composition from standing crops is imperative for field management practices and plant phenotyping. This paper presents for the first time the potential of Unmanned Aerial Vehicles (UAV)-based high-resolution ...
Kamila Dilmurat +4 more
doaj +1 more source
CatLC: Catalonia Multiresolution Land Cover Dataset
Scientific Data, 2022 Measurement(s) RGB orthophoto image • Infrared orthophoto image • Radar backscattering • Hyperspectral image • Topographic measurements • Landcover classification Technology Type(s) Airborne camera • Satellite SAR sensor • Satellite hyperspectral sensor •
Carlos García +3 more
doaj +1 more source
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery [PDF]
, 2009 This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing ...
Moussaoui, Saïd +10 more
core +1 more source
Using an airborne hyperspectral and LiDAR integrated sensor approach to spectrally discriminate and map savanna bush encroaching species in the Greater Kruger National Park region [PDF]
, 2011 Includes abstract.Includes bibliographical references (leaves 105-113).Bush encroachment is an environmental phenomenon which affects arid and semi-arid savanna rangelands across the world.
Naidoo, Laven
core +1 more source
Cathodoluminescence hyperspectral imaging on the nanometre scale [PDF]
, 2011 Extending cathodoluminescence microscopy into the hyperspectral imaging mode brings significant benefits to an already powerful nano-scale characterization tool.
Martin, Robert +3 more
core +4 more sources
Feature-Decision Level Collaborative Fusion Network for Hyperspectral and LiDAR Classification
Remote Sensing, 2023 The fusion-based classification of hyperspectral (HS) and light detection and ranging (LiDAR) images has become a prominent research topic, as their complementary information can effectively improve classification performance.
Shenfu Zhang +4 more
doaj +1 more source
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2020 In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data.
Renlong Hang +5 more
openaire +6 more sources
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
Supercontinuum-based hyperspectral LiDAR for precision laser scanning
Optics Express, 2023 Hyperspectral LiDAR enables non-contact mapping of the 3D surface geometry of an object along with its spectral reflectance signature and has proved to be effective for automated point cloud segmentation in various remote sensing applications.
Pabitro Ray +3 more
openaire +3 more sources
Design and evaluation of multispectral LiDAR for the recovery of arboreal parameters [PDF]
, 2014 Multispectral light detection and ranging (LiDAR) has the potential to recover structural and physiological data from arboreal samples and, by extension, from forest canopies when deployed on aerial or space platforms.
Nichol, Caroline +12 more
core +1 more source