Results 131 to 140 of about 2,046 (182)

Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision. [PDF]

open access: yesJ Imaging
Gálvez-Salido A   +5 more
europepmc   +1 more source

Improving Brain Tumor Detection by Cortical Surface and Vessels Segmentation Through RGB-to-HSI Transfer Learning. [PDF]

open access: yesCancers (Basel)
Vazquez G   +5 more
europepmc   +1 more source

Hyperspectral Lidar: A Progress Report

Optics and Photonics News, 2021
Laser-scanning instruments that allow hyperspectral and spatial data to be collected in a single shot could improve remote sensing in a wide range of applications.
Sanna Kaasalainen, Tuomo Malkamäki
openaire   +1 more source

Fusion of Multispectral LiDAR and Hyperspectral Imagery

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
This paper presents a technique for the fusion of multispectral LiDAR and hyperspectral data. The proposed method is based on the fusion of the features of multispectral LiDAR and hyperspectral data projected in two different subspaces. First, the spatial features are extracted from both data using morphological filters.
Rasti, B., Ghamisi, P., Gloaguen, R.
openaire   +2 more sources

Probability Fusion for Hyperspectral and LiDAR Data

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
In this paper, a new probability fusion strategy is proposed for hyperspectral and LiDAR data classification, which is inspired by the representation residual fusion strategy in our previous work. Unlike the residual fusion strategy utilizes a collaborative representation classifier, the probability fusion strategy deploys a deep residual network (DRN).
Chiru Ge, Qian Du 0001
openaire   +1 more source

Hyperspectral and lidar data integration and classification

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
Light Detection and Ranging (LiDAR) is a technology used in different topic (mapping, urban land cover, agriculture, forestry, etc.). The great potential of LiDAR data lies in its high accuracy in the measurement of heights. Hyperspectral images, which comprise hundreds of (nearly contiguous) spectral channels, can also have spatial resolution of up to
Maria Angeles Garcia-Sopo   +3 more
openaire   +2 more sources

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