Zobrazeno 1 - 10
of 151
pro vyhledávání: '"Eli Saber"'
Publikováno v:
Sensors, Vol 24, Iss 18, p 6054 (2024)
Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the
Externí odkaz:
https://doaj.org/article/030beb2d1c694c33b5d5c74ca5f82594
Autor:
Manish Sharma, Mayur Dhanaraj, Srivallabha Karnam, Dimitris G. Chachlakis, Raymond Ptucha, Panos P. Markopoulos, Eli Saber
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 1497-1508 (2021)
Deep-learning object detection methods that are designed for computer vision applications tend to underperform when applied to remote sensing data. This is because contrary to computer vision, in remote sensing, training data are harder to collect an
Externí odkaz:
https://doaj.org/article/0f0551c073f049a3bdaaa03800c5a3e0
Publikováno v:
Remote Sensing, Vol 11, Iss 13, p 1614 (2019)
An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have be
Externí odkaz:
https://doaj.org/article/1fdf04b0f8d944bea5e220194592a69c
Publikováno v:
Remote Sensing, Vol 11, Iss 14, p 1648 (2019)
Hyperspectral (HS) sensors sample reflectance spectrum in very high resolution, which allows us to examine material properties in very fine details. However, their widespread adoption has been hindered because they are very expensive. Reflectance spe
Externí odkaz:
https://doaj.org/article/0fd0984bb77b4b56944bd8ba5b415129
Publikováno v:
Remote Sensing, Vol 10, Iss 9, p 1429 (2018)
In this paper, we present a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are
Externí odkaz:
https://doaj.org/article/ad77d05d827a4ec184947e6af8b67f52
Autor:
Eli Saber, Raymond Ptucha, Srivallabha Karnam, Mayur Dhanaraj, Panos P. Markopoulos, Dimitris G. Chachlakis, Manish Sharma
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 1497-1508 (2021)
Deep-learning object detection methods that are designed for computer vision applications tend to underperform when applied to remote sensing data. This is because contrary to computer vision, in remote sensing, training data are harder to collect an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a60ee4f7da6bc147ad240cf61073aac9
https://ieeexplore.ieee.org/document/9273212/
https://ieeexplore.ieee.org/document/9273212/
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bac4a732daa97b568ae369e384c4af7d
https://doi.org/10.1109/iccvw54120.2021.00027
https://doi.org/10.1109/iccvw54120.2021.00027
Publikováno v:
IGARSS
Detection CNN architectures often exhibit over-parameterization which results in excessive computational and storage overhead, but also undesired overfitting and reduced performance. In this work we focus on YOLOrs, a state-of-the-art CNN for target
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::48ecb8012d342d048d94a5a5ece625e4
https://doi.org/10.1109/igarss47720.2021.9554418
https://doi.org/10.1109/igarss47720.2021.9554418
Publikováno v:
Computational Imaging
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f554bdcd99d3ac9ef53154bfc5645ef
https://doi.org/10.2352/issn.2470-1173.2016.19.coimg-162
https://doi.org/10.2352/issn.2470-1173.2016.19.coimg-162
Publikováno v:
Journal of Electronic Imaging. 29:1
We propose a three-dimensional video segmentation method using deep learning convolutional neural nets. The algorithm utilizes the local gradient computed at each pixel location together with the global boundary map acquired through deep learning met
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dff08a52d2876b784b3cf907a176ec09
https://doi.org/10.1117/1.jei.29.1.013019
https://doi.org/10.1117/1.jei.29.1.013019