Classification of multispectral satellite image data using improved NRBF neural networks

2003 ◽  
Author(s):  
Xiaoli Tao ◽  
Howard E. Michel
Keyword(s):  
Neural Networks ◽  
Satellite Image ◽  
Image Data ◽  
Satellite Image Data ◽  
Multispectral Satellite Image

scholarly journals Level II Vegetation Classification of Satellite Image Data using PCA Method

IJARCCE ◽  
2017 ◽  
Vol 6 (4) ◽  
pp. 297-301
Author(s):  
Mayur Kolhe ◽  
Yogesh Kadam ◽  
Ninad Chousalkar ◽  
Sunny Chordiya
Keyword(s):  
Satellite Image ◽  
Image Data ◽  
Vegetation Classification ◽  
Level Ii ◽  
Pca Method ◽  
Satellite Image Data

scholarly journals Symmetry perception with spiking neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonathan K. George ◽  
Cesare Soci ◽  
Mario Miscuglio ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Mirror Symmetry ◽  
Satellite Image ◽  
Image Data ◽  
Coincidence Detection ◽  
Spiking Neural Networks ◽  
Feed Forward Neural Network ◽  
Neuromorphic Computing ◽  
Symmetry Perception ◽  
High Level

AbstractMirror symmetry is an abundant feature in both nature and technology. Its successful detection is critical for perception procedures based on visual stimuli and requires organizational processes. Neuromorphic computing, utilizing brain-mimicked networks, could be a technology-solution providing such perceptual organization functionality, and furthermore has made tremendous advances in computing efficiency by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation. The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. More importantly it enables a technology pathway in bridging the gap between the low-level incoming sensor stimuli and high-level interpretation of these inputs as recognized objects and scenes in the world.

scholarly journals Artificial Neural Networks for Satellite Image Classification of Shoreline Extraction for Land and Water Classes of the North West Coast of Peninsular Malaysia

2018 ◽  
Vol 24 (2) ◽  
pp. 1382-1387 ◽  
Author(s):  
Syaifulnizam Abd Manaf ◽  
Norwati Mustapha ◽  
Md. Nasir Sulaiman ◽  
Nor Azura Husin ◽  
Mohd Radzi Abdul Hamid
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Image Classification ◽  
West Coast ◽  
Satellite Image ◽  
Peninsular Malaysia ◽  
North West ◽  
The North ◽  
Artificial Neural

scholarly journals Verification of multispectral data processing for the Sentinel-2A bands, field ASD FieldSpec® 3FR and UAV with the DJI STS-VIS

2019 ◽  
pp. 29-39 ◽  
Author(s):  
Stanislav Dugin ◽  
Oksana Sybirtseva ◽  
Stanislav Golubov ◽  
Yelizaveta Dorofey
Keyword(s):  
Pearson Correlation ◽  
Plant Cover ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Image Data ◽  
Multispectral Data ◽  
Maximum Value ◽  
Substantial Correlation ◽  
Satellite Image Data ◽  
Sentinel 2A

The study of plant cover have been performed by the hyperspectral remote sensing method using ASD FieldSpec® 3FR and DJI STS-VIS measurements. The orthophotoplans are compiled for the test plots of interest at the spatial resolution of 2.5 cm. The substantial correlation for the results of terrestrial verification for the satellite image data in the range of Sentinel-2A bands are confirmed. 15 vegetation indices for the Sentinel-2А wavelength bands were drawn at the Pearson correlation coefficient r > 0.97, with a maximum value of the correlation error of 0.07.

Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets

2018 ◽  
Vol 15 (9) ◽  
pp. 1451-1455 ◽  
Author(s):  
Grant J. Scott ◽  
Kyle C. Hagan ◽  
Richard A. Marcum ◽  
James Alex Hurt ◽  
Derek T. Anderson ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Deep Neural Networks ◽  
Image Data ◽  
Data Sets ◽  
Resolution Image ◽  
High Resolution Image
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