A Quantitative Validation of Multi-Modal Image Fusion and Segmentation for Object Detection and Tracking

A Quantitative Validation of Multi-Modal Image Fusion and Segmentation for Object Detection and Tracking

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitativel...

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Bibliographic Details
Main Authors: Nicholas LaHaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Remote Sensing
Subjects:
big data applications
clustering
computer vision
restricted Boltzmann machines (RBMs)
unsupervised machine learning
image segmentation
Online Access:https://www.mdpi.com/2072-4292/13/12/2364

Internet

https://www.mdpi.com/2072-4292/13/12/2364

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