One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF...

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Bibliographic Details
Main Authors: Daniel Guidici, Matthew L. Clark
Format: Article
Language:English
Published: MDPI AG 2017-06-01
Series:Remote Sensing
Subjects:
hyperspectral imagery
1-dimensional (1-D)
Convolutional Neural Network (CNN)
Support Vector Machine (SVM)
Random Forests (RF)
machine learning
deep learning
TensorFlow
multi-seasonal
regional land cover
Online Access:http://www.mdpi.com/2072-4292/9/6/629

Internet

http://www.mdpi.com/2072-4292/9/6/629

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