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Remote Sens. 2017, 9(6), 629; doi:10.3390/rs9060629

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

1
Department of Engineering Science, Sonoma State University, 1801 E Cotati Ave, Rohnert Park, CA 94928, USA
2
Center for Interdisciplinary Geospatial Analysis (CIGA), Department of Geography, Environment and Planning, Sonoma State University, 1801 E Cotati Ave, Rohnert Park, CA 94928, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez and Prasad S. Thenkabail
Received: 10 May 2017 / Revised: 12 June 2017 / Accepted: 14 June 2017 / Published: 20 June 2017
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [20385 KB, uploaded 20 June 2017]   |  

Abstract

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) and Support Vector Machine (SVM) classifiers were trained and tested with the same data. In order to support space-based hyperspectral applications, all analyses were performed with simulated Hyperspectral Infrared Imager (HyspIRI) imagery. Three-season data improved classifier overall accuracy by 2.0% (SVM), 1.9% (CNN) to 3.5% (RF) over single-season data. The three-season CNN provided an overall classification accuracy of 89.9%, which was comparable to overall accuracy of 89.5% for SVM. Both three-season CNN and SVM outperformed RF by over 7% overall accuracy. Analysis and visualization of the inner products for the CNN provided insight to distinctive features within the spectral-temporal domain. A method for CNN kernel tuning was presented to assess the importance of learned features. We concluded that CNN is a promising candidate for hyperspectral remote sensing applications because of the high classification accuracy and interpretability of its inner products. View Full-Text
Keywords: 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 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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Guidici, D.; Clark, M.L. One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California. Remote Sens. 2017, 9, 629.

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