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Sensors 2017, 17(10), 2421;

A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features

School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Highly Automated Driving Team, HERE Technologies Automotive Division, Chicago, IL 60606, USA
Authors to whom correspondence should be addressed.
Received: 8 August 2017 / Revised: 7 October 2017 / Accepted: 13 October 2017 / Published: 23 October 2017
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
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During recent years, convolutional neural network (CNN)-based methods have been widely applied to hyperspectral image (HSI) classification by mostly mining the spectral variabilities. However, the spatial consistency in HSI is rarely discussed except as an extra convolutional channel. Very recently, the development of pixel pair features (PPF) for HSI classification offers a new way of incorporating spatial information. In this paper, we first propose an improved PPF-style feature, the spatial pixel pair feature (SPPF), that better exploits both the spatial/contextual information and spectral information. On top of the new SPPF, we further propose a flexible multi-stream CNN-based classification framework that is compatible with multiple in-stream sub-network designs. The proposed SPPF is different from the original PPF in its paring pixel selection strategy: only pixels immediately adjacent to the central one are eligible, therefore imposing stronger spatial regularization. Additionally, with off-the-shelf classification sub-network designs, the proposed multi-stream, late-fusion CNN-based framework outperforms competing ones without requiring extensive network configuration tuning. Experimental results on three publicly available datasets demonstrate the performance of the proposed SPPF-based HSI classification framework. View Full-Text
Keywords: hyperspectral image classification; convolutional neural networks; spatial pixel pair features hyperspectral image classification; convolutional neural networks; spatial pixel pair features

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Ran, L.; Zhang, Y.; Wei, W.; Zhang, Q. A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features. Sensors 2017, 17, 2421.

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