Next Article in Journal
Stable and Fast-Response Capacitive Humidity Sensors Based on a ZnO Nanopowder/PVP-RGO Multilayer
Next Article in Special Issue
Dimension Reduction Aided Hyperspectral Image Classification with a Small-sized Training Dataset: Experimental Comparisons
Previous Article in Journal
Development of a Telemetric, Miniaturized Electrochemical Amperometric Analyzer
Previous Article in Special Issue
A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery
Open AccessArticle

A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features

1
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
2
Highly Automated Driving Team, HERE Technologies Automotive Division, Chicago, IL 60606, USA
*
Authors to whom correspondence should be addressed.
Sensors 2017, 17(10), 2421; https://doi.org/10.3390/s17102421
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)
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
Show Figures

Figure 1

MDPI and ACS Style

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

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop