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Remote Sens. 2016, 8(3), 257; doi:10.3390/rs8030257

Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification

1
College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China
2
USDA-Agricultural Research Service, Aerial Application Technology Research Unit, 3103 F & B Road, College Station, TX 77845, USA
3
College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, China
4
Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, 111 Jiulong Road, Hefei 230601, China
5
College of Engineering, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Academic Editors: Mutlu Ozdogan, Clement Atzberger and Prasad S. Thenkabail
Received: 21 January 2016 / Revised: 7 March 2016 / Accepted: 11 March 2016 / Published: 18 March 2016
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
View Full-Text   |   Download PDF [5330 KB, uploaded 21 March 2016]   |  

Abstract

Remote sensing systems based on consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. However, the performance of consumer-grade cameras for practical applications has not been well documented in related studies. The objective of this research was to apply three commonly-used classification methods (unsupervised, supervised, and object-based) to three-band imagery with RGB (red, green, and blue bands) and four-band imagery with RGB and near-infrared (NIR) bands to evaluate the performance of a dual-camera imaging system for crop identification. Airborne images were acquired from a cropping area in Texas and mosaicked and georeferenced. The mosaicked imagery was classified using the three classification methods to assess the usefulness of NIR imagery for crop identification and to evaluate performance differences between the object-based and pixel-based methods. Image classification and accuracy assessment showed that the additional NIR band imagery improved crop classification accuracy over the RGB imagery and that the object-based method achieved better results with additional non-spectral image features. The results from this study indicate that the airborne imaging system based on two consumer-grade cameras used in this study can be useful for crop identification and other agricultural applications. View Full-Text
Keywords: consumer-grade camera; crop identification; RGB; near-infrared; pixel-based classification; object-based classification consumer-grade camera; crop identification; RGB; near-infrared; pixel-based classification; object-based classification
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Zhang, J.; Yang, C.; Song, H.; Hoffmann, W.C.; Zhang, D.; Zhang, G. Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification. Remote Sens. 2016, 8, 257.

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