Title
Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification.
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.
Year
DOI
Venue
2016
10.3390/rs8030257
REMOTE SENSING
Keywords
Field
DocType
consumer-grade camera,pixel-based classification,object-based classification,RGB,near-infrared,crop identification
Computer vision,Feature (computer vision),Georeference,Remote sensing,Usability,Remote sensing application,RGB color model,Artificial intelligence,Contextual image classification,Geology
Journal
Volume
Issue
Citations 
8
3
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jian Zhang111.03
Chenghai Yang25411.63
Huaibo Song300.34
Wesley Clint Hoffmann471.60
Dongyan Zhang500.34
Guozhong Zhang611.03