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.
Highlights
Remote sensing has played a key role in precision agriculture and other agricultural applications [1]
The additional NIR band and the object-based method both could improve the performance of image classification for crop identification
This study addressed important and practical issues related to the use of consumer-grade
Summary
Remote sensing has played a key role in precision agriculture and other agricultural applications [1]. It provides a very efficient and convenient way to capture and analyze agricultural information. Numerous commercial satellite and custom-built airborne imaging systems have been developed for remote sensing applications with agriculture being. Remote sensing is very mysterious for most people who usually perceive it as complex designs and sophisticated sensors on satellites and other platforms. This is true for quite a long time, especially for the scientific-grade remote sensing systems on satellites and manned aircraft. More and more imaging systems based on consumer-grade cameras have been designed as remote sensing platforms [3,4,5,6]
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