Images captured from airborne imaging systems can be mosaicked for diverse remote sensing applications. The objective of this study was to identify appropriate mosaicking techniques and software to generate mosaicked images for use by aerial applicators and other users. Three software packages—Photoshop CC, Autostitch, and Pix4Dmapper—were selected for mosaicking airborne images acquired from a large cropping area. Ground control points were collected for georeferencing the mosaicked images and for evaluating the accuracy of eight mosaicking techniques. Analysis and accuracy assessment showed that Pix4Dmapper can be the first choice if georeferenced imagery with high accuracy is required. The spherical method in Photoshop CC can be an alternative for cost considerations, and Autostitch can be used to quickly mosaic images with reduced spatial resolution. The results also showed that the accuracy of image mosaicking techniques could be greatly affected by the size of the imaging area or the number of the images and that the accuracy would be higher for a small area than for a large area. The results from this study will provide useful information for the selection of image mosaicking software and techniques for aerial applicators and other users.
Cotton root rot is a destructive disease affecting cotton production. Accurate identification of infected areas within fields is useful for cost-effective control of the disease. The uncertainties caused by various infection stages and newly infected plants make it difficult to achieve accurate classification results from airborne imagery. The objectives of this study were to apply fuzzy set theory and nonlinear stretching enhancement to airborne multispectral imagery for unsupervised classification of cotton root rot infections. Four cotton fields near Edroy and San Angelo, Texas, were selected for this study. Airborne multispectral imagery was taken and the color-infrared (CIR) composite images were used for classification. The intensity component was enhanced by using a fuzzy-set based method, and the saturation component was enhanced by a nonlinear stretching image enhancement algorithm. The enhanced CIR composite images were then classified into infected and noninfected areas. Iterative self organization data analysis and adaptive Otsu’s method were used to compare the performance of the proposed image enhancement method. The results showed that image enhancement has improved the classification accuracy of these two unsupervised classification methods for all four fields. The results from this study will be useful for detection of cotton root rot and for site-specific treatment of the disease.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.