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.
Chlorophyll content and distribution in leaf can reflect the plant health and nutrient status of the plant indirectly. It is
meaningful to monitor the 3D distribution of chlorophyll in plant science. It can be done by the method in this paper:
Firstly, the chlorophyll contents at different point in leaf are measured with the SPAD-502 chlorophyll meter, and the
RGN images composed by the channel R, G and NIR are captured with the imaging system. Secondly, the 3D model is
built from the RGN images and the RGN texture map containing all the information of R, G and NIR is generated.
Thirdly, the regression model between chlorophyll content and color characteristics is established. Finally, the 3D
distribution of chlorophyll in rice is captured by mapping the 2D distribution map of chlorophyll calculated by the
regression model to the 3D model. This methodology achieves the combination of phenotype and physiology, it can
calculated the 3D distribution of chlorophyll in rice well. The color characteristic g is good indicator of chlorophyll
content which can be used to measure the 3D distribution of chlorophyll quickly. Moreover, the methodology can be
used to high throughout analyze the rice.
Gastric cancer is one of the leading causes of cancer death in the world due to its high morbidity and mortality. Hyperspectral imaging (HSI) is an emerging, non-destructive, cutting edge analytical technology that combines conventional imaging and spectroscopy in one single system. The manuscript has investigated the application of near-infrared hyperspectral imaging (900-1700 nm) (NIR-HSI) for gastric cancer detection with algorithms. Major spectral differences were observed in three regions (950-1050, 1150-1250, and 1400-1500 nm). By inspecting cancerous mean spectrum three major absorption bands were observed around 975, 1215 and 1450 nm. Furthermore, the cancer target detection results are consistent and conformed with histopathological examination results. These results suggest that NIR-HSI is a simple, feasible and sensitive optical diagnostic technology for gastric cancer target detection with chemometrics.