Impact of GCP distribution on the rectification accuracy of Landsat TM imagery in a coastal zone

The purpose is to explore the effect of the spatial distribution of ground control points （GCPs） on the accuracy of imagery rectification. Both area-distributed and linearly distributed GCPs were used to rectify a Landsat TM image of a coastal zone. Rectification accuracy was checked against 99 independent points over the intertidal mudflats with no ground control. Results indicate that the root-mean-square error of residuals over these areas is several times larger than its GCPs-measured counterpart. If the GCPs are spatially dispersed over an area, residuals fluctuate but increase steadily with distance to the source of control in easting （R^2= 0. 827）. in northing they fluctuate around 150 m until 15 km, beyond which they rise steadily at a small range of fluctuation. These residuals are less predictable from distance to the source of control than in easting （R^2= 0.517 ）. If the GCPs are distributed along a control line, residuals rise with distance to it linearly and predictably （R^2 = 0. 877） in the direction perpendicular to it. In a direction parallel to it, the distance has little impact on rectification residuals.

An Effective On-line Surface Particles Inspection Instrument for Large Aperture Optical Element

Surface particles growing in large aperture optical element （LAOE） have significant impact on LAOE＇s stable operation. It is a challenge for the online system to inspect the particles with long working distance, enough precision and high efficiency because of the system constraints. In this paper, an effective and portable inspection instrument is designed based on dark-field imaging principle. A Nikon lens and an industrial high definition （HD） camera are selected to construct the vision system to inspect particles of microns size spreading over hundreds of millimeters. Using two motors and other mechanical structure, the system can realize auto-focus and image rectification functions. The line light sources are installed on both sides of the LAOE in a sealed box while the vision system is portable and working outside the box. An adaptive binarization method is proposed to process the captured dark-field image. The distribution of particles on the LAOE＇s surface is investigated. Because of the high resolution of the captured image, the SSE2 instructions optimization method is used to reduce the time cost of the algorithm. Experiments show that the instrument can inspect LAOE effectively and accurately.

Corn ear test using SIFT-based panoramic photography and machine vision technology

Corn ear test is important to modern corn breeding.The test indexesmainly include lengths,radiuses,rows and numbers of corn ears and the kernels they bear,which can benefit the study on breeding new and fine corn varieties.These corn traits are often collected by traditional manual measurement,which is difficult to meet the needs of high throughput corn ear test.In this study,image sequences of corn ear samples were captured by building a panoramic photography collecting system.And then,to get the lengths and radiuses indexes,the corn area images were processed based on Lab color space and adaptive threshold segmentation.The sequence images were then matched and the panoramic image of a corn surface were extracted using Scale-invariant feature transform(SIFT).Finally,by using Exponential transformation(ETR)and Sobel-Hough algorithm,ears and rows indexes were acquired.Test results showed that the accuracy of the radiuses and lengths were 93.84%and 94.53%,respectively.Meanwhile,the accuracy of kernels and rows indexes were 98.12%and 96.14%,whichwere 4.03%and 7.25%higher than that of common mosaiced panoramic image.And the accuracy of kernel area and length-width ratio were 95.36%and 97.42%,respectively.All the results showed that the proposed method can be used for corn ear test effectively.