In the using of remote sensing technology for agriculture investigation, it is often required to extract plant and vegetation distribution from land covers, especially when plants are sparsely dispersed. Otherwise it would consume considerable expense to perform ground survey. Due to its narrow spectral bandwidth and high spectral resolution, hyperspectral remote sensing technique is regarded as a suitable technique for such task. Conventional classification techniques often suffer from unrealistic mathematical distribution assumption, and are also subject to the lack of prerequisite information. Trying to address the problem of unsurprised plant classification, this paper proposes an Independent Component Analysis (ICA) based feature extraction algorithm. ICA is a technique that stems out from the Blind Source Separation. In hyperspectral data processing, ICA projects the vector to the space where the items of the vector are mutually statistically independent, and therefore is capable of extracting various kinds of information from imagery. Besides, so as to strengthen the contrast of result independent component, a mathematical morphology based post-processing procedure is appended after ICA decomposition. Through real hyperspectral data experiments, our algorithm has demonstrated better performance in classification. Computation efficiency and noise robustness have also been improved by an extra noise filtering effort.
In order to remove MODIS bowtie effect, an analytical algorithm is proposed, which is based on solid geometry projection and requires no ephemeris information. The geometry projection model is established from the parameters of MODIS platform and the amount of overlapping pixels is quantified as a function of the instantaneous scanning angle. Lookup table is utilized to guide the deletion of overlapping pixels and improve efficiency, and cubic spline interpolation is applied to subpixelly restore data following their profile. Resampling is followed to generate integral pixel coordinates. The border incontinuity problem that occurs due to the gap between different swaths is solved by introducing of a special blocking method. The validity of out algorithm is verified by comparing with three other Non-ephemeris algorithms, and the result shows that not only the bowtie effect within a single swath is effectively removed, the incontinuity caused by conventional pixel grouping method is mostly well eliminated.
A novel vision measurement system specially designed for on-line measurement of thickness of micrograph of work-piece is presented. This system consists of CCD camera, telecentric microscopic lens, illumination, image sampling and processing unit etc. The telecentric microscopic lens and relating LED illumination system is carefully designed to fit the environment on-line such as: illumination, vibration, noise light and out-of focus so as to reduce the measurement error. The following image processing techniques are studied. (1) a new image preprocessing method based on gray level, gradient and spatial information is developed to get rid of the noise in image. (2) several image segmentation methods and the results are analyzed and the evaluation system of image segmentation and the relating evaluation standards are discussed to satisfy the requirement of on-line automatic image segmentation. Finally, an iterative least-square method is utilized to calculate the thickness of the micrograph in order to improve the measurement precision further. The results show that the precision of the vision measurement system is sub-pixel, which can fully satisfy the requirement of industry.