A set of near infrared high resolution spectral imaging system is set up, the infrared absorption properties of raw cotton and colorless foreign are analyzed through the system, and scheme of polypropylene fiber detection based on the near infrared spectral image is proposed; On this basis, reduce dimensions the spectral images through the principal component analysis, further improve the efficiency of colorless foreign detection. The experimental results show that the spectral images after reducing dimensions can be used to detect colorless or light color raw cotton fiber effectively.
The aim of this paper is to pave the way for the establishment of analysis of the lights
reflected from the leaf's surface as an analytical method of plant disease. An imaging LCTF
spectrometer that covers a visible light with 400-720 nm wavelength bands has been developed.
This paper first outlines the structure of imaging LCTF spectrometer, including their operational
principles and construction. Next, various spectral images acquired using the LCTF spectrometer in laboratory environment experiments to measure spectral characteristics of rays reflected from cucumber leaves surfaces that are infected by different germs are analyzed. Then, the results of the experiments conducted using the imaging spectrometer are shown, including the analyzed relative radiance of rays reflected from the plants, and spectral images acquired at various wavelengths. These experimental results demonstrate clearly that rays reflected from plant contaminated by different disease germs have different spectral properties.
An eight-channel imaging spectrometer based on narrowband multi-spectral imaging technology is presented. After
acquiring eight images in real time, the spectrometer is used to process images, and finally the color image of object is
compounded. Focus is on the methods of image registration and spectral construction. The experiment indicates that
point mapping and cubic spline interpolation are effective, and the color composition image is close to the real one. The
system has the advantages of high spatial resolution, strong real-time character, hence it can be widely used in the field
of moving target recognition.
Using conventional camera to capture natural scenes with high dynamic range generally results in saturation as well as
underexposure, because of their limited dynamic range. And moreover, the image of conventional RGB camera with
RGB color filter lacks color accuracy. We present a promising solution - a high dynamic range multispectral camera
placing a Liquid Crystal Tunable Filter (LCTF) between lens and gray level imaging sensor. For each bands, gray level
images with different exposures are acquired separately and are combined into a multispectral high dynamic range image
afterwards. The high dynamic range multispectral image has higher color accuracy and greater dynamic range than the
images of the traditional RGB camera.
We present our latest research development of the all-reflective Fourier transform imaging spectrometer
based on the principle of wavefront-splitting interference. The optical configuration of this system
includes a set of Fresnel's double mirrors and a number of other reflective telescopes or mirrors. The
major advantages of this system includs higher optical throughput, larger spectral bandwidth, and less
chromatic aberration as compared with conventinal
chromatic-despersion imaging spectrometer . In this
paper ,the optical principle and the prototype device of our system are introduced, and the latest
experimental results from our prototype device are presented.
This paper is to introduce Common hyperspectral image database with a demand-oriented Database design method
(CHIDB), which comprehensively set ground-based spectra, standardized hyperspectral cube, spectral analysis together
to meet some applications. The paper presents an integrated approach to retrieving spectral and spatial patterns from
remotely sensed imagery using state-of-the-art data mining and advanced database technologies, some data mining ideas
and functions were associated into CHIDB to make it more suitable to serve in agriculture, geological and environmental
areas. A broad range of data from multiple regions of the electromagnetic spectrum is supported, including ultraviolet,
visible, near-infrared, thermal infrared, and fluorescence. CHIDB is based on dotnet framework and designed by MVC
architecture including five main functional modules: Data importer/exporter, Image/spectrum Viewer, Data Processor,
Parameter Extractor, and On-line Analyzer. The original data were all stored in SQL server2008 for efficient search,
query and update, and some advance Spectral image data Processing technology are used such as Parallel processing in
C#; Finally an application case is presented in agricultural disease detecting area.
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