Band selection work is mainly focused on various kinds of vegetables. Two kinds of data are used in this work project. One is the spectral data measured with ASD spectrometer; the other is airborne Push-broom Hyperspectral Imager (PHI) data. The band selection work consists of three parts, bandwidth selection, wavelength range selection, and center wavelength selection. Bandwidths of filters should be in the range 25nm to 50nm because of the angle effect of the bandpass interference filters. Two factors, light source characteristics and the CCD spectral responsivity, confine the filter center wavelength range in the range from 410nm to 810nm.. Methodology used in the center wavelength selection work is spectral correlation-based approaches, maximum relative technique and the linear forward stepwise regression technique. Those two kinds of method have almost the same result. And they are relatively well distributed over the whole spectral domain.
KEYWORDS: Cameras, Imaging systems, Image restoration, Digital cameras, Control systems, Digital imaging, Multispectral imaging, Global Positioning System, Optical filters, Agriculture
For rapid and steady collection of high spectral resolution airborne data, a narrow band multispectral digital camera system (MDCS) was developed and tested in the year 2000. The MDCS was built based on three 1024x1024 pixels, 12bits digitalized area CCD cameras, whose FOV and IFOV are about 20 degree and 0.34 mrad respectively. Precise exposure control and synchronic trigger control are provided in this system, and the problem of collection and recording of large digital image data has been well solved. The center wavelength and bandwidth of the bandpass optical filters in this system can be customized to fit different application. The filter bandwidth can be changed from 10 to 25nm, and the filter center wavelength can be changed from 400nm to 900nm. The 10nm bandpass filters centered at 555, 650, 725nm and 650, 725, 825nm were used for agriculture research in the test phase. High spatial-resolution multispectral images were acquired on December 5, 2000 with the MDCS. At an altitude of approximately 3500 meters, the spatial resolution was 1.2 meter. Image processing was made for improvement of the image quality. The image restoration of motion-blurred image is discussed in the paper.
Crop physiology analysis and growth monitoring are important elements for precision agriculture management. Remote sensing technology supplies us more selections and available spaces in this dynamic change study by producing images of different spatial, spectral and temporal resolutions. Especially, the remote sensing data of high spectral and high temporal resolution will play a key role in land cover studies at national, regional and global scales. In this paper, Multi-temporal Index Image Cube (MIIC) is proposed, which is an effective data structure for the parameterization of multi-dimensions spectral curve. MIIC is very useful for supporting the dynamic analysis on vegetation phenological and physiological characters. Based on multi-temporal meteorological satellite data and multi-temporal ground spectral measurement data, the temporal characters of different vegetation physiological parameters are contrasted and analyzed from temporal index image cube. In addition, MIIC also has very wide use in hyperspectral remote sensing applications.
According to the advanced feature of hyperspectral image and Correlation Simulating Analysis Model (CSAM), a new simple but efficient kernel-adaptive filter (SRSSHF) especially for hyperspectral image is suggested in this paper. It is achieved not based on the traditional sigma (standard deviation) statistics in spatial dimension, but on the valid-pixel judge in spectral dimension and the intellectualized shift convolution in spatial dimensions. So its criteria is based on the intrinsic property of objects by adequately utilizing the spectral information that hyperspectral affords. Such a filter also is an adaptive filter, and its kernel size theoretically has no strong influence on the filter results. What it concentrates is the feature of signal itself but not the speckle noise, its criterion is in spectral dimension, and multiple iteration is available. So the tradeoff of spatial texture is not necessary. It is applied to filter and improve quality of PHI hyperspectral images acquired both in Changzhou, China and Nagano, Japan, and a >200 looks iteration and a comparison with other typical adaptive filters also are tried. It shows that SRSSHF can smooth whole the internal of a homogeneous area while ideally keep and, as well as, enhance the edges well. As good results are achieved, this paper suggests that SRSSHF on the base of CSAM is a relative ideal filter for HRS images. Some other features of SRSSHF are also discussed in this paper.
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