In the hyperspectral remote sensing, the continuum-removed method is used only with the spectrum of a single pixel to analyze spectrum and extract the feature bands useful with the classification. While in this paper, based on the continuum-removed algorithm, we programmed with Visual C++ to fulfill the functions of the continuum removed to the whole hyperspectral image, normalizing and extracting the feature space for the classification. At last, aiming at the former image and the after-continuum removed image, the classification results of the MLC and SAM are compared.
Usually the spectral unmixing and endmember extraction were based on the spectral statistics algorithm. In this paper, spatial knowledge, such as field patch information, was involved in the pure pixel selecting. In this way, endmember extraction was not only carried out in spectral space but also considering the spatial location of pixels. In addition, these known background information can also improve the accuracy of image classification, and also can be used to
intellectually separate pixels and evaluate each sub-pixels different attributes.
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.
An experimental study in monitoring the hot wastewater which is discharged into sea by the Futtsu Power Plant on the east coast of Tokyo Bay, Japan, was carried out in August-September, 2001, by using airborne hyperspectral remote sensing (HRS) sensor OMIS (Operational Modular Imaging Spectrometer). The fundamental progress of experiment, features of OMIS HRS image, data progressing and information extraction technologies, and a primary but successful result are introduced in detail. A new algorithm to extract the features and the infection extension of hot wastewater is developed and suggested in this paper. The algorithm adequately uses the whole spectral range of OMIS according to the general spectral responding characters of water. The water in the whole area is extracted by its spectral features in VNIR at first and then the polluted water is picked out from it by combine-using the MIR and TIR information. As a result, a temperature distribution map is successfully achieved in a test area and some other abnormal points are popped out and therefore some other pollution sources are discovered successfully in the whole scopes. The relatively good results in this paper show that hyperspectral remote sensing technology has a great prospect in detecting ocean and coastal environment both in qualitatively and quantitatively, at least for the hot wastewater. And an OMIS system with the algorithm suggested in this paper is operational for monitoring the infection features of hot wastewater.
Recent advances in remote sensing have led the way for the development of hyperspectral sensors and the applications of the hyperspectral data. Hyperspectral remote sensing is a relatively new technology, which is currently being investigated by researchers and scientists with regard to the detection and identification of minerals, terrestrial vegetation, and man-made materials and backgrounds. The airborne hyperspectral imaging data have operationally been used to a number of land-use, natural environment, geology, agriculture and other studies. In this study, airborne hyperspectral imaging data were tested in vegetation and man-made object identification. Natural grassland and artificial grassland, different types of crops, different types of forest and bush, different types of metal slabs in construction project have been precisely classified and greatly identified. In these works, the Operational Modular Imaging Spectrometer (OMIS) provides the imaging spectrometer data. OMIS has 128 spectral bands, including visible, short wave infrared, middle infrared and thermal infrared spectral region. Results suggest that hyperspectral imaging data, especially with short wave infrared and thermal infrared wavelength, have broad application perspectives in object identification.
In recent years, hyperspectral remote sensing has stepped into a new stage in China. There are some advanced hyperspectral imagers and CCD cameras developed by Chinese institutes and companies. Pushbroom Hyperspectral Imager (PHI) and Operative Modular Imaging Spectrometer (OMIS) have presented the level of airborne hyperspectral imagers in China, which have been developed by the Chinese Academy of Sciences. A narrow band hyperspectral digital camera system (HDCS) was developed and tested in 2000, the center of wavelength of which can be changed to fit different applications. There is also a kind of Fourier Imaging Spectrometer developed in China. Accordingly, Chinese scholars have created a number of models to meet different application problems. Some new models for hyperspectral remote sensing are provided. They are Hyperspectral Data Classification Model, POS Dat Geometric Correction Model, Derivative Spectral Model (DSM), Multi-temporal Index Image Cube Model (MIIC), Hybrid Decision Tree Model (HDT) and Correlation Simulating Analysis Model (CSAM). Some successful applications are provided and evaluated.
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.
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.
Some new vegetation models for hyperspectral remote sensing are provided in this paper. They are Derivative Spectral Model (DSM), Multi-temporal Index Image Cube Model (MIIC), Hybrid Decision Tree Model (HDT) and Correlation Simulating Analysis Model (CSAM). All models are developed and used to process the images acquired by Airborne Pushbroom Hyperspectral Imager (PHI) in Changzhou area, China, 1999. Some successful applications are provided and evaluated. The results show that DSM has the ability of eliminating the background interference of vegetation analysis. MIIC is a viable method for monitoring dynamic change of land cover and vegetation growth stages. HDT is effective in precise classification of rice land while CSAM provide a possibility and theoretical basis for crop identification, breed classification, and land information extraction especially for rice.
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