95 results about "Spectral distortion" patented technology

In phased array antennas, multiple signals that perform different functions, such as radar, electronic warfare (EW) and telecommunications, can each be simultaneously transmitted only through a different sub-aperture of the array. For maximum power and efficiency, the power amplifiers operate on one signal at a time. This patent provides the technique of forming a common waveform from multiple signals for transmission through a common aperture. To practically implement this technique in wideband operations, processes performing the required waveform-shaping and amplitude-to-phase-modulation are devised that can transmit the high-power diverse waveforms without serious intermodulations and spectral distortion through every element of the array.

The invention discloses a novel image fusion algorithm based on combination of IHS (intensity-hue-saturation) transform and wavelet transform for effectively fusing a low-resolution multispectral image and a high-resolution full-color image. The method comprises the following steps: carrying out wavelet decomposition on a strength (I) component image and a high-resolution full-color image of a multispectral image; carrying out fusion with different standards according to respectively high and low frequency features; carrying out wavelet inverse transformation on the new formed high and low frequencies to obtain a new I component image; and finally carrying out IHS inverse transformation to obtain a final fusion image. A test result shows that the method is used for not only reducing the spectral distortion value, but also effectively increasing the spatial resolution.

The invention discloses an adaptive remote sensing image panchromatic sharpening method, and belongs to the technical field of remote sensing image fusion. The spectral distortion of fusion results is reduced, and the sharpening effect of fusion results is improved. The method comprises the following steps: performing interpolation amplification on a low-resolution multispectral image to make the multispectral image and a corresponding panchromatic image have the same resolution; filtering the panchromatic image and the multispectral image of the same resolution to obtain low-frequency components; performing difference operation on the panchromatic image and the multispectral image of the same resolution and the low-frequency components to obtain corresponding high-frequency components; estimating missing initial detail images of the multispectral image on the basis of the high-frequency components of the two images; building an optimization function for the initial detail images, and optimizing the initial detail images in a rapid descent method to obtain final detail images suitable for different channels of the multispectral image; and injecting the final detail images into the corresponding channels of the multispectral image to obtain a high-resolution multispectral image. The adaptive remote sensing image panchromatic sharpening method of the invention is used for remote sensing image panchromatic sharpening.

The invention discloses a panchromatic sharpening method of remote sensing image based on convolution neural network, includes the following steps: reading the original multispectral remote sensing image and its matching panchromatic remote sensing image; preprocessing the image data to obtain training sample; constructing convolution neural network structure; the training samples being inputted into the convolution neural network, and the loss function being stabilized at the minimum value by using the stochastic gradient descent algorithm, thus the optimal solution of the network structure is obtained. The same preprocessed test samples are input into the optimal convolution neural network structure, and the output is processed to obtain high-resolution multi-spectral remote sensing images. The invention can effectively reduce the spectral distortion of the processing result and enhance the sharpening effect.

The provided method for high-resolution multispectral image comprises: to the original low-resolution image from satellite and the space-matched high-resolution full-color image, composing the simulated low-resolution full-color image by opposite wave band statistic relation; then, determining details to add by ratio on the original image and generate target image. This invention can eliminate spectral distortion efficiently.

A method includes determining calibration factors for calibrating photon-counting detectors of a spectral imaging system by combining a heuristic calibration of the photon-counting detectors and an analytical calibration of the photon-counting detectors and generating a set of photon-counting calibration factors based on the combining of the a heuristic calibration and the analytical calibration. The photon-counting calibration factors, when applied to measured energy-resolved data from the photon-counting detectors of a spectral CT scan of a subject or object, mitigate spectral distortion caused by a radiation intensity profile shaper that filters a radiation beam of the spectral CT scan.

A method and system for quantizing LSF vectors in a speech coder, wherein predicted LSF values based on previously decoded output values are used to estimate spectral distortion, along with the residual codebook vectors and the LSF coefficients. The method comprises the steps of obtaining a plurality of quantized LSF coefficients from the respective predicted LSF values and the residual codebook vectors; rearranging the quantized LSF coefficients in the frequency domain in an orderly fashion; obtaining the spectral distortion from the rearranged quantized LSF coefficients and the respective LSF coefficients; and an optimal code vector is selected based on the spectral distortion.

The invention discloses a hyperspectral image sharpening method based on a spectral prediction residual convolutional neural network. The method comprises the steps of reading an original hyperspectral image; Synthesizing a corresponding panchromatic image by utilizing the visible light wave band of the hyperspectral image; Preprocessing the image data to obtain a training sample pair; Constructing a spectral prediction residual convolutional neural network structure; Inputting the training sample pair into a spectrum prediction residual convolutional neural network, and reducing a training error to a minimum value by using an adaptive moment estimation algorithm, thereby obtaining an optimal network structure parameter; And inputting the same preprocessed test sample pair into the optimalspectral prediction residual convolutional neural network structure, and outputting a high-resolution hyperspectral image. The invention can effectively alleviate the phenomenon of spectrum distortion and enhance the sharpening effect.

The invention discloses a remote sensing multi-spectral and panchromatic image fusion method based on mixed sub-pixel un-mixing, which comprises the steps of sampling the resolution of an original panchromatic image PAN from an original low-resolution multi-spectral image MS0 by adopting a cubic mode, and acquiring an MS image; calculating a normalized differential vegetation index NDVI based on the MS image; classifying sub-pixels in the MS image by using the original panchromatic image PAN and the normalized differential vegetation index NDVI, and acquiring a vegetation-non-vegetation mixed sub-pixel CM and other sub-pixels CO; judging the surface feature type of the vegetation-non-vegetation sub-pixel by using the original panchromatic image PAN and the normalized differential vegetation index NDVI; and carrying out fusion according to the corresponding sub-pixel type and the corresponding surface feature type as for each sub-pixel t n the MS image. The beneficial effects are that spectral un-mixing processing is carried out on the mixed sub-pixels in the multi-spectral image in the fusion process, so that the number of mixed pixels in a fusion image is reduced, the boundary of the fusion image is sharpened, and the spectral distortion is reduced.

The invention belongs to the field of image fusion technology, in particular to a multi-spectral image and panchromatic image fusion method based on residual hypercomplex symplectic decomposition. The method comprises the following steps of : respectively constructing models of residual images of a multi-spectral image and a panchromatic image by using hypercomplex, and respectively subjecting the hypercomplex residual models of the multi-spectral image to hypercomplex symplectic decomposition along the grayscale axis direction to obtain a simplex portion containing brightness information and a perplex portion including chromaticity information. The analysis shows that the residual of the multi-spectral image with high resolution can be recovered by substituting the simplex portion obtained by decomposing the low-resolution multi-spectral image with hypercomplex residual image of a high-resolution panchromatic image, and the fusion of the multi-spectral image and the panchromatic image can be achieved by synthesizing symplectic decomposition. The simulation result shows that the invention is free of visible spectral distortion. The evaluation result of various prior image fusion methods shows that the invention is superior to HIS, PCA and wavelet transformation fusion method.

The invention provides a hyperspectral image panchromatic sharpening method based on a spectral dimension control convolutional neural network. The hyperspectral image panchromatic sharpening method comprises the following steps: reading original hyperspectral image data; synthesizing corresponding panchromatic image data by using the hyperspectral image; preprocessing the image data to obtain a training sample; constructing a spectral dimension control convolutional neural network structure; inputting the training sample into a spectral dimension control convolutional neural network, and stabilizing a loss function at a minimum value by using an adaptive moment estimation algorithm, thereby obtaining an optimal network structure parameter; inputting the test samples subjected to the samepreprocessing into an optimal spectral dimension control convolutional neural network structure, and outputting and processing to obtain a high-resolution hyperspectral image. According to the method,the characteristics of the hyperspectral image are combined, the missing detail information of the hyperspectral image can be better restored by controlling the spectral dimension of the hyperspectral image, and the spectral distortion phenomenon of a sharpening result is reduced.

The invention discloses an imaging spectrometer beam splitting system based on a free-form surface. The system is composed of an entrance slit, a free-form surface collimation reflector, a plane diffraction grating, a free-form surface imaging reflector and an image surface detector. The imaging spectrometer beam splitting system is characterized in that the high-freedom-degree non-rotation-symmetrical free-form surface is used in cooperation with the plane grating which is high in performance and mature in technology, and the beam splitting system which is simple and compact in structure, low in aberration, low in distortion and high in efficiency is realized. In the system, the mode that the hole diameter deviates from the axis is adopted to achieve the purpose of no light obstruction, relative angle rotation does not exist among all optical elements, installation and adjustment can be realized conveniently and easily, and the development time can be shortened. The beam splitting system based on the free-form surface realizes good spectral imaging at the 8-12.5-micrometer long wave infrared spectrum, and on the premise that it is guaranteed that the system is simple and compact, spherical aberration, comatic aberration, astigmatism, spectral distortion, spectral line bending and the like are well corrected. The imaging spectrometer beam splitting system can be applied to the field of aerospace and aviation hyperspectral imaging.

The invention discloses a fusion method and device for a full-color image and a multi-spectral image. The method comprises: a multi-spectral image and a full-color image have same pixel elements and the processed multi-spectral image is transformed to form one luminance component and n-1 angle components; gray stretching processing is carried out on the full-color image; the luminance component and the full-color image after gray stretching are fused to obtain a new component; and inverse transformation is carried out on the new component and the n-1 angle components to form a fused image. In addition, the device is composed of a processing module, a transformation module, a stretching module, a fusion module and an inverse transform module. According to the fusion method and device, the multi-spectral image has a high spatial resolution and spectral information of the original multi-spectral image is kept well, so that a spectral distortion problem in the traditional fusion method can be solved. Moreover, the fused image obtained by the fusion method has advantages of high overall contrast ratio, clear texture, and enriched edge information.

The invention provides a remote sensing panchromatic and multispectral image distributed fusion method based on a residual network, which mainly solves the problems of spectrum distortion, low spatial resolution and low fusion quality in the prior art, and comprises the following steps: collecting an original image of a target area through a satellite, and preprocessing the original image; constructing a simulation training set and a test set by using the preprocessed panchromatic image and the preprocessed multispectral image according to a Wald criterion, constructing a residual network-based distributed fusion model consisting of three branches, and fully training a network by taking the panchromatic image and the multispectral image of the training set as input of the network; and inputting panchromatic and multispectral images to be fused into the trained fusion network to obtain a fused image. According to the method, the features of different scales of different branches are used for fusion, more spectral information and spatial information are reserved, the method has better performance in the aspects of improving the spatial resolution and reserving the spectral information, and the fusion quality is improved.

The invention provides a hyperspectral image super-resolution method based on a spectral constraint adversarial network. The problems that a hyperspectral image generated in the prior art is serious in spectral distortion, depends on prior information and is high in operation complexity are solved. The method comprises the following steps: constructing a generator network; constructing a decisiondevice network; constructing a spectral constraint adversarial network; initializing a spectral constraint adversarial network; generating a training set; training a spectral constraint adversarial network; and performing super-resolution on the hyperspectral image. According to the method, the spectral constraint adversarial network is utilized, high-resolution multispectral images in the same scene do not need to be used as prior information for image fusion, the spatial resolution of the hyperspectral images can be effectively improved, and meanwhile spectral distortion of the hyperspectralimages after super-resolution is reduced.

The invention provides a remote sensing image space spectrum fusion method of a deep recursive residual network, and electronic equipment. The method comprises the following steps: (a) processing a plurality of original remote sensing images to generate a data set; (b) constructing a deep recursive residual network model; (c) training the deep recursive residual network model obtained in the step(b) by utilizing the data set obtained in the step (a) to obtain a trained deep recursive residual network model; (d) fusing remote sensing images to be fused by using the trained deep recursive residual network model to obtain a fused image. According to the method, the advantages of the residual network and the recursive network can be utilized, rich image features of the deep-level network arelearned through end-to-end network design, the spectral information of the original low-resolution multispectral image is reserved as much as possible while the spatial resolution is improved, and thespectral distortion phenomenon existing in a traditional method is well improved.

The invention discloses a remote sensing image fusion method based on a directionlet domain hidden Markov tree (HMT) model. The remote sensing image fusion method mainly solves the problems of spectral distortion and lacking of wavelet transform method direction information, wherein the problems exists in an HIS method. The remote sensing image fusion method comprises the following steps: (1) conducting HIS transformation to multispectral images, and obtaining chrominance (H), luminance (I) and saturation (S); (2) enabling full-color images and the component I to conduct directionlet transformation; (3) respectively establishing HMT model for high-frequency coefficients, training to obtain parameter set estimated values, and calculating posterior probability; (4) selecting the optimized high-frequency coefficient according to the posterior probability; (5) fusing low-frequency coefficients by adopting fusion rules of a genetic algorithm; and (6) conducting directionlet inverse transformation to the fused coefficients to obtain a new component I, then conducting HIS inverse transformation by combining components of H and S, and obtaining a final fused image. According to the remote sensing image fusion method based on the directionlet domain HMT model, anisotropic characteristics of source images can be fully abstracted, pertinence in data is fully unearthed, detailed information of the fused image is improved, and meanwhile the spectral characteristics of the multispectral images can be better remained unchanged.

The invention discloses an imaging spectrometer optical splitting system based on a single free curved surface. The system is composed of an entrance slit, a free curved surface reflecting mirror, plane blazed grating and an image surface. The system is characterized in that one non-rotational symmetric free curved surface reflecting mirror is used for two times to act as a collimating mirror and an imaging mirror respectively, and plane blazed grating which is high in performance and mature in processing technology is used cooperatively so that the spectrometer optical splitting system which is simple and compact in structure, small in aberration, low in distortion and high in efficiency is realized. According to the embodiment of the imaging spectrometer optical splitting system based on the single free curved surface, great spectral imaging is realized in 400-1000nm visible near-infrared spectrum so that spherical aberration, coma aberration, astigmatism, spectral distortion and spectral line bending are greatly corrected under the premise that the system is ensued to be simple and compact. The imaging spectrometer optical splitting system based on the single free curved surface can be applied to the field of hyper-spectral imaging of spaceflight and aviation.

The invention relates to a demodulation method for a fiber bragg grating distortion spectrum. Firstly, a sampling spectrum is obtained; secondly, a super-gaussian-function like distorted spectrum theoretical model with asymmetric properties is adopted, and the demodulation principle based on reflection spectrum reconstruction is applied to design a self-adaptive demodulation model for a distortedspectrum; finally, the self-adaptive demodulation model for the distorted spectrum is achieved by using the particle swarm optimization algorithm. The center wavelength and the distortion parameter ofFBG can be obtained by optimizing the sampling spectrum, and the problem is solved that demodulation is difficult due to the spectral distortion of FBG. The method can not only ensure high precisiondemodulation of a normal spectrum, but also perform self-adaptive demodulation of the distorted spectrum to the greatest extent.

The invention discloses an imaging spectrum image sparse representation method based on a ground object class classification redundant dictionary. The method comprises K-means, K-SVD-based classification redundant dictionary training and imaging spectrum image full-wave band sparse representation. Through unsupervised K-means clustering, imaging spectrum image information is classified into corresponding subsets according to ground object class; and through classification dictionary sparse decomposition of imaging spectrum images, full-wave band information quality of a reconstructed image is improved under the premise of sparsity. The characteristic that the ground objects, the spectrum images of which are similar, have similar spectrum curves is fully utilized, thereby preventing introducing of wrong information, ensuring the quality of the reconstructed spectrum image and effectively avoiding the problem of spectral distortion caused by independent operation of each wave band.