66 results about "Spectral vector" patented technology

The invention relates to the technical field of medicine quality detection, and particularly discloses an optical detection method and system for the content of vitamin tablets. The method comprises the following steps: driving an optical fiber probe array to carry out three-dimensional multi-angle near-infrared scanning through a multi-axis mechanical arm, and collecting reflection spectrum data; a weighted spectrum is generated through derivative spectrum analysis and double evaluation, and auxiliary material noise is corrected and separated in combination with subspace projection and a dynamic kernel function; performing spectral vector projection and regression coefficient iterative optimization through an online PLS model, and synthesizing an error coefficient based on a four-level index retrieval standard library to perform dual-channel feedback; and finally, fusing the credibility weight to output a detection result of the binding confidence. The method realizes nondestructive and high-precision detection of the vitamin tablets, has the beneficial effects of multi-dimensional data fusion, dynamic error compensation and high model adaptability, and remarkably improves the detection accuracy and reliability.

The invention relates to the technical field of image analysis, in particular to a concrete arch bridge crack semantic segmentation method based on multispectral imaging, and the method comprises the following steps: collecting a multi-period multispectral image through an unmanned aerial vehicle, segmenting a crack region through a K mean value of the multispectral image, extracting a multiband spectral vector sequence, and carrying out the sliding normalization to generate a principal axis spectral vector; and calculating a spectral vector included angle and a change rate mark jump point, screening boundary points by combining direction consistency and gradient scoring, extracting spectral lines Z-score standardization by time sequence alignment, evaluating discrete fluctuation, correcting an abnormal output concrete arch bridge crack binary segmentation map. According to the method, the reflection spectrum sequence is constructed through the multi-period multi-spectral image, the crack recognition precision is improved by combining the main shaft features and the sliding window, the texture interference is eliminated by using the spectral vector included angle change and the gradient score, the standardized spectral line time sequence model is generated, the boundary positioning accuracy is enhanced, the noise interference is reduced, and the long-term monitoring of the structural damage is supported.

The invention relates to the technical field of image enhancement, in particular to a traditional Chinese medicinal material infrared microscopic image enhancement method, which comprises the following steps of: performing two-dimensional Fourier transform on each spectral band image based on input original traditional Chinese medicinal material infrared spectral microscopic image data to obtain frequency domain representation, identifying a target frequency peak value position generated by interference in the frequency domain representation, and obtaining a target frequency peak value; and generating a stripe-removed hyperspectral data cube. According to the method, frequency domain analysis is carried out on an original infrared spectrum microscopic image through two-dimensional Fourier transform, stripe noise generated by light path interference is recognized and eliminated, a stripe-removed hyperspectral data cube is generated, and the input data quality of subsequent component separation is improved. And performing nonlinear decomposition on the spectrum vector after stripe removal, constructing a full-pixel end member contribution weight set, organizing weight values into an original abundance image set in a two-dimensional matrix form by utilizing an end member spectrum index, and realizing spatial decoupling and independent expression of multi-chemical component distribution.

The invention discloses a hyperspectral image particle segmentation method and device, relates to the field of image processing, and is used for improving the accuracy of particle segmentation according to a hyperspectral image. The method comprises the steps that a local area corresponding to pixels is selected, a preset matrix of the pixels is constructed, the preset matrix is formed by covariance of any two spectral vectors in spectral vectors of the pixels in the local area corresponding to the pixels, and whether the pixels are candidate edge pixels or not is judged according to the characteristic value condition of the preset matrix; compared with the existing edge detection method which only utilizes the spatial gradient information of the image, the method has the advantages that the accuracy of edge pixel detection can be improved by judging whether the pixel is the candidate edge pixel or not through combining the characteristics of the spatial dimension and the spectral dimension; and the particle region is further segmented according to the candidate edge pixels, so that the edge pixels are detected by combining the features of the spatial dimension and the spectral dimension and the particle region is segmented according to the edge pixels, and the accuracy of performing particle segmentation according to the hyperspectral image can be improved.

The present invention relates to the field of image analysis technology, specifically to a method for semantic segmentation of cracks in concrete arch bridges based on multispectral imaging. The method comprises the following steps: collecting multi-time multispectral images using an unmanned aerial vehicle (UAV), segmenting crack regions using K-means on the multispectral images, extracting and sliding normalizing a multi-band spectral vector sequence to generate a principal axis spectral vector, calculating the spectral vector angle and rate of change to mark transition points, screening boundary points by combining directional consistency and gradient scoring, extracting spectral patterns through time-series alignment and Z-score standardization, evaluating discrete fluctuations and correcting anomalies to output a binary segmentation map of concrete arch bridge cracks. In the present invention, a reflectance spectrum sequence is constructed using multi-time multispectral images, crack identification accuracy is improved by combining principal axis features and a sliding window, texture interference is eliminated by using spectral vector angle changes and gradient scoring, and a standardized spectral pattern time series model is generated to enhance boundary location accuracy, reduce noise interference, and support long-term monitoring of structural damage.

The invention provides a germplasm vigor detection method, device and equipment based on hyperspectral imaging and a medium, and relates to the technical field of intelligent agriculture, and the method comprises the following steps: obtaining spectral data of a target seed; determining a hyperspectral data cube of the target seed based on the spectral data; preprocessing the hyperspectral data cube to obtain a spectral vector corresponding to the hyperspectral data cube; extracting a local convolution feature of the spectral vector and a global feature of the hyperspectral data cube, and fusing the local convolution feature and the global feature to obtain a germplasm fusion feature; inputting the germplasm fusion features into a pre-trained classifier to obtain a target seed vigor detection result output by the classifier; the classifier is obtained based on germplasm sample features and corresponding label training. According to the method, the germplasm fusion features are obtained through the spectral data, so that the activity detection result of the target seeds is obtained, destructive experiments on the seeds are not needed, the detection efficiency and portability are improved, and the labor cost is reduced.

The invention relates to the technical field of foreign matter recognition, in particular to a foreign matter early warning method and device, electronic equipment and a storage medium. Carrying out dimensionality reduction on the plurality of first spectral vectors through a dimensionality reduction conversion matrix to obtain a plurality of second spectral vectors; classifying each second spectrum vector according to the relationship between the second spectrum vectors and a plurality of spectrum classes to obtain a plurality of spectrum class identifiers; and finally, selecting a plurality of target identifiers from the plurality of spectrum identifiers, determining a first foreign matter probability according to the plurality of target identifiers, the perimeter area ratio of the first region and a conditional probability equation, and performing early warning according to the first foreign matter probability. According to the invention, based on the visual identification area, the spectral data is extracted, dimensionality reduction and classification are carried out on the spectral data, the foreign matter risk is determined based on data dimensionality reduction, classification and statistics, the accuracy of foreign matter early warning is improved, and the probability of false alarm when the foreign matter is found is reduced.

The invention discloses a hyperspectral unsupervised anomaly detection method based on course learning and spectral parity decomposition. The method comprises the following steps of: 1, preprocessing an input hyperspectral image to obtain a spectral vector, and constructing a region-level training sample; 2, constructing a hyperspectral anomaly detection model based on curriculum learning and spectral parity decomposition, and obtaining representative spectral features of the obtained fine-grained region; 3, defining a mean square error loss function of the hyperspectral anomaly detection model; 4, training the hyperspectral anomaly detection model; 5, reconstructing the representative spectral features of the fine-grained region by using the trained hyperspectral anomaly detection model to obtain a reconstructed image, and calculating a reconstruction error graph of the spectral vector and the reconstructed image; and 6, carrying out binarization segmentation on the reconstructed error image to obtain an anomaly detection result. According to the method, the false alarm rate and the calculation complexity are reduced while the high detection precision is ensured, and the dual requirements of an actual scene for efficiency and reliability are met.

The invention discloses a plant leaf physicochemical parameter inversion method based on spectrum two-dimensional characterization and multi-task deep learning, and belongs to the technical field of plant physiological detection. The method comprises the following steps: constructing a hyperspectral plant leaf data set; preprocessing a hyperspectral plant leaf data set; reconstructing the preprocessed one-dimensional spectral vector into a two-dimensional image; taking the generated two-dimensional image as input, constructing a multi-task learning model, and performing training and evaluation; and applying the trained model to a test set to obtain an inversion result of the physical and chemical parameters of the leaf. The method solves the problem of parameter inversion interference caused by the same-spectrum foreign matter phenomenon, achieves the precise modeling of local fine feature extraction and global context relation, solves the inversion precision bottleneck caused by spectral response overlapping among parameters, improves the distinguishing capability of the model for easily confused parameters such as chlorophyll and carotenoid, and improves the accuracy of the model. And finally, high-precision and robust synchronous inversion of the physical and chemical parameters of the plant leaves is realized.

The invention provides a sea wave spectrum prediction method based on deep learning, and belongs to the technical field of sea wave spectrum prediction, and the method comprises the steps: calculating an accumulated energy function and relative accumulated energy based on sea wave spectrum data, and fixedly dividing a frequency domain into a plurality of subintervals according to a preset energy percentile threshold value; for each sub-interval, extracting at least one physical feature of segment energy, center frequency, peak frequency and spectral width of the interval, and combining the original spectral vector of the interval with the extracted physical features to form an input feature vector of the sub-interval; an independent neural network model is constructed and trained for each subinterval, the input feature vector of the corresponding subinterval is used as input, and the predicted spectrum vector of the subinterval is output; and splicing the prediction spectrum vectors output by the neural network of each subinterval according to a frequency sequence, performing weighted average processing at the junction of the subintervals, and performing reconstruction to obtain a complete sea wave spectrum prediction result.

Embodiments herein relate to a process for chemical interaction monitoring, such as employing data output from a Raman spectroscopy system relative to a composition undergoing the chemical interaction in a bioreactor. A system can comprise a memory that stores, and a processor that executes, computer executable components. The computer executable components can comprise an identifying component that identifies a raw dataset corresponding to a chemical interaction, and a matching component that generates matched data comprising a set of matches between time series data, corresponding to a range of time over which the chemical interaction was observed, and chemical interaction data comprised by the raw dataset.

The invention discloses a perception method based on multi-semantic space attention and Daubechies wavelet double-branch Mama, and belongs to the field of hyperspectral image processing. The problem that in the existing Mama-based model feature extraction process, the capacity of capturing multi-scale local structures and direction sensing information is insufficient is solved. The method comprises the following steps: inputting a hyperspectral image; projecting the spectral vector to an embedding space through an embedding layer to obtain an embedding feature; inputting the embedded features into an encoder, wherein the encoder comprises an SMSAMama branch, a DWTMama branch and a self-adaptive feature fusion module; the SMSAMama branch is used for extracting spatial features; the DWTAMba branch is used for extracting spectral features; the adaptive feature fusion module performs weighted integration on the spatial features and the spectral features by using randomly initialized fusion weights; and inputting the integrated features into a segmentation head to generate a final perception result. The method is used in agricultural monitoring and urban planning fields.

The invention provides a hyperspectral spectrum data fusion method and a hyperspectral spectrum data fusion system capable of widening a spectral band, and the method comprises the steps: carrying out the wavelength domain matching of Vis-NIR hyperspectral data obtained by a ground object spectrometer based on the visible light band of a hyperspectral image, and storing the resampled Vis-NIR hyperspectral data in a spectrum library. Performing similarity measurement on the spectral vector of each pixel in the hyperspectral image and all standard spectrums in the spectrum library to generate a spectral feature fingerprint map and a spectral response coefficient map; and performing spectrum reconstruction on each pixel in the hyperspectral image data in combination with the spectral feature fingerprint map and the spectral response coefficient map. And finally, obtaining a hyperspectral image cube after broadened spectrum fusion. According to the scheme, on the basis of wavelength domain matching, the spectral feature fingerprint map, the spectral response coefficient map and the like, the hyperspectral image cube obtained through final fusion covers a wider spectral range, and the original high spatial resolution is reserved.

The present invention relates to the field of image enhancement technology, specifically to a method for enhancing infrared microscopic images of traditional Chinese medicine, comprising the following steps: based on the input original infrared spectrum microscopic image data of traditional Chinese medicine, performing a two-dimensional Fourier transform on each spectral band image to obtain a frequency domain representation, identifying the target frequency peak position generated by interference in the frequency domain representation, and generating a de-streaked hyperspectral data cube. The present invention performs frequency domain analysis on the original infrared spectrum microscopic image through a two-dimensional Fourier transform, identifies and eliminates the stripe noise generated by optical path interference, generates a de-streaked hyperspectral data cube, and improves the input data quality of subsequent component separation. The de-streaked spectral vector is nonlinearly decomposed to construct a full-pixel end-member contribution weight set, and the end-member spectral index is used to organize the weight values ​​into an original abundance atlas in the form of a two-dimensional matrix, thereby realizing spatial decoupling and independent expression of the distribution of multiple chemical components.

The present invention discloses a metal-polymer target recognition and classification system based on infrared wide spectrum, which belongs to the field of target classification and recognition. It includes: a radiation brightness inversion module for obtaining a detected spectral vector with a target, respectively calculating the response gain vector and radiation bias vector of each band, weightedly calculating the response gain and radiation bias vector of the system, and inverting the radiation brightness vector of the target; a target recognition module for dividing the target's radiation brightness vector into three sub-vectors: shortwave band, medium wave band, and long wave band, respectively inputting them into a trained recognition network to obtain the recognition probability under each band; a fusion classification module for weighted fusion of the recognition probabilities under each band, comparing the fused probability with a set threshold, and if it exceeds, it is a metal material target; otherwise, it is a polymer material target. The present invention breaks through the limitations of interference target recognition, improves the anti-interference ability of the recognition network, and also improves the accuracy of target recognition.

The invention relates to the technical field of foreign matter recognition based on hyperspectrum, in particular to a foreign matter recognition method and device based on spectral feature selection, equipment and a medium, and the method comprises the steps: firstly obtaining a main dimension conversion matrix and a plurality of first dimension conversion matrixes; converting each first spectral vector in the first spectral vector set according to the main dimension conversion matrix, and screening out a plurality of first risk spectral vectors from the first spectral vector set according to a conversion result; performing conversion on each first risk spectral vector through the plurality of first dimension conversion matrixes, and performing spectral characteristic matching according to a conversion result; and finally, extracting a first image contour from the target image according to spectral characteristics, and sending the first image contour to an identification model for foreign matter identification. The method is good in preprocessing effect, and the recognition efficiency and the recognition precision are improved.

The application discloses a hyperspectral image real-time local anomaly detection method based on multi-line wave band processing, which comprises the following steps: reading hyperspectral image data, initializing the number of wave bands n determined by the number of filters, initializing the number of lines j based on wave band extraction, and initializing the interval number k between different wave bands; converting the data into data required for real-time detection processing according to the values of the number of wave bands n, the number of lines j and the interval number k; establishing a state equation of a spectral vector correlation matrix R(n) of hyperspectral data pixels; updating the inverse matrix R(n) of the correlation matrix R(n) by using a block matrix inversion formula ‑1 ; detecting the hyperspectral data required for real-time detection processing by using a multi-line wave band correlation matrix real-time local anomaly RX operator, and finally obtaining the result of the hyperspectral image real-time local anomaly detection. The application realizes local anomaly detection of the hyperspectral image, avoids high-dimensional data storage and repeated calculation, and has a good real-time local anomaly detection effect.

This invention provides a 3D printing metal material grinding device and grinding system, comprising: acquiring a sequence of hyperspectral images of metal powder particles to be ground, identifying and segmenting individual particles; extracting the pixel coordinates of the particle edge contour and the corresponding spectral response vector, and normalizing the spatial coordinates and spectral vectors respectively; constructing a first covariance matrix representing the spatial distribution of particles and a second covariance matrix representing the material distribution; performing singular value decomposition on the two matrices respectively, terminating the iteration when the relative change of the principal singular values ​​is less than a preset threshold, and obtaining singular values ​​and singular vectors; using the ratio of the two largest principal singular values ​​of the first matrix as the contour anisotropy parameter; using the sum of the singular values ​​of the second matrix plus the reciprocal of a preset constant as the material uniformity parameter; inputting the two types of metric parameters into a pre-trained control model, generating control commands and sending them to the grinding equipment actuator.

The present application relates to the technical field of foreign matter identification, and particularly relates to a foreign matter early warning method and device, electronic equipment and storage medium, the method of the present application first acquires a first region; then a plurality of first spectral vectors are reduced in dimension through a dimension reduction conversion matrix to obtain a plurality of second spectral vectors; then each second spectral vector is classified according to the relationship between the second spectral vector and a plurality of spectral classes to obtain a plurality of spectral class identifiers; finally, a plurality of target identifiers are selected from the plurality of spectral class identifiers, a first foreign matter probability is determined according to the plurality of target identifiers, a perimeter area ratio of the first region and a conditional probability equation, and early warning is performed according to the first foreign matter probability. Based on the region of visual identification, spectral data is extracted, the spectral data is reduced in dimension and classified, the foreign matter risk is determined based on data dimension reduction, classification and statistics, the accuracy of foreign matter early warning is improved, and the probability of false alarm when discovering foreign matter is reduced.

The invention provides a hyperspectral image aerial small target detection method and device, and the method comprises the steps: determining a plurality of wavebands with the maximum contrast based on a local contrast feature pattern of each waveband in a target hyperspectral image, and obtaining the target hyperspectral image after the waveband selection; calculating the enhanced mahalanobis distance of the spatial-spectral variance ratio of each spectral vector in the target hyperspectral image after the band selection; and determining a small target detection result of the target hyperspectral image based on the Mahalanobis distance with the enhanced spatial-spectral variance ratio. Therefore, small targets in the air can be monitored, wavebands with differences between the targets and the background can be well screened out, redundant background interference is reduced, the detection precision is improved, and the false alarm rate is reduced.

This invention provides a method and system for quality detection of edible fungi based on spectral features, belonging to the field of spectral detection technology. This invention acquires the light intensity sequence of a detection light source under a preset fluctuation state, extracts standard deviation, root mean square, and frequency features to construct a multidimensional fluctuation feature vector, achieving a comprehensive characterization of the light source's fluctuation characteristics. This provides rich input information for the offset prediction model, significantly improving the accuracy of spectral correction. Using the multidimensional fluctuation feature vector as the independent variable and the spectral offset as the dependent variable, an offset prediction model corresponding to each wavelength is established. Target wavelengths are then selected based on the goodness of fit, achieving high-precision correction of the original spectral data. The corrected spectral data is compared with the grade template spectral data in the quality template library using cosine similarity calculation, and the quality grade is selected by spectral vector matching, thereby significantly improving the stability and reliability of the edible fungi quality detection results.

The present invention discloses a method and device for identifying ground objects by spatial-spectral fusion of hyperspectral and lidar multi-mode images. The method comprises first generating a hyperspectral spatial image block and a spectral vector based on the hyperspectral image, and generating a radar spatial image block based on the radar image; then utilizing a multi-level spatial-spectral fusion encoder network to perform spatial-spectral feature-level fusion to obtain multi-level fusion coding features, and classifying the ground object samples through a classifier network to obtain classification results, wherein the multi-level spatial-spectral fusion encoder network is composed of a dual-branch spatial feature encoder branch and a spectral feature encoder branch whose outputs are multiplied, and the spectral feature encoder branch includes a feature embedding layer and a plurality of cascaded spectral feature encoders. The present invention aims to solve the problem of heterogeneous multi-source remote sensing data fusion between multi-mode remote sensing images such as hyperspectral and lidar, realize the mining of multi-level spatial-spectral fusion features, and make the performance of the classifier network no longer highly dependent on the number of labeled samples.

The invention discloses a multi-dimensional light field information space construction method for high-resolution infrared imaging, and the method comprises the steps: selecting an imaging scene, and determining a spectrum scanning voltage range; fixing a polarization angle, carrying out spectral voltage imaging scanning, carrying out vector slicing on an image obtained by each obtained spectral scanning voltage, stacking into a column vector, and finally obtaining a two-dimensional spectral vector space; the polarization angles are rotated, spectral voltage imaging scanning continues to be carried out, and a two-dimensional spectral vector space is obtained when each polarization angle is rotated; after all collection is finished, combining the two-dimensional spectrum vector spaces obtained at each polarization angle together in sequence to form a multi-dimensional light field information space containing a space image, spectrum information and polarization information; according to the invention, spectrum scanning is controlled through voltage capable of in-situ regulation and control, a complete multi-dimensional light field information space containing a space image, a spectrum and polarization is constructed, and high-resolution infrared imaging is realized.

The present invention provides a hyperspectral anomaly detection method based on a dual-branch generative adversarial network, comprising: decomposing a low-rank rough background matrix and a sparse rough anomaly matrix from an original hyperspectral image; inputting the rough background matrix and the rough anomaly matrix into a dual-branch network model, wherein the dual-branch network model outputs a reconstructed background matrix corresponding to the rough background matrix, and the dual-branch network model outputs a reconstructed anomaly matrix corresponding to the rough anomaly matrix; fusing the reconstructed background matrix and the reconstructed anomaly matrix to obtain fused hyperspectral data; calculating a pure background mean vector and a background covariance matrix using the reconstructed background matrix; solving for anomaly response values of each spectral vector in the fused hyperspectral data based on the background mean vector and the background covariance matrix; and performing normalization processing on the anomaly response values of each spectral vector to obtain an anomaly detection result map for the entire hyperspectral image. The present invention enhances the distinguishability between background and anomaly samples and improves anomaly detection accuracy.

The invention relates to the technical field of culture medium detection, and discloses a fruit fungus culture medium pollution identification method and system based on hyperspectral imaging, and the method comprises the steps: obtaining a data cube of a hyperspectral image of a culture medium, executing non-negative tensor decomposition, and obtaining a group of space factor graphs and corresponding spectral vectors; calculating a Bhattacharyya distance between each spectrum vector and a preset pollution strain spectrum library, and generating a pollution affinity coefficient corresponding to each space factor graph; calculating the mahalanobis distance of each pixel point in the data cube, and generating a global spectral distortion graph; initializing a pollution potential field with the same spatial size as the hyperspectral image, and carrying out iterative updating through a reaction diffusion equation until convergence; and performing threshold segmentation on the converged pollution potential field to obtain a binary mask of the pollution area. According to the method, the analysis of the spectral dimension and the structural constraint of the spatial dimension are deeply fused by constructing the reaction diffusion model, so that the early and accurate identification of the culture medium pollution is realized.

The invention relates to the technical field of remote sensing image processing, and relates to a hyperspectral image dimension reduction method, device and equipment and a storage medium, and the method comprises the steps: dividing hyperspectral image data into N three-dimensional tensor blocks; taking the complete spectral vector of each pixel of the hyperspectral image data as a clustering input feature in a super-pixel segmentation algorithm, and generating a super-pixel semantic pseudo tag; on the basis of a maximum frequency principle or a voting strategy, according to the pixel semantic pseudo-tag of each pixel in the tensor block, determining a super-pixel semantic pseudo-tag of each tensor block; constructing a graph by taking the tensor blocks as nodes and taking the similarity between the tensor blocks as edges; and constructing a graph regularization dimensionality reduction model according to a locality preserving projection rule, solving the model to obtain an optimal projection tensor, and performing dimensionality reduction on the hyperspectral image by using the projection tensor. According to the method, the spatial-spectral information of the hyperspectral image can be considered at the same time, the discrimination and stability of intra-class structure expression and dimension reduction results are improved, and the method has good practical value and popularization potential.

The application provides an online water quality analysis method and system in a radioactive waste liquid treatment process, and relates to the technical field of online water quality monitoring.The method comprises the following steps: step 1, passing a clarified sample into an analysis pool, collecting Raman spectral characteristic peak data in real time through a plurality of groups of optical fiber Raman probe arrays arranged in the analysis pool along the flow direction of the waste liquid; performing principal component analysis on the Raman spectral characteristic peak data, extracting characteristic spectral vectors corresponding to main radioactive nuclides and interfering substances, and constructing a two-dimensional concentration distribution field according to the continuous change of the characteristic spectral vectors in the spatial position; and step 2, extracting an isochromatic line and performing discrete sampling on a specific interfering substance component in the two-dimensional concentration distribution field, calculating the minimum circumscribed circle of all sampling points, and obtaining the center coordinates and radius of the isochromatic line.The application realizes intelligent regulation and control of treatment process parameters and guarantees that the discharged water quality is stable and up to the standard.

The present application belongs to the technical field of remote sensing, and relates to a weight dictionary thermal infrared hyperspectral mixed pixel temperature emissivity solving method. The method comprises: obtaining simulation data and performing inversion channel screening according to the simulation data; obtaining metadata and quality control marks and performing effective pixel screening; performing atmospheric correction; constructing an initial value dictionary and training to obtain an emissivity dictionary; calculating a ground surface emissivity spectral vector, reconstructing the emissivity spectral vector based on a ROMP algorithm, performing weight redistribution through a weight distribution algorithm, and iteratively solving to restore the ground surface temperature and the noise-free and unbiased emissivity spectrum of the mixed pixel. The present application combines the ROMP algorithm and the weight distribution algorithm into a WOMP algorithm, overcomes the greediness of the traditional algorithm, cooperates with the initial value dictionary and the emissivity dictionary, reconstructs the unbiased emissivity spectrum, realizes the synchronous inversion of the ground surface temperature and the emissivity spectrum, and significantly improves the stability, unbiasedness and decoupling capability of the mixed pixel inversion.

The invention provides a fire range extraction method based on a nuclear combustion index and adaptive spatial spectrum optimization, and relates to the technical field of remote sensing image processing, and the method comprises the steps: collecting remote sensing image data; mapping a spectral vector corresponding to the fire image data set to a high-dimensional space based on a Gaussian radial basis kernel function, and performing kernel principal component analysis on the spectral vector to construct a kernel difference combustion index; according to kernel density estimation, performing adaptive threshold segmentation on the kernel difference combustion index to obtain an initial fire pixel detection result; constructing a spectrum space composite kernel similarity matrix, and optimizing the initial fire pixel detection result in a conditional random field according to the spectrum space composite kernel similarity matrix to obtain a transition fire pixel detection result; and performing morphological operation and small patch area filtering on the transition fire pixel detection result to obtain a predicted fire pixel detection result. According to the invention, the overall precision of fire range extraction can be improved.

A method for monitoring gas leakage based on spectral vector data processing, which adopts technical solutions such as collecting spectral vector data, inputting into a generative adversarial network for training, differentiating true sample data and false sample data to achieve data augmentation, inputting into a feature extraction model for training, adjusting the weights and biases of the neural network using different strategies at each stage, inputting into a feature dimensionality reduction model for training, reconstructing features from the low-dimensional representation, inputting into a classifier model for training, converting to qubits, and processing sample data using the trained feature extraction model, data dimensionality reduction model, and classifier model. It overcomes the technical problems of poor model generalization ability caused by insufficient training samples, relying on global gradient information to update weights, and overfitting, and produces technical effects such as improving the quantity and quality of generated data of spectral data samples, enhancing the flexibility of the algorithm at different data stages, minimizing the difference between input and reconstructed output, enhancing the performance of the classifier, and accurately predicting the leakage states of different types of gases.