5208 results about "Wave band" patented technology

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

The invention discloses a portable unmanned aerial vehicle sound wave countering system and method, and relates to the field of radio countermeasure and sound wave interference, and the system comprises a multi-source sensing module which is used for obtaining a multi-modal feature data set of a target unmanned aerial vehicle; the signal processing module is used for carrying out space-time correlation analysis on the multi-modal characteristic data set and generating a threat evaluation parameter set containing an unmanned aerial vehicle type identification result, communication protocol analysis characteristics and navigation mode discrimination indexes; the countering strategy generation module is used for generating a sound wave modulation parameter set, a radio frequency interference waveform sequence and a positioning deception signal template; the cooperative interference module is used for generating a multiband joint countering instruction set according to the improved phase cooperative control strategy; and the execution monitoring module is used for executing the multiband joint countering instruction set in the preset airspace range. According to the portable unmanned aerial vehicle sound wave countering system and method provided by the invention, the multi-level countering capability for different threat levels is improved.

The invention provides a room obstacle detection optimization method and system based on a cross-modal attention mechanism. Wherein a dynamic compensation coefficient is calculated by acquiring motion track data of the mobile equipment, and a stable visual coordinate system is constructed. Visible light and near-infrared wave band acquisition of the multispectral sensor is synchronously triggered, the visible light captures geometric deformation characteristics of a floor projection surface, and the near-infrared detects a local heat radiation abnormal area. And extracting a thermal feature connected domain conforming to periodic biological motion as a living obstacle, and marking a collision risk boundary of a static obstacle. And based on a living body motion law and a collision boundary deviation trend, synchronously optimizing the dual-band exposure parameters and updating the trajectory prediction model, and forming a closed-loop feedback link of sensing and planning. According to the technical scheme provided by the invention, the living body and the static obstacle are accurately distinguished in a dynamic environment, and the sensor parameters are adaptively adjusted to improve the real-time obstacle avoidance response capability.

The invention relates to the technical field of water quality detection, and discloses a water quality pollutant detection method, system and equipment based on spectral analysis, and the method comprises the following steps: carrying out real-time spectral data acquisition on a water body sample in a vehicle driving process through a multispectral sensor array in a vehicle-mounted water quality detection device; obtaining a water body spectrum three-dimensional data cube; carrying out anti-vibration wavelength calibration and ambient light source interference elimination processing to obtain a preprocessed spectrum data set; performing standard multivariate decomposition processing and horizontal local constraint optimization of a pollutant target wave band on the preprocessed spectrum data set to obtain enhanced feature fingerprint spectrums for different types of water quality pollutants; and inputting the enhanced feature fingerprint spectrum into a deep belief network model to perform pollutant feature matching analysis to obtain various pollutant types and corresponding concentration values in the water body sample, thereby effectively eliminating ambient light changes and other background interferences, not only determining the pollutant types, but also accurately quantifying the concentration values.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention relates to the technical field of image recognition, and further relates to a phenotypic character image recognition and extraction method for efficient corn breeding. The method comprises the following steps: step 1, acquiring an image of the same plant canopy in a test field by using an unmanned aerial vehicle carrying a multispectral sensor in a plurality of different growth periods from a corn jointing period to a mature period, so as to obtain a normalized grayscale image; 2, performing gradient operation on a target wave band based on the normalized grayscale image to obtain significant edge intensity, and generating a character sensitive probability field of a corresponding time phase; and 3, presetting an optimal observation period for each target character, selecting a pixel set which has a probability value not less than 0.5 and is judged to be the character type from an image of a time phase corresponding to the optimal observation period, and sequentially measuring the total projection area, the average connected domain area and the average leaf angle to obtain a comprehensive breeding sequence value of the character. According to the method, high-precision probability identification of the effective phenotype area of the grain-leaf is realized.

The invention relates to the technical field of trajectory tracking, in particular to an unmanned aerial vehicle trajectory tracking target classification method and system based on a multi-frequency-band radar, and the method comprises the following steps: synchronously collecting original radar echo data of a millimeter wave frequency band, a Ku frequency band and an L frequency band, and generating a multi-frequency-band data set comprising a timestamp, an azimuth angle and a pulse sequence; extracting multi-dimensional features of the target from the synchronized multi-band data set, and generating a multi-band feature vector; constructing a dynamic trajectory association matrix, and determining the space-time continuity of a target trajectory through a trajectory association algorithm based on an attention mechanism; constructing a lightweight classification model; performing online classification on the real-time multi-band feature vectors by using a lightweight classification model, and outputting a target type and confidence; according to the method, capture of the motion state of the target is enhanced, rapid response can be made to tiny motion and change of the target in a complex scene, and therefore more comprehensive and accurate time sequence context information is provided for subsequent classification tasks.

The invention relates to the technical field of photovoltaic module detection, in particular to a hot spot detection system and method for a photovoltaic module. Comprises: a multispectral imaging module configured to synchronously collect image data of a photovoltaic module in a visible light band, a near-infrared band and a long-wave infrared band; the electroluminescence analysis module is in signal connection with the multispectral imaging module and is used for acquiring carrier recombination radiation data of the photovoltaic module in a reverse bias state; the dynamic threshold value processing module is configured to establish a dynamic correlation model of the environment parameters and the hot spot characteristic parameters; the three-dimensional reconstruction module is configured to perform spatial registration on the multispectral imaging data and the electroluminescence data and construct three-dimensional temperature field distribution of the photovoltaic module; the defect positioning module is configured to determine the accurate position of a hot spot according to temperature field distribution and carrier recombination characteristics; according to the scheme, a novel detection system architecture with multi-mode collaboration, high dynamic adaptability and three-dimensional analysis capability is constructed.

The invention relates to the technical field of remote sensing hyperspectral images, in particular to a remote sensing hyperspectral image super-resolution reconstruction method based on a hybrid neural network, and the method comprises the steps: firstly carrying out the preprocessing of a remote sensing hyperspectral image data set, and obtaining a training set; constructing a hybrid neural network with a spectrum hypergraph branch, a space hypergraph branch, a semantic hypergraph branch, a sensitive wave band extraction branch and a recovery and reconstruction branch; training the hybrid neural network by using a training set to obtain a hybrid network model; and finally, inputting a to-be-reconstructed low-resolution remote sensing hyperspectral image into the hybrid network model to obtain a corresponding reconstructed remote sensing hyperspectral image. According to the method, space, spectrum and semantic feature information are fully combined, and a main body structure and detail change characteristics in an image are captured; the sensitive wave band extraction branch is provided to enhance cross guidance and fusion among multi-domain features, and the effect of super-resolution reconstruction is fully improved.

The invention provides a live-action three-dimensional model quality inspection method and system fused with deep learning, and the method comprises the steps: building a live-action three-dimensional model through obtaining multispectral imaging data of a target scene; multispectral imaging data is optimized, standardized input data is generated, on the basis of the standardized input data, correlation characteristics among multispectral bands are extracted through a semantic segmentation network driven by deep learning, a pixel-level segmentation result is generated in combination with geometric structure characteristics of a live-action three-dimensional model, verification is carried out based on a preset topological continuity rule, and the segmentation result is obtained. Generating a multispectral feature associated data set, and performing quality inspection on the live-action three-dimensional model according to a comparison result of geometric feature segmentation deviations among different wavebands and a preset tolerance range to obtain a quality inspection result; according to the method, the refined quality inspection of the live-action three-dimensional model is realized, the abnormal region with the fractured or distorted geometric structure is effectively identified and positioned, and the geometric consistency and the overall quality of the live-action three-dimensional model under the complex illumination condition are improved.

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

The invention provides a torreya grandis forest extraction method based on a deep learning network and a multi-temporal remote sensing image, and the method comprises the steps: carrying out the data preprocessing of a remote sensing image, constructing and obtaining a comprehensive feature image of each month, extracting the pixel samples of torreya grandis and non-torreya grandis types, calculating and obtaining a comprehensive class spacing distinguishing capability index of each month, and obtaining a torreya grandis forest extraction result. Obtaining an original wave band feature set of the similar hyperspectral structure; performing feature optimization by using a maximum correlation minimum redundancy algorithm to obtain an optimized waveband feature set; marking torreya grandis and non-torreya grandis areas according to the torreya grandis sample points and the high-resolution remote sensing image, and making classification labels for deep learning; and constructing a space-spectrum multi-scale feature fusion network model, inputting the optimal waveband feature set into a deep learning network for training, and outputting classification results of torreya grandis and non-torreya grandis. According to the method, the multi-temporal remote sensing image can be fully utilized, and the spatial and spectral features are effectively extracted and fused, so that the recognition precision and efficiency of the torreya grandis are improved.

The embodiment of the invention provides an X-band weather radar non-rainfall data quality control method and system. The method is applied to the technical field of weather radars and comprises the following steps: acquiring X-band weather radar data; identifying and eliminating the electromagnetic interference echo according to the continuity characteristic of the electromagnetic interference echo and the distribution condition of the correlation coefficient CC and the differential reflectivity factor ZDR; judging whether the difference value of the reflectivity factors of each echo point before and after hardware filtering is greater than a preset threshold value or not, and if so, marking the echo point as a rainfall echo; if not, fuzzy logic recognition elimination based on characteristic parameters is carried out; wherein the non-precipitation echo probability PNPR and the precipitation echo probability PPR of each echo point are calculated according to the characteristic parameters, whether the PNPR is larger than the PPR or not is judged, and if yes, the echo point is marked as a non-precipitation echo; if not, non-rainfall weak echo discrimination is carried out; whether the echo is a non-rainfall echo is determined by comparing the reflectivity factor mean value # imgabs0 #, the correlation coefficient mean value # imgabs1 # of the elevation angle of the whole layer of the echo and the ratio ValCC of the effective distance library number of the correlation coefficients with a judgment threshold value, and if the echo is the non-rainfall echo, the echo point is marked as the non-rainfall echo; and if not, continuously filtering scatter and plaque noise around the echo point by using the reflectivity factors in the radial direction and the azimuth to obtain the rainfall echo. In this way, the quality of weather radar data can be improved.

The invention relates to the technical field of coal gangue recognition, in particular to a coal gangue AI recognition method and system based on infrared spectrum measurement, and the method comprises the steps: obtaining a near infrared spectrum of each contrast reference sample and a near infrared spectrum of each to-be-detected sample, and respectively recording the near infrared spectrums as each contrast infrared spectrum and each to-be-detected infrared spectrum; determining a contrast absorption vector and a to-be-detected absorption vector; determining the absorption peak saliency of each wave band; obtaining a target wave band; determining an absorption level vector of any target wave band; determining the absorption peak recognition degree and the potential information characteristic value of any target wave band in each infrared spectrum to be detected; determining the coal gangue recognition degree; and obtaining a training set and a training label of the classification model, and identifying the coal gangue in the to-be-detected sample. According to the method, the problem that the key feature information of the coal gangue cannot be accurately extracted due to the fact that the infrared spectrum data acquisition process is easily interfered by noise or redundant information is solved, so that the coal gangue recognition accuracy is improved.

The invention relates to the technical field of image recognition, and discloses a multispectral image and deep learning-based forest disease and insect pest automatic recognition method, which comprises the following steps of 1, carrying a multispectral camera containing a red edge wave band through an unmanned aerial vehicle to obtain a forest region image; 2, calculating a red edge normalized vegetation index of the image; 3, performing time sequence modeling on the red edge normalized vegetation index data of more than five consecutive periods, and inputting a time sequence convolutional network to generate an early lesion probability graph; 4, detecting a pest and disease damage target by adopting a multi-scale adaptive feature pyramid network; 5, outputting a disease and pest distribution thermodynamic diagram; and 6, driving the unmanned aerial vehicle cluster to execute precise pesticide spraying. According to the method, through the high sensitivity of the red-edge wave band to chlorophyll degradation and in combination with sequential convolutional network dynamic modeling, an initial lesion area can be recognized 7-10 days before disease development, the early disease recognition capability is remarkably improved, the disease discovery period is shortened, and large-scale disease diffusion is effectively avoided.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a terrain recognition and three-dimensional reconstruction method based on a remote sensing surveying and mapping image, which comprises the following steps of: recognizing and selecting a key spectral band based on a remote sensing image, adjusting the configuration of a spectral filter, extracting key terrain data according to configuration data, and analyzing terrain change by utilizing terrain feature data. And recording related wave bands and positions, forming a comparison table, and combining natural colors and surface texture analysis to generate a three-dimensional model of the terrain. According to the method, the key spectrum sections are screened by adopting the preferable waveband index, the specificity and efficiency of data acquisition are improved, the spectral response section is accurately adjusted, it is ensured that a spectral region with remarkable earth surface reflection characteristics is focused on, and the practical value of spectral data is improved; the spectral configuration data optimizes the response performance of the sensor so that the data collected from the terrain is more sensitive and accurate.

The present disclosure provides a method for remote sensing blue-green wave band ratio logarithmic water depth retrieval of wavelet spline instantaneous tidal height correction, and belongs to the field of remote sensing water depth retrieval. Aiming at water depth retrieval precision reduced by blue-green light and a tidal height in a remote sensing water depth retrieval process, the model fully considers a linear correlation between an attenuation ratio of the blue-green light in water and a depth, the smoothness of the tidal height, and the characteristics of consistent convergence, first-order continuous derivation and second-order continuous derivation of the tidal height with time change, and constructs the method for remote sensing blue-green wave band ratio logarithmic water depth retrieval of wavelet spline instantaneous tidal height correction, and according to Molokai experiment verification, compared with an early model, the invention improves the remote sensing water depth retrieval precision.

The invention relates to the technical field of infrared gas analysis, and discloses a multi-sensor fused infrared gas analysis method and device.The method comprises the steps that self-adaptive digital filtering and multi-scale wavelet denoising operation is carried out on multi-component gas spectral absorption data collected by a multi-band infrared sensor array; a corrected multi-dimensional spectrum matrix is obtained; performing partition cross validation compensation operation to obtain a standard spectrum matrix; constructing a covariance matrix, and extracting principal component features and latent variable features through eigenvalue decomposition to obtain a spectral eigenvector; inputting a deep self-encoding network model to carry out nonlinear mapping and residual reconstruction calculation to obtain a compensated feature mapping vector; performing information fusion to obtain a fusion decision vector; and respectively inputting the fusion decision vector into a Gaussian kernel support vector classifier and a partial least square regression model, and outputting a type identifier and a concentration value of the target gas. According to the method, high-precision identification and quantitative analysis of gas components are realized.

The invention provides a large-scale water body underwater terrain inversion method and device and a medium, and the method comprises the steps: carrying out the partitioning of a to-be-inverted water body region, and obtaining a deep water region and a land-water conversion shallow water region; based on a spatial interpolation method, carrying out inversion on the land-water transformation shallow water area to obtain a land-water transformation shallow water area topographic image; a deepwater area water depth inversion model constructed based on the wave band reflectivity information and the underwater topographic elevation data performs inversion on the deepwater area to obtain a deepwater area underwater topographic image; and carrying out image splicing fusion on the land and water transformation shallow water region topographic image and the deep water region underwater topographic image to obtain the underwater topography of the water body to be inverted. In the face of a complex and changeable water body environment, a water body area to be inverted is divided into a deep water area and a land and water conversion shallow water area, and different and more targeted inversion methods can be adopted according to the characteristics of water bodies in different areas.

The invention discloses a multi-source data-combined pit-pond water depth calculation and storage capacity calculation method, which comprises the following steps of: acquiring actually measured water depth data, performing data preprocessing, and dividing the data into a training sample point set and a test sample point set; acquiring a multispectral remote sensing image of the reckoning area, and executing image preprocessing; unifying to the same coordinate system; fractal dimensions of each wave band and the combined wave band are calculated, and a water body region is segmented; establishing a mapping relation between the water depth values of the training sample points and the standardized multispectral apparent reflectance; establishing a water depth inversion model, and verifying the water depth inversion model by using the test sample point set; calculating discrete water depth points of the pit pond by using the optimal water depth inversion model; and establishing a minimum energy curved surface to obtain the water storage amount of a single pit pond, and accumulating to generate the total water storage amount. According to the method, actual measurement and remote sensing data are fused, the energy minimization water depth inversion model is constructed, accurate calculation of the water storage capacity of the pit pond is achieved, and the method has the advantages of being high in precision and high in applicability.

The invention relates to the technical field of spectrum water quality detection, in particular to a livestock breeding water quality detection method which comprises the following steps: acquiring absorption spectrum data of a livestock breeding water body, and acquiring a short-time fluctuation difference value of livestock breeding water at each measurement moment on each wave band absorption spectrum; the method comprises the following steps: based on the deviation between short-time fluctuation difference values of livestock breeding water in each wave band and other wave bands, acquiring short-time interference characteristic values of the livestock breeding water in absorption spectra in each wave band at each measurement moment, and combining spectral data differences of each measurement moment and a plurality of previous measurement moments in the same wave band; determining a water quality change coefficient of the absorption spectrum of each wave band; and according to the water quality change coefficient in combination with the short-time interference characteristic value, obtaining a water quality pollution evaluation value of the livestock breeding water at each measurement moment so as to judge the water quality condition of the livestock breeding water. The detection accuracy of the water quality of the livestock breeding water can be improved.

The invention belongs to the field of remote sensor calibration, and provides an ROLO model improvement method combining foundation and meteorological satellite lunar observation data, which comprises the following steps of: acquiring a meteorological satellite lunar observation image data set and a foundation lunar observation image data set, and respectively correcting the meteorological satellite lunar observation image data set and the foundation lunar observation image data set; obtaining standard meteorological satellite observation moon radiance and foundation observation moon radiance, and calculating observation moon irradiance; calculating geometric parameters of observation points, the earth, the moon and the sun at the observation moments of the foundation and the meteorological satellite, and calculating the moon irradiance of the ROLO model; polynomial fitting is adopted for the relative deviation between the observed moon irradiance of different wave bands and different moon phase angles and the moon irradiance of the ROLO model, and the ROLO model is improved. The method is not affected by the atmosphere, and errors caused by atmospheric correction of ROLO model foundation observation data are directly avoided; and the phase angle dependence of the ROLO model can be corrected, and the prediction precision of the ROLO model can be improved.

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

The invention discloses a cultural relic digital fingerprint identification method based on geometric texture and spectral information, and the method comprises the steps: obtaining multi-source data, carrying out the texture mapping, and forming a three-dimensional grid model with texture data; curvature values and topological features of all vertexes of the three-dimensional grid model are calculated, feature fusion is carried out on the basis of local saliency adjustment feature weights, and fingerprint point location screening is completed; based on the screened fingerprint point locations, a significant plane is constructed through a bit plane, and texture features of the cultural relics are extracted based on an improved histogram and a gray-level co-occurrence matrix; extracting characteristic wave bands of the cultural relics based on the screened fingerprint point locations in combination with the hyperspectral data; and multi-modal fusion identification is carried out based on features of geometry, texture and spectral information. According to the method, the geometric structure, the texture features and the hyperspectral information are fused, multi-mode collaborative cultural relic identity high-precision identification is achieved, and the method has the advantages of being high in accuracy, high in robustness, good in interpretability and capable of adapting to complex environments.

The invention discloses a soil water content inversion method and system based on L-band dual-polarization data and a medium, and belongs to the field of remote sensing technology and environment monitoring. The system comprises an SAR data acquisition module, an optical data processing module, a vegetation scattering separation module, an inversion model construction module, a spatial distribution calculation module and a risk assessment module, wherein the SAR data acquisition module is used for regularly acquiring L-band dual-polarized SAR data of a target area; the optical data processing module assists in identifying and separating vegetation scattering components by calculating vegetation indexes; the vegetation scattering separation module is used for extracting pure soil back scattering signals; the inversion model building module is used for training and optimizing a soil water content inversion model based on the pure soil back scattering signals and field sampling data; the spatial distribution calculation module is used for calculating a spatial distribution diagram of the surface soil water content of the target area; and the risk assessment module is used for carrying out landslide risk grade division and early warning management.

The invention discloses a snapshot type hyperspectral imaging industrial online sorting system which comprises a support assembly, an optical imaging assembly and a motion control assembly. The bracket assembly consists of a bottom plate, and an adjustable bracket and a fixed bracket which are fixed on the bottom plate; the optical imaging assembly comprises a lighting unit, an imaging objective lens and a snapshot type hyperspectral camera, and can synchronously obtain space image information of a measured object and spectral information of a continuous wave band in a single exposure process; the motion control assembly realizes automatic transmission of the measured object through the transmission platform; the data processing module is integrated with an image preprocessing unit, a spectral analysis unit and a spectral database, and is used for end-to-end closed-loop control from original hyperspectral data generation to sorting decision making; the system adopts a metasurface light splitting structure to realize real-time decoding, performs material or category identification by using a pre-trained classification model, and finally automatically generates a sorting decision signal according to an identification result to drive a motion control assembly to complete sorting operation.