469 results about "Image texture" patented technology

The invention provides a real scene three-dimensional modeling method and system fusing laser point cloud and images, and belongs to the technical field of urban three-dimensional modeling. According to the method, laser point cloud data and multi-view image data of a target scene are acquired through a synchronous acquisition device, and joint calibration is performed on a laser radar and a camera to generate space-time synchronization parameters. And a dynamic weight registration algorithm is utilized to dynamically adjust the registration weight according to the point cloud density distribution and the image texture complexity so as to realize the multi-level registration of the laser point cloud and the image data. The registered data is used for constructing a three-dimensional geometric model and generating a seamless texture map, meanwhile, semantic-driven hole repairing is carried out on a model missing area, and finally a complete three-dimensional model is output. According to the real-scene three-dimensional modeling method and system fusing the laser point cloud and the image, the limitation of single data source modeling is solved, and real-scene three-dimensional modeling with high precision, high integrity and real-time updating is realized.

The invention discloses a few-sample defect identification method based on cross-modal text semantic driving, and belongs to the technical field of image processing. Aiming at the problem of insufficient generalization of a detection model caused by scarcity of abnormal samples and dynamic evolution of defect types in an industrial quality inspection scene, an unknown defect type can be accurately identified only by a small amount of normal data by establishing a dynamic feature recombination mechanism and an adaptive discrimination boundary; according to the method, a simulation sample similar to a real defect in form is generated on a normal sample through a matching relation between text description and image features; when a defect type which is not seen is encountered, a comparison standard of image textures can be automatically adjusted according to text semantics, subtle differences between a normal area and an abnormal area can be accurately distinguished, dependence on real defect data is not needed, and the sample defect identification precision is further improved.

The invention relates to the technical field of intelligent manufacturing, in particular to an HTCC ceramic defect automatic identification method based on industrial model assistance, which comprises the following steps: step 1, collecting sintering temperature gradient, lamination pressure and green body water content process parameters in the HTCC production process in real time; step 2, performing non-contact dielectric property scanning on the sintered HTCC substrate on a production line conveyor belt; 3, based on the coordinates of the dielectric abnormal region, controlling an annular polarization light source to inhibit the incident angle reflected by the metallization layer from irradiating the target region; 4, inputting the dielectric anomaly frequency band feature vector and the defect image texture feature in the step 3 into a cross-modal attention network to generate a fusion feature map; and 5, establishing an association mapping library of the defect type and the process deviation type. Through accurate real-time monitoring, intelligent defect identification and classification, automatic process adjustment and a continuously optimized feedback mechanism, the efficiency, quality and intelligent level of the ceramic production process are greatly improved.

The invention discloses a water quality monitoring method and system based on artificial intelligence. The method comprises the following steps: acquiring a comprehensive data set composed of sensor data, satellite images and meteorological parameters; according to the water flow velocity and pollution concentration gradient in the comprehensive data set, adopting a dynamic sampling algorithm to adjust the sampling frequency and position, and outputting adjustment data; performing space-time interpolation processing on the adjusted data to obtain a preprocessed data set with high-density space-time coverage; key features of sensor values, image textures and meteorological parameters are extracted from the preprocessed data set by adopting a principal component analysis method, a weighted feature matrix is constructed, and a fusion feature set is obtained; and judging whether the dimension of the fusion feature set exceeds a preset threshold value, if the dimension of the fusion feature set exceeds the preset threshold value, performing dimension reduction and classification on the fusion features by adopting a random forest algorithm, and optimizing model parameters through cross validation to obtain a pollution concentration prediction result. Effective technical support is provided for water environment protection, and important ecological and social benefits are achieved.

The invention discloses a tunnel disease recognition model training method and system based on a point cloud and an image, and the method comprises the steps: synchronously collecting tunnel point cloud and image data, carrying out the calibration and registration, achieving the spatial alignment, preprocessing the point cloud, generating a gray-scale image and a depth image, collecting continuous images through a line-scan digital camera, generating a spliced image, and carrying out the recognition of tunnel diseases. Partitioning a large-size image after multi-image space-time synchronization; a multi-branch network is constructed, point cloud geometry and image texture features are extracted by using Point Net / 3DCNN and CNN / Transform respectively, and semantic collaborative fusion is realized through an intermediate layer fusion module; parameters are adjusted according to errors through self-adaptive training, manual labeling dependence is reduced in combination with transfer learning and the like, and convergence is accelerated through online iteration, joint loss and self-adaptive weight to improve generalization; the performance of the model is evaluated through field testing and indexes in various tunnel environments, and the structure is optimized according to data to ensure that engineering is feasible and efficient. The model can accurately identify various diseases on different tasks, and can adapt to complex and changeable working conditions in tunnel detection.

The invention discloses a coal bunker reserve real-time monitoring method integrating a laser radar and a camera. The method comprises the following steps: step 1, carrying out space-time joint calibration and hardware-level synchronization on a laser radar and a camera, and establishing a projection relationship between a point cloud and an image; 2, cooperatively collecting coal bunker data based on the calibration parameters, and obtaining a point cloud and a high-resolution image; step 3, heterogeneous preprocessing is carried out on the collected point cloud and the image, denoising, segmentation and down-sampling are carried out on the point cloud, semantic segmentation is carried out on the image, and coal and foreign matters are identified; step 4, based on image texture and point cloud coordinate coupling mapping, reconstructing a three-dimensional model with a semantic tag, projecting the point cloud to an image plane to perform feature matching so as to endow the texture, and fusing image semantic information to correct the model; and 5, dynamically calculating the volume of the coal pile by using the semantic three-dimensional model to realize real-time monitoring of reserves. The system overcomes the limitation of a single sensor, achieves the real-time and high-precision monitoring of the reserves of the coal bunker, and effectively improves the intelligent level of coal bunker management.

The invention discloses a non-aligned spatio-temporal data driven multi-sensor fusion method and system, and the method comprises the steps: building a matching measurement function through collecting radar point cloud density and visual image texture, and constructing a synchronous gradient field through employing a space depth change parameter and a visual motion contour, and determining a data synchronization standard; detecting a time sequence abnormal accumulation area based on a synchronization standard, and identifying an abnormal starting point and propagation time delay through a backtracking track to form an asynchronous sensor coordinate table; iMU angle increment and sensor response delay are collected, and sampling imbalance information is generated through time domain matching of an attitude switching signal and a data collection inflection point; performing association mapping on the asynchronous coordinate table and the sampling imbalance information to generate a feature offset, determining a matching core area, and creating a hierarchical calibration sequence; performing internal and external source difference identification analysis on the calibration sequence to obtain an offset transfer speed to form an offset coupling factor; and a fusion enhancement region is determined based on the offset coupling factor, and high-precision multi-source data fusion is realized through cascade analysis and gain coefficient coding.

The invention discloses a roadside guardrail deformation detection method based on image and point cloud fusion and related equipment, and the method comprises the steps: collecting a guardrail region image through a high-definition camera carried by an unmanned plane, and synchronously collecting the point cloud data of a guardrail region through a laser radar; then, a joint calibration and iterative nearest algorithm is used for carrying out space-time registration on the guardrail area image and the point cloud data which are synchronously collected, and a multi-modal data set is generated, so that the problem of space-time dislocation caused by sensor movement is solved; an improved double-branch deep learning model is adopted to respectively extract image texture features and point cloud geometric features from a multi-modal data set, and the two features are fused to overcome a single-modal defect; and extracting the contour of the guardrail beam plate from the fusion feature map, aligning the contour of the guardrail beam plate with the reference model, and carrying out quantitative calculation to quantify the deformation degree of the guardrail and realize quantitative detection of the deformation of the guardrail.

The invention discloses a marine plankton hyperspectral imaging detection system, relates to the technical field of spectrum detection, and is used for solving the problem of poor spectrum classification and recognition in a water body environment. According to the method, the hyperspectral image and the environmental disturbance parameters are synchronously acquired, and the disturbance mapping sequence is constructed, so that accurate alignment between the image and disturbance is realized. A disturbance contribution weight matrix is constructed based on a dominant wave band, pixel-level spectrum stripping is carried out, a plankton purification spectrum is extracted, the spectrum purity and the recognition accuracy are improved, and a spectrum abnormal drift region is precisely recognized in combination with derivative spectrum change and image texture features; a coupling relation between the drift region and background disturbance is further established, a dynamic interference factor map is generated, and identification model parameters are dynamically adjusted, so that the identification stability and classification accuracy of the system in a complex interference environment are improved, and the method is suitable for multi-scene marine ecological monitoring.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention relates to the technical field of roadway surrounding rock danger identification, and discloses a roadway surrounding rock danger identification model construction method, which comprises the steps of collecting multi-modal data, and generating preprocessed data through synchronous calibration and denoising; extracting a seismic wave frequency domain and image texture features, and generating a multi-modal feature matrix; in combination with a geological prior clustering mining abnormal mode, generating a labeled sample data set; generating a danger identification model based on a transfer learning and feature fusion training network; and the edge deployment model performs real-time reasoning, and generates an early warning result through an adaptive algorithm. According to the method, the frequency domain features of the seismic fluctuation signals and the depth texture features of the surrounding rock images are fused, the multi-modal feature matrix is constructed, abnormal mode mining is carried out in combination with geological prior knowledge, and early weak abnormal signals such as hidden fault slippage or asymmetric microfracture extension which are difficult to find by a single monitoring means can be effectively recognized.

The invention discloses a multi-temporal remote sensing image cloud removal method based on global perception selective fusion, which belongs to the technical field of image cloud removal, is used for image cloud removal, and comprises the following steps: inputting a three-temporal cloud remote sensing image into a generative adversarial network framework, and inputting an output result into the generative adversarial network framework again after obtaining the output result; and repeating the cycle until a cloudless image meeting the requirement is obtained. The generative adversarial network framework comprises a generator and a discriminator, the generator comprises an encoder, an advanced feature extraction module and a decoder, the discriminator outputs a probability value to represent an image source, a multi-scale convolutional layer extracts image texture features, an adversarial loss function optimizes network parameters, and adversarial loss comprises pixel-level reconstruction loss and adversarial training loss. According to the method, the problem of space-time discontinuity in multi-temporal data fusion is effectively solved, earth surface detail retention and time sequence change rule capture are realized in cloud coverage area feature reconstruction, and a cloud-removed image with relatively good quality is obtained.

The invention discloses an image video super-resolution enhancement method based on a degradation generative adversarial network, and relates to the field of image processing, and the method comprises the steps: carrying out the image collection and preprocessing; building and training a super-resolution enhancement model; and carrying out super-resolution enhancement on the image based on the degradation generative adversarial network model. According to the method, an image content self-adaptive dynamic degradation kernel generation mechanism is adopted, the degradation process of the image under different equipment and organization structures is truly simulated, a dynamic up-sampling and residual error correction network guided by the degradation kernel is adopted, the detail reduction capability and the structure fidelity of the super-resolution image are remarkably improved, and the super-resolution image quality is improved. The image texture authenticity and key organization density consistency are effectively enhanced, the balance of training games between a generator and a discriminator is realized, and the model stability and convergence quality are improved.

The invention discloses a water heavy metal concentration simulation and prediction method, which comprises the following steps: integrating original monitoring data, hydrodynamic data, total suspended solids, image remote sensing data and human activity data, and generating a multi-source cleaning sequence data packet; executing cross-modal adsorption capacity estimation by using image remote sensing data in the data packet, inferring particle chemical composition and adsorption isotherm parameters from image textures, and generating a capacity feature packet containing an adsorption capacity upper bound; time-varying travel time is calculated based on the hydrodynamic data and the human activity data, causal alignment is performed on the capacity feature packet and the upstream signal, and a travel time alignment feature packet is generated; and in combination with metal fingerprint parameters, applying an adsorption capacity upper bound as a physical constraint on a form distribution constraint head, explicitly decoupling and predicting the form, and generating a prediction result packet. According to the method, the hydrodynamic physical mechanism and the particle adsorption chemical mechanism are deeply coupled, and the prediction precision and the physical consistency of the model under the unsteady state condition are improved.

The invention relates to the technical field of industrial mechanical arm control, in particular to a mechanical arm execution control method for industrial production, which comprises the following steps: collecting parameters in real time; generating a geometric risk index; generating a mechanical risk index; extracting image features; abnormal mechanical arm judgment; determining an adjusting mechanical arm; and generating an adjustment instruction. According to the method, the inclination angle, the plane offset distance, the contact image features, the tightening torque and the force value of the tail end of the mechanical arm are monitored in real time in a multi-dimensional mode, a dual-risk judgment model based on geometric offset and mechanical loading is constructed, image texture fluctuation and posture change are further combined, the continuously abnormal mechanical arm is accurately positioned, and the safety of the mechanical arm is improved. And geometric and mechanical risk indexes are evaluated again after adjustment, so that the aluminum shell positioning and bolt assembling precision is guaranteed, and the problems that the production efficiency is reduced and the product defect rate is increased due to assembling quality fluctuation caused by mechanical arm positioning errors and inaccurate force control are effectively solved.

The invention relates to the technical field of image processing, and discloses a morphology-based image texture feature extraction method, which comprises the following steps: an image preprocessing step: carrying out graying processing on an original image, and carrying out smoothing processing on a grayscale image by using a Gaussian filter; a morphological operation step: selecting structural elements suitable for image texture characteristics, performing expansion and erosion operation on the smoothed image, performing opening operation on the expanded image, and performing closing operation on the eroded image; and an adaptive feature selection step: carrying out local binary pattern feature extraction on the image after morphological processing, adaptively determining a threshold according to statistical distribution of LBP features, and segmenting the image into a foreground and a background. The image texture feature extraction method based on morphology aims at solving the problems that feature extraction is inaccurate, noise interference is likely to happen, and feature combination is complex in an existing image texture feature extraction method.

The invention provides an underwater image recognition method based on deep learning, and relates to the technical field of information, and the method comprises the steps: preprocessing an original image, recognizing image texture interference caused by the density of suspended particles in an interfered region, separating a noise signal, and obtaining a first processed image; performing deep analysis on the multi-scale feature response values and the key feature points in combination with an underwater target recognition task, and judging the category and position information of the target object to obtain a preliminary recognition result; comparing the preliminary identification result with an actual marine environment condition to obtain a matching degree between the identification result and an expected target feature, and if the matching degree is lower than a preset matching threshold value, adjusting a scale weight parameter and a feature point screening threshold value to obtain corrected identification data; and analyzing changes caused by environmental condition fluctuation according to the corrected identification data, generating target identification output, and determining the accurate position and category information of the underwater target through confidence weighted fusion and coordinate precision correction processing of the identification data.

The invention relates to the technical field of multi-unmanned-aerial-vehicle cooperative operation and three-dimensional modeling, in particular to a multi-unmanned-aerial-vehicle cooperative three-dimensional rapid modeling method for a highway accident scene, and the method comprises the steps: generating a three-dimensional grid map of an accident area through the scanning of a millimeter-wave radar by a main control unmanned aerial vehicle; subareas are divided according to a load balancing strategy and are distributed to slave unmanned aerial vehicles, the slave unmanned aerial vehicles traverse grids along a snake-shaped track, laser radar point clouds and five-view-angle images are synchronously collected, data are bound through double time stamps and space coordinates, the point clouds are preprocessed through edge computing nodes, and the point clouds are stored in a database; the master control unmanned aerial vehicle evaluates quality based on density standard deviation and overlapping matching degree and instructs to reacquire, performs high-precision Poisson reconstruction on an accident core area, performs voxelization processing on a peripheral area, maps image textures, optimizes vehicle deformation details, simplifies a model and retains key element precision, and finally performs data processing. License plate coordinates, a scattered object thermodynamic diagram and an emergency lane occupation state are automatically marked, a visual model is generated, and rapid and accurate restoration of an accident scene is realized.

The invention discloses a watermark imperceptible embedding and recovering method based on a deep learning network structure. Firstly, a mask guide watermark embedding scheme is designed, a mask generation module is constructed by using a residual dense feature extraction module and an attention mask generation module, and a watermark is adaptively guided to be embedded into an image texture rich area so as to improve the invisibility of the watermark. And secondly, constructing a watermark decoding network based on comparative learning, and by comparing a loss function, taking the decoding features of the same watermark image under different noise conditions as positive samples and taking the decoding features of different watermark images as negative samples so as to enhance the consistency of the decoding features, thereby improving the robustness of the watermark. The deep learning network structure can improve the robustness of the watermark in a real screen shooting scene, and has a huge application value in copyright protection and traceability tracking.

The invention provides a cross-domain Internet of Things equipment intelligent collaboration method and system based on a semantic knowledge graph. The method comprises the following steps: adjusting the sampling frequency of a cross-domain Internet of Things device according to a level value, and after the sampling frequency is adjusted, obtaining image texture data and sound spectrum data of a monitored device collected by the cross-domain Internet of Things device; performing cross-modal feature space alignment by using a feature level fusion classifier based on the image texture data and the sound spectrum data, and generating a joint semantic feature vector representing the state of the monitored equipment; constructing a semantic knowledge graph based on the joint semantic feature vector; and dynamically optimizing the feature weight of the cross-domain Internet of Things equipment in the semantic knowledge graph according to the level value, so as to generate a cross-domain Internet of Things equipment cooperative control instruction according to the dynamically optimized feature weight. According to the invention, multi-modal data collaborative sensing and dynamic weight optimization driven by vibration signals are realized, and the monitoring precision and control efficiency of the cross-domain equipment state are improved.

The invention relates to the technical field of image processing, and provides an image filling method and device, electronic equipment and a storage medium, and the method comprises the steps: encoding a text prompt word to obtain a text encoding vector, and encoding a masked background image to obtain an image encoding vector; splicing the image coding vector, the position mask and the random noise to obtain a fused noise vector; and step-by-step denoising is carried out on the fused noise vector, a text coding vector and a texture guide vector are added in the denoising process as guide conditions, a filling image is generated, and the texture guide vector is obtained by carrying out splicing, smooth transition and coding processing on texture features of the foreground image and texture features of the background image. According to the method, the filling image which is rich in details, natural in texture and highly fused with the background image can be generated through a step-by-step denoising process and combining the text coding vector and the texture guide vector as double guide conditions.

The invention discloses a bar count visual identification automatic calculation system and method, the system comprises a stereo visual acquisition unit, a dynamic threshold adjustment unit, a geometric feature analysis unit, a data processing module and a man-machine interaction unit, and the method comprises the following steps: S01, image acquisition and preprocessing; s02, adjusting an environment self-adaptive threshold value; s03, bar geometric feature extraction; s04, the number of bars is calculated; s05, outputting a result, displaying the bar count obtained by calculation on an interface of a man-machine interaction unit, and storing a counting result and related data in a database at the same time; the beneficial effects of the invention are that a conventional visual counting system usually only depends on two-dimensional image information, recognition errors are easy to occur under the conditions of bar adhesion, surface reflection and the like, the scheme constructs a stereoscopic vision system through an industrial camera to obtain three-dimensional point cloud data, image textures are enhanced in combination with coding structured light projected by a structured light emitter, and the recognition accuracy is improved. And the spatial position and surface depth information can be obtained.

The invention relates to the technical field of computer vision, in particular to an industrial facility autonomous inspection and intelligent diagnosis method based on multi-modal vision fusion, which comprises the following steps of: acquiring a multi-frame seam image to extract a texture dot matrix, analyzing displacement frequency to evaluate stability, calculating angle difference to identify a deformation structure surface, and performing multi-modal vision fusion. And detecting image and point cloud overlapping, screening abnormal blocks to generate a fusion set, identifying a spectrum hopping mapping image, positioning a boundary region, extracting a frequency analysis path repetition rate, identifying an abnormal focusing position, and generating a diagnosis list. According to the invention, through multi-frame image texture dot matrix displacement tracking, the dynamic abnormal region identification precision is improved, micro deformation is captured through three-dimensional point cloud angle difference, the structural anomaly detection is enhanced, the multi-modal anomaly judgment accuracy is improved, the anomaly characteristics, the inspection path frequency and anomaly aggregation analysis are refined, the high-frequency abnormal region is positioned, and the inspection resource configuration is optimized; risk prevention and control are enhanced, and limitation of equipment surface sensing and abnormal positioning is broken through.

The invention discloses a video noise estimation method, a video noise estimation device and a computer storage medium. The video noise estimation method comprises the following steps: calculating an inter-frame difference result between corresponding pixel points in adjacent frame images; performing image edge calculation on each pixel point in each frame image to obtain an edge detection feature value corresponding to each pixel point in each frame image; determining a static edge point in each frame image according to the edge detection feature value corresponding to each pixel point in each frame image; and determining a video noise estimation value according to an inter-frame difference result between the static edge point in each frame image and each corresponding pixel point in the adjacent frame image. By adopting the method, the influence of image texture details and inter-frame motion on the video noise estimation result can be avoided, so that the video noise estimation result can better reflect the real noise level.

The invention provides a scanning electron microscope image edge detection method, which comprises the following steps: carrying out anisotropic diffusion filtering on a scanning electron microscope image to suppress noise and reserve edges to obtain a filtered image; an original scale gradient and a down-sampling scale gradient of the filtered image are calculated, a plurality of pixel regions in the original scale gradient and the down-sampling scale gradient are fused based on a plurality of adaptive weights to obtain a gradient map, the adaptive weights are determined based on the complexity of the texture of the filtered image, and each pixel region corresponds to one adaptive weight; multi-dimensional features are extracted from the gradient map, the multi-dimensional features are input into a machine learning model for threshold prediction, a continuous pixel-level threshold map is generated based on a predicted threshold, and a gradient threshold included in the continuous pixel-level threshold map is a critical value for distinguishing different types of pixels in the gradient map; and comparing the gradient map with the continuous pixel-level threshold map to obtain a target edge pixel, and generating an edge detection result map based on the target edge pixel.

The invention provides a wireless time-frequency domain signal identification method based on a wavelet time scattering network, and the method comprises the steps: obtaining a wireless signal, carrying out the preprocessing of the wireless signal, obtaining a scattering coefficient based on the preprocessed wireless signal through wavelet scattering transformation, constructing a scattering coefficient graph, constructing a feature vector based on the scattering coefficient graph, and carrying out the recognition of a wireless time-frequency domain signal. And taking the wavelet scattering coefficient spectrogram as a training data set, constructing a wireless time-frequency domain signal recognition model, training the wireless time-frequency domain signal recognition model based on the training data set, and inputting the image texture features of the wavelet scattering coefficient spectrogram of the wireless signal to be recognized into the trained wireless time-frequency domain signal recognition model for recognition to obtain a recognition result. According to the invention, the method can achieve the efficient recognition of wireless signals, and is good in robustness and accuracy.

The invention relates to the technical field of semantic understanding, in particular to a three-dimensional scene semantic understanding method and system based on multi-modal deep learning, and the method comprises the following steps: collecting a point cloud image and a depth map in an automatic driving scene, carrying out the normalization standardization and deletion filling, extracting texture geometric space features, and carrying out the fusion through an attention mechanism; a multi-time-step state vector is introduced to calculate change features, a spatial relation between road participation objects is modeled, a dynamic instance graph structure is constructed, semantic tags are reasoned, and fusion features are compared to generate a three-dimensional scene semantic understanding result. According to the method, the fusion quality is guaranteed through multi-source data normalization standardization, the semantic complementarity is enhanced through collaborative extraction of image texture and point cloud geometric features, the dynamic scene perception ability is improved through state vector modeling, the object interaction semantic relation is described through a spatial relation graph, and the recognition accuracy and consistency are improved through a semantic label reasoning mechanism. And the integrity and robustness of three-dimensional semantic understanding are integrally enhanced.

The invention discloses a remote sensing image generation method and system based on SAR and optical image fusion, and relates to the technical field of image processing, the system comprises an image monitoring module, a data acquisition module, an edge calculation module and a central server, image data and remote sensing data are acquired through a data acquisition module, a data processing model is constructed for image analysis, the quality of a source image is preliminarily analyzed and evaluated through an edge calculation module, image texture features and image color features are sequentially analyzed, image fusion and feature correction are performed through a central server, and a remote sensing image is generated; from the perspective of SAR and optical image fusion, the advantage complementation of the stability of the SAR image under poor atmospheric conditions and the high spatial resolution of the optical image is realized, the generalization ability of the system in different application scenes is improved, the real-time monitoring requirement is met through edge calculation, and the data accuracy and the generation stability of the remote sensing image are ensured.

The invention relates to the technical field of three-dimensional modeling, in particular to a live-action three-dimensional processing method and system based on big data, which are used for extracting height difference mutation and texture features based on remote sensing big data, fusing transparency and direction continuity information, dynamically adjusting a boundary confidence level and realizing weighted reconstruction and three-dimensional expression optimization of a mountain texture layer. According to the method, the elevation grid is established through remote sensing big data, and first-order difference is carried out to identify a height difference mutation region, so that a mountain boundary identification process is changed from dependence on image texture features to quantitative analysis on topographic changes, and boundary state discrimination is carried out in combination with texture layer transparency and gray scale contrast features; according to the method, boundary judgment has continuity and directivity recognition capability, joint evaluation is performed based on a transparency change trend and a boundary direction fitting error, dynamic adjustment of boundary confidence is realized, gray scale reconstruction is performed on a layer boundary region according to a confidence adjustment result, and the accuracy of a layer splicing position and direction is enhanced.

The invention discloses a method for discriminating chicken breast with a white line and wood symbiosis defect based on a hyperspectral microscopic imaging technology, which comprises the following steps: acquiring normal meat, white line meat, wood meat and white line and wood symbiosis chicken breast samples, and cutting the samples into microsections; building a hyperspectral microscopic imaging system, and collecting and correcting microscopic spectral data; extracting a region of interest by using an image processing algorithm; performing defect grade labeling on each region of interest; extracting texture features of the microscopic hyperspectral image of each region of interest; selecting the characteristic wavelength of the spectral data in each region of interest; the obtained spectral data under the characteristic wavelength of each region of interest is fused with image texture features to serve as the input of a model, the defect category and defect grade score of the corresponding region of interest serve as the output of the model, and a Transform model is established and trained; compared with the existing visual and palpation methods, the method provided by the invention has the advantages of high accuracy and high efficiency.