1620 results about "Space resolution" patented technology

The invention relates to an underwater image enhancement method based on wavelet Mama, which aims at the problems of color shift, low contrast ratio and fuzzy details of an underwater image, extracts shallow layer features of an underwater low-quality image, inputs the shallow layer features into a multi-scale coding network, and performs joint enhancement on low-frequency color information and high-frequency detail features by using a wavelet Mama unit. Global semantic features are obtained by combining down-sampling layer-by-layer compression, and global modeling of color offset correction and contrast enhancement is completed; the spatial resolution is recovered through up-sampling, and reconstruction and enhancement of texture details and color information are realized in combination with jump connection and a wavelet Mama unit; and finally, the features are mapped to an image domain and fused with input image residual errors, and an enhanced underwater image with natural color, clear texture and balanced contrast is generated. According to the method, frequency domain-space domain joint feature modeling is realized through the wavelet Mama unit, and the color authenticity, the structural definition and the detail perceptibility of the underwater image are remarkably improved.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention discloses a multi-source remote sensing image zero sample change detection method, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining remote sensing images collected by two or more remote sensing sensors at different time points in the same geographic area, the image types including optical images and radar images; preprocessing each source image, unifying the spatial resolution and the registration precision, and denoising and standardizing the image; according to the method, the cross-modal shared semantic embedding space is constructed and unsupervised comparative learning is introduced, so that the semantic consistency of the multi-source remote sensing image is effectively improved, and the change recognition capability of the model under the zero sample condition is enhanced; and meanwhile, a difference fusion calculation and structure consistency constraint module is adopted, so that the boundary judgment precision of a change region and the overall structure consistency are improved, and the accuracy and stability of a detection result are remarkably improved.

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The invention relates to a sea fog quantitative forecasting method based on a period enhanced Transform, and the method comprises the steps: firstly constructing a Transform model as a forecasting model, enabling the Transform model and a self-attention mechanism to better capture the complex nonlinear relation and long-distance time dependence related to the formation of sea fog, and being superior to a conventional statistical method and an early-stage RNN / LSTM model, a data set for model training is constructed based on a time period enhancement technology and visibility standardization mapping, and the explicit time period enhancement technology enables the model to more accurately learn and forecast seasonal and daily change rules of sea fog; and a sample amplification and training set balancing strategy is executed on the data set, so that the problem of sparse fog samples is effectively solved, and the forecasting capability of the model on key low-visibility events is improved. According to the sea fog quantitative forecasting method based on the period enhanced Transform, the precision, timeliness (forecasting per hour) and spatial resolution of sea fog forecasting of a target area can be improved, and sea fog data characteristics can be effectively processed.

The invention discloses a remote sensing image segmentation method fusing frequency modulation and spatial perception, and the method comprises the steps: obtaining and preprocessing an original remote sensing image, and generating a standardized input image; the image is input into a multi-scale frequency domain enhanced feature extraction network, features are extracted step by step according to a plurality of feature levels, each level realizes frequency adaptive semantic enhancement through frequency domain modulation transformation and spatial feature fusion, and deep feature expression is enhanced through feedforward neural network modeling and residual connection output and cross-level residual fusion introduction; the final multi-scale features are decoded through a decoding module, the spatial resolution is recovered, and a pixel-level segmentation result is generated; and constructing a composite loss function containing classification errors, boundary perception and frequency consistency items, and carrying out optimization training on the network. According to the method, semantic complementarity of a remote sensing image in a frequency domain and a space domain is fully mined, so that segmentation precision and robustness of a ground object target in a complex scene are improved, and the method has good generalization ability and engineering practicability.

The invention discloses an atmospheric pollution remote sensing intelligent monitoring system and method based on multi-modal data, and belongs to the technical field of atmospheric pollution remote sensing monitoring. Comprising a multi-source heterogeneous data acquisition and preprocessing module, a dynamic adaptive modal fusion module, a sudden change scene adaptive module, a three-dimensional pollution inversion enhancement module, a space-time diagram convolution traceability module and a weak signal pollution source collaborative sensing module. According to the method, the concentration contribution values of a plurality of pollution sources are superposed, the three-dimensional distribution of pollutants in space and time is accurately simulated, a visual basis is provided for pollution source positioning and diffusion path analysis, the synchronization of an inversion result and a real-time environment state is ensured through dynamic parameter updating, and the spatial resolution and prediction precision of pollution monitoring are remarkably improved.

The invention discloses a tooth segmentation method of a CBCT (Cone Beam Computed Tomography) image in the technical field of medical image segmentation. The method comprises the following steps: S1, sequentially carrying out standardization and data enhancement processing on a CBCT image data set; s2, inputting the CBCT image preprocessed in the S1 into an encoder part of a network structure, and completing step-by-step extraction of image features to obtain multi-scale features; s3, further fusing the multi-scale feature maps generated by each downsampling layer of the encoder network structure to obtain richer and more effective feature expressions; and S4, performing step-by-step spatial resolution recovery on the encoder features, and finally realizing fine segmentation of the tooth image, the tooth segmentation method of the CBCT image solves the problem that the teeth and surrounding tissue boundary details are fuzzy and the teeth are not clear due to the influence of metal artifacts in CBCT segmentation.

The invention provides a scene semantic occupancy prediction method based on semantic-distance adaptive Gaussian, and relates to the technical field of machine learning. The method comprises the following steps: carrying out sign extraction and point cloud acquisition on a look-around image sequence around a vehicle, inputting a feature map into a double-branch prediction network to obtain semantic information and position information of original sampling points, screening a cloud set of the original sampling points to obtain initialized Gaussian anchor points, and distributing the initialized Gaussian anchor points to levels with different spatial resolutions; according to the hierarchy and semantic information, adjusting a preset basic semantic scale to obtain a correction scale of Gaussian ellipsoids, and randomly adding a rotation vector to each Gaussian ellipsoid to obtain an initial four-dimensional attribute of the Gaussian ellipsoids; and performing multi-round dynamic adjustment on the initial four-dimensional attribute by using a Gaussian attribute iteration model, and finally projecting the initial four-dimensional attribute to a target voxel grid to generate dense 3D semantic occupancy prediction, so that the prediction result is more accurate. And intelligent dynamic allocation of computing resources and efficient and real-time three-dimensional scene understanding capability are realized.

The invention relates to the technical field of vibration monitoring, in particular to a self-powered transmission line fitting aeolian vibration damage diagnosis system and method, and the system comprises a sensing collection module, a signal decoupling module, a damage identification module, a damage association module and a risk assessment module. According to the method, stress wave velocity and acceleration data are synchronously collected, time alignment is implemented, feature coupling precision is enhanced, wave crest offset and energy density are respectively extracted by using moving average filtering and wavelet transform, effective data segments are dynamically screened, and environmental noise interference is suppressed. A stress wave propagation change rate is quantified based on a path attenuation model, a continuous energy abnormal node is matched to realize damage positioning, a breeze response abnormal region is identified by combining vibration direction change and a signal envelope offset degree, multi-dimensional features are coded and subjected to risk judgment through a neural network, a damage positioning and risk assessment closed-loop framework is formed, and the risk assessment accuracy is improved. And the spatial resolution and evaluation precision of aeolian vibration damage identification under complex working conditions are significantly improved.

The invention provides a medical image segmentation method and system based on spatial detail enhanced vision, and relates to the technical field of image processing. The method comprises the following steps: performing initial feature mapping on an input and preprocessed medical image to obtain an embedded feature map; the embedded feature map is input into an encoder for feature extraction, and multi-scale features are obtained; the encoder comprises a plurality of encoding stages, and the number of channels is doubled and the spatial resolution is halved through down-sampling operation between the encoding stages; the multi-scale features are subjected to up-sampling and spatial enhancement reconstruction step by step through a decoder, and a high-precision segmentation result is generated; wherein the decoder comprises a plurality of decoding stages, the decoding stages correspond to the encoding stages, and feature fusion is carried out between the corresponding stages of the encoder and the decoder through jump connection. According to the method, the boundary description precision and the segmentation robustness of the low-contrast image can be improved without increasing the linear complexity, and the method is suitable for medical image segmentation scenes of skin lesions, gastrointestinal polyps and the like.

The invention relates to an optical cable monitoring and data analysis method and device, equipment and a medium. The method comprises the following steps: acquiring current state data of an optical fiber link, and dynamically generating adaptive pulse coding parameters and matched filter parameters based on the data by using a deep reinforcement learning agent, so as to generate and inject coded pulses into the optical fiber link; and then receiving an echo signal, performing matched filtering processing on the signal by using the generated matched filter parameter to obtain a filtering output signal, further calculating a performance index vector and a reward signal, and finally updating a strategy network of the intelligent agent by using the reward signal, thereby realizing continuous optimization of pulse coding and matched filtering parameters. Therefore, the system can automatically adapt to the dynamic change of an optical fiber link, the spatial resolution, the dynamic range and the signal-to-noise ratio are effectively improved, and the adaptive capability and the robustness of the system are enhanced at the same time.

The invention discloses a vertical rail scanning remote sensing satellite natural multi-coverage image resolution improving method and system, and relates to the field of remote sensing image processing. The method solves the problems that due to the fact that the distance between an existing vertical rail scanning imaging sensor and a ground target is increased, image pixels cover a wider ground range, the actually-measured spatial resolution is reduced, and fine detection and recognition of ships, aircrafts and the ground target cannot be supported. SIFT feature point extraction is carried out, and feature points extracted from different images are matched by using a Euclidean nearest distance matching strategy; taking one registered image as a reference, and constructing an initial high-resolution image through up-sampling; establishing an imaging degradation model from a high-resolution image to a low-resolution observation image; calculating a residual image of the simulation image and the real observation image; and back-projecting the residual error back to the high-resolution image space, and updating high-resolution image estimation.

The invention relates to an ultrasonic phased array full-focusing imaging method for acoustic wave multi-path propagation compensation of a layered composite material, which is characterized by comprising the following steps of: arranging an ultrasonic phased array probe with N array elements on a composite material piece to be detected, obtaining complete echo data by taking each array element as a transmitting array element and taking all array elements as receiving array elements in sequence in a full-matrix acquisition mode; a two-dimensional pixel grid is established in an imaging area, a Monte-Carlo perturbation mechanism used for representing the acoustic wave propagation uncertainty in the layered composite material is introduced into a propagation model for each emission array element-receiving array element-pixel point combination, and the acoustic beam emission direction, the propagation path and the equivalent acoustic velocity are subjected to random perturbation, so that the acoustic wave propagation uncertainty in the layered composite material can be represented. Generating a plurality of equivalent sound wave propagation paths, calculating two-way propagation time of each path, and performing weighted statistics on energy contributions of different paths according to array element sound beam directivity to obtain equivalent propagation time delays of pixel points; and performing delay correction on a full-matrix echo signal by using the equivalent propagation time delay, and performing coherent superposition on signals of all transmitting-receiving array element combinations to realize dynamic focusing of a full-pixel grid, and finally reconstructing an ultrasonic full-focusing imaging image with high resolution and high signal-to-noise ratio. The time delay error caused by propagation path deviation and sound velocity non-uniformity in the layered composite material can be effectively compensated, the focusing precision and spatial resolution of full-focusing imaging are improved, meanwhile, an existing TFM imaging frame does not need to be changed, and the method has good engineering implementability and popularization and application value.

The invention discloses a flow field velocity measurement and visualization device and method, and relates to the technical field of flow field display measurement, and the device comprises a two-color laser emission system, a surface dielectric barrier discharge system, a Cassegrain light path shaping system, a fluorescence signal detection system and a data processing system. Wherein the two-color laser emission system is used for generating femtosecond pulse laser and first laser, and the femtosecond pulse laser and the first laser are converged and collimated by the Cassegrain light path shaping system and then are focused to a detection area to cooperatively excite nitrogen molecules; the surface dielectric barrier discharge system forms a plasma environment in the detection area; the fluorescence signal detection system is used for collecting nitrogen molecule fluorescence signals in the detection area; and the data processing system is used for controlling a time sequence, realizing visual representation of a flow field structure in a detection area and realizing speed measurement of a flow field in the detection area, and the device and the method can realize synchronous acquisition of a speed vector and flow field structure parameters near a wall surface under a millimeter-level spatial resolution and a microsecond-level time scale.

The invention discloses an area intelligent identification alarm system for tower crane hoisting, which relates to the technical field of alarm systems and comprises a front-end data acquisition module, an acquired data processing module, a wireless network communication module, a hoisting safety analysis module and a safety alarm execution module. The visual detector, the detection sensor and the environment sensor form a multi-element acquisition module, hoisting parameters and environment information can be synchronously acquired in real time on the hoisting site of the tower crane, the comprehensiveness of information acquisition is ensured, and the problem that the traditional configuration is single in acquisition element and the cost is low is solved through the redundancy design of multiple acquisition elements. The method solves the problem that the existing tower crane is easy to fail, realizes real-time intelligent identification and graded early warning of potential safety hazards in a tower crane hoisting operation area, has the characteristics of high dynamic adaptability, high spatial resolution, intelligent response, high deployability and the like, and provides scientific and efficient technical support for safety management of tower crane operation.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention belongs to the technical field of ocean observation, and discloses an underwater sound velocity distribution super-resolution construction method based on multi-modal data fusion. According to the method, a sound velocity distribution forecasting model is constructed, the model carries out temperature preliminary reconstruction on original space area temperature data through a Unet neural network, and sea surface temperature features are extracted through a Mama neural network; correcting the preliminarily reconstructed temperature data through sea surface temperature features to obtain high-spatial-resolution temperature data; and finally, obtaining high-spatial-resolution sound velocity distribution data through a sound velocity distribution calculation module. According to the method, the underwater temperature and the sea surface temperature are subjected to data fusion, so that the sound velocity distribution of the whole space area is quickly and accurately forecasted under the condition of original low-spatial-resolution data, and the problem of insufficient spatial resolution of ocean sound velocity distribution estimation is solved.

The invention discloses a multispectral and hyperspectral imaging proportion balance optimization method based on satellite remote sensing, and relates to the technical field of balance optimization, and the method comprises the following steps: collecting a first image data set which comprises a low-resolution multispectral image and a hyperspectral image; preprocessing the first image data set, screening out a low-resolution multispectral image from the preprocessed images, improving the spatial resolution of the multispectral image by adopting a super-resolution reconstruction algorithm, and generating a reconstructed multispectral image; fusing the reconstructed multispectral image and the preprocessed hyperspectral image, and performing optimization processing on a fusion result to generate an image with an optimal resolution; and extracting second image data based on the optimal resolution image, and establishing a proportion balance optimization model. According to the method, the data processing time and the imaging cost are accurately calculated, and the image fusion quality evaluation is combined, so that the optimal allocation of the imaging ratio under limited resources is realized.

The invention relates to the technical field of geological engineering, and discloses a geological safety risk dynamic assessment method based on multi-source data fusion, which comprises the following specific steps: step 1, collecting and standardizing multi-source geological data; 2, performing semantic fusion and conflict resolution on the geologic features; 3, constructing a dynamic risk assessment model; 4, risk situation real-time updating and early warning are carried out; the satellite remote sensing system in the first step adopts the synthetic aperture radar interference measurement technology, the spatial resolution is better than 3 meters, the revisit period is shorter than 7 days, and the earth surface deformation monitoring precision reaches the millimeter level. Through standardized processing and semantic fusion of the multi-source geological data, the problem of heterogeneous data integration is effectively solved, the data basic quality of risk assessment is improved, a space-time coupling neural network model is adopted, nonlinear features and space-time correlation characteristics of geological risk evolution are accurately captured, and prediction precision and timeliness are improved.

The invention belongs to the field of electric power, and discloses a downscaling method, device and equipment for a rainfall forecast field, and a storage medium, and the method comprises the steps: obtaining meteorological data, inputting the meteorological data into a fortune meteorological model, and enabling the fortune meteorological model to generate an initial forecast field based on the meteorological data; cutting the initial forecasting field to obtain an initial rainfall forecasting field of the target area, and inputting the initial rainfall forecasting field into a pre-trained downscaling model which comprises a shallow feature extraction module, a deep feature extraction module and a reconstruction module, a shallow feature extraction module extracts initial features from the initial rainfall forecast field based on a convolutional layer; the deep feature extraction module captures the spatial dependency relationship of the precipitation field from the shallow feature map through the residual block; the reconstruction module converts the deep feature map into a high-resolution downscaling precipitation field based on a pixel rearrangement operation. The spatial resolution and accuracy of rainfall forecast are improved through a downscaling model based on SwinIR.

The invention discloses a mountainous area wind field downscaling optimization method based on high-precision simulation, and relates to the technical field of meteorological numerical simulation, and the method comprises the steps: obtaining the terrain elevation data and meteorological monitoring data of a target mountainous area, recognizing slope abrupt change points, constructing a spiral sampling path, calculating a slope change value, determining the distance between sampling points, and generating target sampling data; performing orthogonal decomposition on the sampled data to obtain a frequency component, calculating a surface fluctuation coefficient, and constructing a boundary disturbance equation to obtain surface stress distribution; calculating an airflow motion state based on surface stress distribution, solving a vorticity equation to obtain vorticity characteristics of a leeward area, and calculating airflow motion correction parameters; and carrying out downscaling iterative operation on the corrected parameters and the meteorological monitoring data to generate target area wind field data with hectometer-magnitude spatial resolution. The simulation precision of the local wind field under the complex terrain condition is improved.

The present invention relates to a high temperature and drought composite disaster monitoring and early-warning method and system, belonging to the technical fields of disaster risk assessment and early warning. Internal correlation features and abnormality information of high temperature and drought events are input into a model, multi-time-space scale features of the high temperature and drought events can be identified accurately, high event identification accuracy and space resolution are achieved, the progress can be predicted progressively, the drought and high temperature threshold change can be monitored closely, and fine forecasting and early warning can be performed in different periods, regions and intensities, thereby ensuring that indicators are in the same time scale, avoiding the complication of the high temperature and drought process caused by frequent time and space discontinuities of the indicators in a single point or small region, and ensuring the suitability for any periods of the process.

The invention provides a multi-scale morphological gradient and NSST-PCNN image fusion method, and relates to the technical field of multi-source remote sensing image fusion. The method comprises the following steps: performing Gaussian curvature filtering decomposition and LEE filtering decomposition on an SAR image to obtain SAR decomposition features; decomposing the optical image by using non-subsampled shearlet transform (NSST) to obtain an NSST coefficient of the optical image; performing feature extraction on the SAR image after LEE filtering through various structural elements to obtain a multi-scale morphological gradient feature map; and inputting the SAR decomposition features and the NSST coefficient of the optical image into a pulse coupled neural network (PCNN) to obtain a preliminary fusion image, and reconstructing the preliminary fusion image through NSST inverse transformation to obtain a final fusion image. The fused image retains the spectral characteristics of the original optical image, enhances the spatial resolution and edge details, and is suitable for scenes needing to retain high-precision edges, such as road slope deformation monitoring.

The invention belongs to the technical field of image processing, and particularly relates to a mixed U-Net brain tumor segmentation method and system based on quantum attention, and the method comprises a data preprocessing module which is used for obtaining and preprocessing a medical image; the image segmentation module is used for segmenting an image through a trained QHAU-Net segmentation model, and the QHAU-Net segmentation model comprises an encoder used for extracting multi-level features of the input image; the bottleneck layer is used for receiving the semantic features of the highest dimension output by the encoder and generating enhanced features modulated by quantum through a quantum multi-head attention module; the decoder is used for recovering the spatial resolution of the feature map and fusing the features through jump connection; the quantum boundary enhancer is used for performing quantum enhancement optimization on the output features of the decoder and outputting the output features; and the result output module is used for outputting and visualizing the segmentation result graph. The problem of low boundary medical image segmentation precision in medical image processing is solved.

The invention provides a method for rapidly determining the center position of an electron cloud cluster of a reading circuit in an XS anode detector, and aims to solve the problems that the existing method for calculating the center position of the electron cloud cluster of the reading circuit in the XS anode detector cannot realize higher spatial resolution, or easily causes image distortion, and is inconvenient to use. The software processing speed is slow, the FPGA is not easy to implement, or the calculation error at the edge strip is relatively large. The method for rapidly determining the center position of the electronic cloud cluster of the reading circuit in the XS anode detector is realized by adopting an FPGA (Field Programmable Gate Array), the peak value of effective data of each channel is obtained through single-channel processing, and then a corresponding drop point distribution index code is generated through table look-up and cross-channel processing; and then adaptive weight selection is carried out according to the drop point distribution index code, so that the weight accuracy of weighted average operation is ensured, key support is provided for rapid and accurate calculation of the center position of the electronic cloud cluster, and high efficiency and accuracy of the whole data processing flow are ensured.

The invention discloses a farmland soil moisture content real-time surveying system and method based on unmanned aerial vehicle multispectral imaging. The method comprises the steps that multispectral sensor parameters are configured, and an unmanned aerial vehicle flight scanning path is planned; a space coordinate system and time synchronization mechanism is established, and high-precision positioning and time synchronization are achieved; collecting and calibrating visible light, near-infrared and short-wave infrared original image data to form a spectral signal set; calculating an image signal-to-noise ratio, screening a low-quality image, and executing noise suppression; extracting timestamp information from the suppressed image, performing spatial positioning, and establishing a spatial corresponding relation database; calculating time synchronization precision and performing image correction; evaluating the spatial resolution according to the distortion coefficient; calculating a comprehensive quality parameter, comparing the comprehensive quality parameter with a threshold value, and obtaining a soil moisture content distribution parameter when a condition is met; a farmland moisture state map is generated, partition marking is carried out according to a multi-level standard, a monitoring result is output, and high-precision and real-time monitoring of the farmland soil moisture content is achieved.

The invention discloses a corn disease recognition method based on an improved generative adversarial network. The corn disease recognition method comprises the steps that S1, an initial low-resolution image of a corn field is collected and obtained through an unmanned aerial vehicle; s2, performing super-resolution reconstruction on the initial low-resolution image by using an improved generative adversarial network model; the model construction comprises the following steps: S2.1, constructing a shallow feature extraction layer; s2.2, constructing a deep feature extraction network based on a plurality of RRDB nested residual dense blocks; s2.3, constructing an attention module based on a space and channel dual attention mechanism; s2.4, a multi-scale texture enhancement module is constructed through multi-scale convolution and smooth branches; s2.5, constructing a global residual connection layer; s2.6, constructing an adaptive hybrid up-sampling module based on transposed convolution and stable up-sampling; s2.7, performing mapping output on the features after up-sampling; and S3, carrying out disease prediction on the high-resolution reconstructed image. According to the method, details such as spatial resolution and texture of the unmanned aerial vehicle high-altitude flight remote sensing image are improved, and then the corn disease monitoring precision is improved.

The invention discloses a total primary productivity estimation method and system based on multi-model coupling deep learning, and the method comprises the steps: obtaining the multi-source data of meteorological data, remote sensing images, latent heat flux, sensible heat flux and solar radiation, carrying out the quality control, missing value processing and nearest neighbor interpolation of different data sources, and carrying out the prediction of the total primary productivity. Unifying to a target spatial resolution and a time resolution; in a light energy utilization rate (LUE) model family, a solar radiation phase factor is introduced into a photosynthetically active radiation absorption ratio (FPAR) to obtain a phase modulation type FPAR, drought duration is introduced into a water stress function f (W) to obtain an exponential decay type f (W), and a GPP time sequence of a plurality of improved mechanism models is calculated according to the exponential decay type f (W); extracting spatial texture features from a remote sensing image stack by using a convolutional neural network (CNN), and performing cross-modal fusion on the spatial features and the GPP estimated by the plurality of improved mechanism models in a gating mode to form fusion representation; carrying out learning and collaborative optimization on the fusion representation of the GPP and CNN spatial features estimated by the plurality of improved mechanism models by adopting a gradient lifting tree model; and high-precision estimation of the GPP is realized through multi-model collaborative optimization. The method aims at solving the problem that a traditional light energy utilization rate model is insufficient in response under the extreme environment conditions of drought and intense radiation, the adaptability limitation of a traditional single model under the complex environment is broken through, and therefore high-precision GPP estimation under the complex environment is achieved.