58 results about "Thermal infrared remote sensing" patented technology

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention discloses a method for evaluating the influence of black land ecological barrier pattern evolution on agricultural climate and yield, and relates to the technical field of agricultural ecological environment monitoring and remote sensing model coupling. Comprising the following steps: S1, acquiring a high-resolution satellite image, microwave remote sensing earth surface humidity data, optical remote sensing vegetation coverage data and thermal infrared remote sensing earth surface temperature data of a target area; according to the method for evaluating the influence of the black land ecological barrier pattern evolution on the agricultural climate and the yield, the problem of lack of ecological-climate-agricultural system collaborative modeling in the traditional technology is solved. A high-resolution satellite image is used for analyzing a network topology structure of the protection forest, microwave and thermal infrared remote sensing are fused to invert'water, soil and gas generation 'multi-element parameters, and dynamic quantitative evaluation of ecological barrier spatial heterogeneity evolution on local climate and crop growth is realized in combination with a dynamic weight distribution and two-way nesting technology.

The invention discloses a geothermal abnormal point identification system based on remote sensing detection. The system comprises a data layer which collects thermal infrared remote sensing data, multispectral / hyperspectral remote sensing data, radar data, geological and topographic data, meteorological data and geophysical prospecting data; the processing layer is used for extracting key features, capturing dynamic changes of geothermal anomalies, distinguishing continuous geothermal anomalies and transient events, combining hyperspectral data and magnetotelluric sounding data, and constructing'earth surface-deep part 'associated features; the analysis layer is used for learning a diffusion rule of geothermal anomalies along a fault zone through a graph propagation model, taking a geothermal field heat conduction equation as a loss function constraint by utilizing a physical information neural network, jointly training a temperature field prediction model, constructing a deep three-dimensional geological structure based on gravity and geophysical prospecting data, and constructing a geologic structure model; constructing a geothermal anomaly detection model through the features provided by the processing layer to recognize geothermal anomaly points; and the output layer is used for generating a geothermal anomaly probability graph.

The invention provides a regional soil component detection method based on a remote sensing image, and belongs to the technical field of soil detection. Aiming at the problems that a traditional detection method depends on a single optical remote sensing image, is interfered by weather and vegetation, and is low in detection precision, low in efficiency, unguaranteed in data credibility and the like, radar and thermal infrared remote sensing data are introduced to be fused with an optical image, features are extracted through principal component analysis, a generative adversarial network is adopted to carry out hyperspectral image super-resolution reconstruction, and a high-resolution hyperspectral image is obtained. A genetic algorithm is utilized to realize adaptive wave band selection, a real-time atmospheric correction model is constructed, a recurrent neural network and a spatial convolutional neural network are utilized to mine spatio-temporal context information, a quantum computing acceleration algorithm is introduced, block chain management data is utilized, and unmanned aerial vehicle cooperative operation is combined. According to the method, the detection precision, efficiency and data credibility are remarkably improved, and the regional soil component detection process is effectively optimized.

The invention discloses a hydrothermal vein type gold deposit alteration zoning remote sensing identification method, and the method specifically comprises the following steps: S1, carrying out multi-platform remote sensing cooperative interpretation, and obtaining the initial alteration zoning information of a regional scale. According to the hydrothermal vein type gold deposit alteration zoning remote sensing identification method, through organic combination of multi-platform remote sensing cooperative interpretation and unmanned aerial vehicle fine mineral mapping, cross-scale detection from regional scale preliminary alteration zoning identification to target region scale alteration lithofacies accurate delineation is realized; the problems that a traditional remote sensing method is single in scale and insufficient in precision in alteration zoning recognition are effectively solved. According to the method, the complementation advantages of multi-temporal optical remote sensing, synthetic aperture radar and thermal infrared remote sensing data are utilized, accurate recognition of key altered minerals is achieved in combination with hyperspectral data of the unmanned aerial vehicle, and the certainty of alteration zoning boundaries and the accuracy of lithofacies classification are remarkably improved.

The invention discloses a multi-source information fusion and intelligent interpretation technology in hot dry rock fracture identification, and the technology specifically comprises the following steps: 1, data collection: collecting multi-source data including seismic reflected waves, logging lithologic parameters, electromagnetic abnormal data and thermal infrared remote sensing images, and building a hot dry rock reservoir physical property parameter database, and 2, data preprocessing: carrying out denoising and offset correction on seismic data, normalizing logging data, inverting resistivity distribution of electromagnetic data, and extracting a temperature abnormal region of a thermal infrared image. The invention relates to the technical field of geothermal development. According to the multi-source information fusion and intelligent interpretation technology in hot dry rock fracture identification, by integrating multi-modal information such as geology, geophysics, remote sensing and drilling data and combining a feature level and decision level fusion strategy, a sensing blind area of a single sensor is effectively made up, the data are preprocessed, and the recognition accuracy is improved. And the capability of effectively inhibiting complex environmental factors such as seismic wave noise and electromagnetic interference is improved.

The invention provides a soybean growth monitoring method based on satellite remote sensing, and relates to the technical field of crop growth monitoring, and the method specifically comprises the steps: setting two monitoring nodes according to a soybean growth period, and collecting multispectral and thermal infrared remote sensing images and normal growth leaf area density and stem diameter sample data; a soybean area and a non-soybean area are distinguished by using a vegetation index threshold, cloud / shadow pixels are eliminated by using a multiband combination-threshold method, effective pixels are screened, and two types of remote sensing data are matched. Extracting leaf area density, canopy temperature and collaborative inversion stem diameter from the effective data; calculating the relative growth rate of the two types of indexes based on samples, and constructing a collaborative growth reference equation and a residual standard deviation; and substituting the growth rate of the planting area into an equation to obtain a theoretical value, calculating a deviation value, and judging the soybean growth state by combining the comparison between the deviation and a normal fluctuation interval and the positive and negative characteristics of the growth rate.

The invention provides a transformer substation noise traceability and diffusion simulation method fusing multi-source remote sensing data. Belongs to the technical field of power environment protection and remote sensing science. The method comprises the following steps: acquiring thermal infrared remote sensing image data of a transformer substation, after preprocessing, finding out a temperature anomaly region of main noise source equipment by using a temperature anomaly recognition algorithm, determining the position of the main noise source equipment in combination with equipment layout information, and generating position data of the noise source equipment; a laser radar is used for scanning a transformer substation and surrounding areas to obtain high-precision point cloud data, the high-precision point cloud data are processed to generate high-precision digital elevation model data, and a transformer substation three-dimensional scene model containing detailed topographic features and noise source positions is constructed in combination with noise source equipment position data. Temperature abnormity of main noise source equipment is identified by means of thermal infrared images, noise sources of specific equipment such as a main transformer and an electric reactor can be accurately positioned, equipment-level traceability is realized, the situation that traceability is difficult in a traditional method is changed, and a clear direction is provided for targeted noise treatment.

The invention provides a simulation design method of an urban ecological corridor, which comprises the following steps: S1, collecting a multi-temporal thermal infrared remote sensing image of a city to be simulated and designed and preprocessing to obtain a core heat island node; s2, heat resistance surface construction is carried out based on different surface types, a heat transfer resistance surface is obtained, and the different surface types comprise a digital elevation model, an impervious surface index, a normalized vegetation index, a normalized building index, a normalized water body index and a gradient; s3, taking the core heat island node as a network end point, searching a communication path with the lowest resistance on a heat transmission resistance surface, and obtaining an urban heat island corridor; and S4, on the basis of the urban land utilization data, ecological lands of forests, wetlands, large parks and water bodies serve as candidate ecological source lands, ecological connection paths are constructed from the angle of ecological connectivity by adopting the S2 and the S3, and the urban ecological corridor is obtained.

The invention relates to the technical field of image segmentation, in particular to a coal mine area ground fracture identification method and device. The method comprises the following steps: acquiring a visible light remote sensing image and a thermal infrared remote sensing image of a target coal mine area; constructing a ground fracture data set based on the visible light remote sensing image and the thermal infrared remote sensing image; a ground fracture recognition model is constructed based on the neural network, the ground fracture recognition model comprises a double-branch encoder, a feature extraction module and a decoder, and the double-branch encoder comprises a feature fusion module; iteratively training the ground fracture recognition model according to the ground fracture data set; and performing ground fracture identification on the visible light remote sensing image to be detected and the thermal infrared remote sensing image to be detected of the target coal mine area by using the trained ground fracture identification model to obtain a ground fracture identification result map. According to the method and the device, the visible light and thermal infrared bimodal data are fused, so that accurate identification of the ground fissures is realized in a complex scene of a coal mine area.

The application relates to the technical field of image segmentation, in particular to a coal mine area ground fissure identification method and device.The method comprises the following steps: acquiring a visible light remote sensing image and a thermal infrared remote sensing image of a target coal mine area; constructing a ground fissure data set based on the visible light remote sensing image and the thermal infrared remote sensing image; constructing a ground fissure identification model based on a neural network, wherein the ground fissure identification model comprises a double-branch encoder, a feature extraction module and a decoder, and the double-branch encoder comprises a feature fusion module; iteratively training the ground fissure identification model according to the ground fissure data set; and identifying ground fissures in the to-be-detected visible light remote sensing image and the to-be-detected thermal infrared remote sensing image of the target coal mine area by using the trained ground fissure identification model to obtain a ground fissure identification result map.The application realizes accurate identification of ground fissures in a complex scene of a coal mine area by fusing visible light and thermal infrared dual-mode data.

The present application relates to a multi-source remote sensing driven precision irrigation and fertilization method and an integrated mobile operation system, aiming at the problems of large environmental heterogeneity influence, poor data consistency, delayed feedback and low execution adaptability of multi-source remote sensing in farmland irrigation and fertilization strategy making. The method establishes a multi-source spatio-temporal alignment observation data set through physical preprocessing of satellite, unmanned aerial vehicle and thermal infrared remote sensing images and environmental label data, realizes unified mapping from physical characteristics to crop stress semantic space by using a lightweight semantic encoder, carries out abnormal source elimination combined with semantic consistency measurement and mismatch detection, and realizes adaptive and closed-loop decision making according to crop growth stages and stress types by integrating historical feedback and dynamic correction of model parameters. The present application improves the accuracy, environmental robustness and response timeliness of farmland irrigation and fertilization strategy, and is conducive to the optimization of intelligent operation.

The invention provides a remote sensing and crop growth model-based field corn irrigation water demand calculation method. The method comprises the following steps of: constructing a microwave scattering model based on microwave remote sensing data, and constructing a thermal inertia model based on thermal infrared remote sensing data; learning a non-linear mapping relation between remote sensing data and ground actually-measured multi-layer soil moisture through a deep neural network, optimizing key parameters of a multi-source fusion model, and establishing calibration parameter values and soil moisture content parameter values of the multi-source fusion model under different hierarchies; on the basis of the difference of different soil layer moisture demands in different growth stages of the corn, the water amount needing irrigation is calculated in combination with a multi-layer soil moisture calculation result; according to the possible rainfall in the preset time period, combining the area of the region to obtain predicted rainfall; according to the intelligent irrigation decision-making system for the field corn, the remote sensing technology, the crop physiological model and meteorological prediction are organically combined, and a complete and practical intelligent irrigation decision-making system for the field corn is formed.

The utility model discloses a dam leakage identification test device based on different temperature differences, which relates to the technical field of dam leakage identification tests and comprises a supporting table, a model groove is mounted at the top of the supporting table, and a heat preservation irradiation assembly is mounted at the top of the model groove through a hinge. The heat preservation irradiation assembly is used for providing heat for generating temperature difference, the heat preservation irradiation assembly comprises a protective shell, a through groove and a heating irradiation lamp, the protective shell is installed at the top of the model groove through a hinge, the through groove is formed in the front face of the protective shell, and the heating irradiation lamp is installed on the top slope of the protective shell in a penetrating mode. According to the utility model, the heating irradiation lamp irradiates the dam surface after being electrified, so that the temperature of the dam surface rises, and different temperature differences are formed between the temperature of the dam surface and the water temperature of the leakage water outlet, so that the effect of different temperature differences on dam leakage identification of the test device is researched, and the minimum temperature difference index is determined; and the support is provided for the thermal infrared remote sensing technology embankment patrol risk inspection.

The application provides a near real-time fire point dynamic monitoring method, device, equipment and medium, relates to the technical field of infrared remote sensing monitoring, and the method comprises the following steps: acquiring thermal infrared remote sensing data and land cover product of a forest and grass monitoring area, and performing data preprocessing; the thermal infrared remote sensing data is segmented to obtain tile data, and cloud extraction is performed based on the tile data; the extracted cloud is used for mask of the thermal infrared remote sensing data, and the medium infrared band and the far infrared band are segmented to obtain an isothermal graph spot layer; the isothermal graph spot layer is masked through water body and building area corresponding to the land cover product; the masked isothermal graph spot is comprehensively identified based on the statistical characteristics of the isothermal graph spot and the comparison characteristics of the isothermal graph spot and adjacent background graph spots, and a fire point detection result in the forest and grass monitoring area is obtained. The application alleviates the technical problems of pixel-by-pixel identification, large amount of calculation, low efficiency and limited precision in the prior art.

The present invention discloses an angle effect correction method for geostationary meteorological satellite thermal infrared surface temperature data, which belongs to the field of thermal infrared remote sensing technology. The present invention first obtains geostationary satellite thermal infrared remote sensing surface temperature data of the target area and converts it to the target coordinate system; then calculates the angle parameters of each pixel based on the satellite sub-satellite data; performs system error elimination processing on the nighttime surface temperature data of two satellites screened based on the satellite zenith angle; then uses the nighttime surface temperature data to simplify the kernel-driven model and solve the emissivity kernel parameters; solves the solar nuclear coefficient and hotspot height based on the daytime surface temperature data at multiple consecutive moments, and combines the solved parameters to obtain vertical observations of each pixel. The present invention can quickly and accurately solve the parameters of the kernel-driven model and then accurately solve the vertically observed surface temperature, overcoming the defects of the traditional method such as large data demand, low calculation efficiency, and low accuracy, and can serve a large number of thermal infrared remote sensing surface temperature application scenarios.

The invention discloses a granite identification method and device based on SDGSAT-1 satellite thermal infrared remote sensing, and the method comprises the steps: carrying out the preprocessing of obtained SDG SAT-1 satellite thermal infrared remote sensing data, and obtaining a remote sensing image of a research region; establishing a corresponding relation between mineral spectral curve characteristics of the research area and a satellite sensor spectral domain; according to the remote sensing image and the mineral spectrum curve in the corresponding relation, a GTI granite index graph is extracted through a GTI granite index formula, and according to the GTI granite index graph, a granite distribution result graph meeting the preset precision is extracted by combining an image segmentation and edge detection method.

The application discloses a kind of dam leakage passage digital twin systems and diagnostic methods based on flow-heat coupling inversion reconstruction, the diagnostic method includes: obtaining the time series thermal infrared remote sensing observation data of dam to be measured, environmental driving data and digital twin dam model;Under the assumption of no leakage, seepage-heat coupling simulation is carried out, and the benchmark temperature field that is continuous in space-time is generated;Based on the difference between time series thermal infrared remote sensing observation data and benchmark temperature field, extract thermal infrared anomaly index;Seepage-heat coupling inversion model containing physical constraint and structured constraint is constructed, and three-dimensional leakage parameter field inside dam is obtained by minimizing the difference between observation and simulation inversion;It is written back to digital twin dam model to carry out seepage stability calculation, and the leakage risk classification early warning result of dam is obtained.The application effectively solves the problem of ill-conditioned and environmental interference of thermal infrared inversion, realizes the structured reconstruction and risk quantitative evaluation of dam leakage passage.

This invention discloses an end-to-end land surface temperature retrieval method based on a multi-source heterogeneous data fusion network, comprising: acquiring visible light, near-infrared, and thermal infrared remote sensing images and land surface temperature label data; constructing a dataset by combining NCEP water vapor data with spatiotemporal interpolation resolution matching; building a cross-modal retrieval model that integrates normalization processing, differential feature extraction, a dual-stream encoder, and a U-Net decoder; completing model training and testing, verifying spatial generalization ability using non-overlapping independent region data, and retraining with parameter adjustments and sample additions if the standard is not met; introducing field-observed temperature as a physical constraint to calculate residuals, freezing the main network parameters, and fine-tuning only the linear layer at the end of the decoder to optimize the model; inputting multi-source data of the area to be inverted into the fine-tuned model to achieve high-resolution and high-precision land surface temperature retrieval. This invention achieves automated retrieval without atmospheric parameters, significantly improving the absolute accuracy and spatial detail of the retrieval results, and is suitable for large-scale monitoring of complex surfaces.

The application discloses a method for estimating power generation of a nuclear power plant by using a space-based thermal infrared remote sensing warm water discharge area map, and comprises the following steps: obtaining a thermal infrared image of a thermal imager of a target power plant adjacent water area, performing radiation calibration, calculating the brightness temperature of each wave band, and inversely calculating the water surface temperature field by using a split window algorithm; constructing a water body background temperature field and calculating a temperature rise field; performing threshold segmentation according to the temperature rise field, introducing multi-wave band consistency constraints and connected form prior constraints, generating a warm water discharge area map, and extracting a characteristic parameter set of the warm water discharge; combining flow field information and a water body heat exchange model, calculating the theoretical heat discharge power of the warm water discharge according to the characteristic parameter set, further inversely calculating the actual heat discharge power; establishing a mapping relationship between the actual heat discharge power and the unit electric power, inversely calculating the unit electric power, and performing time integration on the obtained unit electric power in a given time interval to obtain the power generation. The application can realize continuous, wide-area and traceable power generation estimation by using a space-based thermal imager.

The application discloses a kind of wheat scab monitoring methods based on satellite remote sensing, belong to remote sensing monitoring technical field, first obtain the remote sensing data and meteorological precipitation data of wheat planting area in predicted year and N years before predicted year, the remote sensing data include multispectral data and thermal infrared remote sensing data;Then the relative vigor index is calculated using the multispectral data;Using the multispectral data and thermal infrared remote sensing data obtains normalized surface temperature index;Using the meteorological precipitation data obtains normalized precipitation index;Finally, the relative vigor index, normalized surface temperature index and normalized precipitation index are used to calculate the wheat scab incidence index of predicted year;The application can better reduce the interference caused by the difference of wheat vigor in different regions in large-scale monitoring on scab monitoring model, improve the objectivity and practicability of scab remote sensing monitoring.

The invention discloses a ship thermal infrared wake detection method based on a rhombus prediction frame YOLO model, and the method comprises the steps: replacing a conventional rectangular frame with a rotatable rhombus prediction frame, and positioning a wake candidate region through the center coordinate of a rhombus, the length of a diagonal line, and a rotation angle; thirdly, Gaussian filtering denoising is carried out on the candidate area, potential line segments are detected in combination with line support domain growth and rectangular fitting, effective line segments are screened through the false alarm number and the Euclidean distance, and the image is segmented based on gray average and variance binaryzation; and finally, extracting a continuous wake contour through edge detection, and fitting and optimizing morphological parameters in combination with a recursive broken line. By utilizing the method, the wake target on the thermal infrared remote sensing image can be quickly detected, the wake contour can be accurately fitted, the detailed form information can be acquired, and the method is particularly suitable for the application requirement of real-time monitoring of the sea surface moving target.

The present invention discloses a method for estimating the spatio-temporal distribution of the lapse rate of mountain air temperature based on FY4A AGRI land surface temperature, including: obtaining the FY4A AGRI thermal infrared remote sensing land surface temperature and its influencing factor data and preprocessing them to obtain data at a resolution of 4 km; using the random forest regression algorithm to perform spatial dimension reconstruction on the missing land surface temperature data, and using the S-G filter to smooth the spatial dimension reconstruction result in the time dimension to obtain the spatio-temporal dimension reconstruction result of the land surface temperature; using the random forest regression algorithm to construct a land surface temperature downscaling model to reduce the land surface temperature from 4 km to 1 km resolution; separating the influence of altitude on the land surface temperature and calculating the standardized land surface temperature; using the sliding window method to establish a linear regression relationship between the standardized land surface temperature and altitude, calculating the regression coefficient, and further obtaining the lapse rate of air temperature. It provides necessary data and technical support for related research fields such as weather forecasting and climate model applications.

The invention discloses a lake surface temperature reconstruction and freeze-thaw phenology extraction method based on filtering-automatic identification. The method comprises the steps of obtaining thermal infrared remote sensing data of a target lake in historical time; dividing a freeze-thaw period and a non-freeze-thaw period of the target lake; for a non-freeze-thaw period, acquiring a lake surface temperature time sequence of the non-freeze-thaw period; aiming at the freezing and thawing period, performing nonlinear curve fitting, and determining a freezing starting day, a freezing ending day, an ablation starting day and an ablation ending day of the target lake; according to the method, the continuity and precision of the lake surface temperature time sequence in the non-freezing-thawing period are improved, so that the error of traditional data caused by cold deviation is effectively reduced; and four accurate phenological key dates, namely freezing start and end and ablation start and end, can be scientifically, automatically and quantitatively extracted according to the freezing and thawing period, so that the deviation and low efficiency of subjective visual interpretation are avoided.

The invention discloses a marine oil spill type thermal infrared remote sensing deep learning identification method based on environmental condition constraint, and relates to the field of oil spill type identification, and the method comprises the steps: obtaining oil spill and seawater thermal infrared brightness temperature data and environmental temperature data of a to-be-identified sea area; calculating to obtain an oil-water brightness temperature difference value sequence; inputting the sequence and the air temperature data into a hot red overflow oil type identification model to obtain a predicted oil overflow type; according to the model, multi-scale time sequence features are extracted through a feature extraction module, feature adjustment is carried out through an explicit global consistency adjustment mechanism and an implicit real-time autonomous optimization mechanism based on temperature data by utilizing a physical constraint time module, and recognition is realized through multi-level feature fusion and a Softmax classifier. According to the method, the air temperature is taken as a physical constraint, gradient anomaly caused by the extreme air temperature is effectively inhibited while the recognition result is ensured to accord with a physical rule through a dual mechanism, and the recognition accuracy and the model training stability in a complex environment are remarkably improved.

The application discloses a remote sensing air temperature reconstruction method based on kernel function time dimension filling and deviation correction, and comprises the following steps: taking the clear sky air temperature retrieved by thermal infrared remote sensing as the basis, adopting a time smoothing method based on kernel function weighting, comprehensively considering the annual periodic change and short-term dynamic fluctuation of air temperature, reconstructing the theoretical clear sky air temperature of a target date, and filling the missing time dimension of the remote sensing air temperature caused by cloud cover; quantifying the systematic deviation between the theoretical clear sky air temperature under the cloud and the actually observed cloud air temperature, combining a plurality of source environment variables to construct a deviation correction model to calculate the air temperature deviation term caused by the cloud disturbance; applying the deviation correction model to the pixel scale, correcting the theoretical clear sky air temperature under the cloud, obtaining the air temperature estimation value closer to the actual cloud condition, and generating continuous, seamless all-weather remote sensing air temperature data. The application can solve the technical problem of data missing of remote sensing air temperature estimation under cloud cover.

The application provides an ozone profile inversion method based on thermal infrared remote sensing and belongs to the technical field of atmospheric environment remote sensing. The technical scheme is as follows: S1, data collection and processing; S2, an ozone prior profile model; S3, a temperature and humidity profile error correction model; S4, thermal infrared ozone profile optimization inversion; and S5, determination of a proportional factor. The application has the beneficial effect that the stratospheric column total amount and the tropospheric column total amount are respectively used for constraint in the optimization inversion, so that the accuracy of the ozone profile, especially the accuracy of the lower troposphere, is improved, and the deficiency of the thermal infrared sensor in the nadir observation mode is made up.

This invention discloses a method and device for geological disaster prevention and control of power transmission and transformation tower foundations, belonging to the field of landslide detection technology. The method involves: real-time acquisition of optical remote sensing images, radar remote sensing images, microwave remote sensing images, and thermal infrared remote sensing images; overlaying optical and radar remote sensing images to generate slope topographic spatial distribution data; analyzing vegetation growth status in the optical remote sensing images to obtain vegetation spatial distribution data; overlaying the slope topographic spatial distribution data and vegetation spatial distribution data to obtain topographic-vegetation coupled spatial distribution data, thereby determining the landslide sensitivity level of each preset grid unit and generating landslide sensitivity level spatial distribution data; and retrieving water storage spatial distribution data based on microwave and thermal infrared remote sensing images to assess the stability of the tower foundation area where the power transmission and transformation equipment is located, obtaining landslide level distribution data to determine control measures. This invention solves the problem of low accuracy in landslide detection in existing technologies.

The application discloses a water body pollution detection method and system based on satellite thermal infrared remote sensing, and relates to the technical field of water body pollution detection. The method comprises the following steps: collecting thermal infrared remote sensing parameters, environmental monitoring parameters and geographical feature parameters, constructing a detection basic parameter set, and extracting an effective water body grid; combining the thermal infrared remote sensing parameters of the effective water body grid with the environmental monitoring parameters, dynamically optimizing temperature inversion model parameters, performing temperature inversion, and obtaining water body surface temperature; performing pollution-free grid screening based on the water body surface temperature, and obtaining water body thermal infrared reference parameters of the pollution-free grid; comparing the water body surface temperature with the water body thermal infrared reference parameters, identifying and extracting water body thermal anomaly grid parameters; and performing spatial feature analysis based on the water body thermal anomaly grid parameters, positioning a water body pollution discharge outlet, and obtaining a pollution diffusion range. The application effectively improves the precision of water body pollution detection and the accuracy of spatial positioning of the pollution discharge outlet.

The present application discloses a dry hot rock fracture identification multi-source information fusion and intelligent interpretation technology, specifically comprising the following steps: step one: data acquisition: collecting multi-source data including seismic reflection wave, well logging lithology parameter, electromagnetic anomaly data, thermal infrared remote sensing image, and establishing a dry hot rock reservoir physical property parameter database, step two: data preprocessing: denoising and migration correction are carried out on seismic data, well logging data is normalized, electromagnetic data is inverted resistivity distribution, and temperature anomaly area is extracted from thermal infrared image, the present application relates to the geothermal development technical field. The dry hot rock fracture identification multi-source information fusion and intelligent interpretation technology integrates geological, geophysical, remote sensing and drilling data and other multi-modal information, combines feature level and decision level fusion strategy, effectively makes up for the sensing blind area of single sensor, and pre-processes the data, effectively improves the inhibition ability of seismic wave noise, electromagnetic interference and complex environmental factors.