401 results about "Brightness temperature" patented technology

The invention provides a microwave remote sensing pixel element decomposing method based on land and water living beings classifying information, which comprises the steps of: by using land and water living beings classifying information of spectrum remote sensing data, establishing a microwave mixed pixel element decomposing module, and calculating component brightness temperature in a microwavemixed pixel element by solving an underdetermined equation set. Through obtaining positions and brightness temperature values of different components in the microwave mixing pixel element, the space resolution of the microwave mixing pixel element of water and land boundary is remarkably improved, microwave brightness temperature values and classifying information of the water and land living beings in an observing region are obtained, and the precision of post stage inversion of the microwave remote data is improved. The invention has the important meanings of completing the decomposition ofthe microwave mixing pixel element data of the water and land boundary in a global region, ensuring that the decomposed microwave remote data space resolution is similar to the spectrum remote sensing data space resolution adopted in the classification, solving the problem of low space resolution of the microwave remote sensing data, and broadening the application field of the microwave remote sensing data.

The invention provides an inter-class variance MODIS forest fire point detection method, which comprises the following steps: pretreating original data required from an MODIS satellite receiving station, acquiring brightness temperature or reflectivity of each channel, and excluding illegal data; identifying image elements of cloud and water; identifying a smog mask; extracting potential fire points from excluded image elements of the cloud, the water and the illegal data; calculating inter-class variance yield of each potential fire point and effective background image elements in a background window; verifying fire points according to the threshold value of the inter-class variance and the smog mask; and outputting an MODIS image containing the fire points. By adopting lower threshold value, the potential fire points comprise true fire points as well as comprise certain false fire points of which the underlying surfaces have higher temperature so as to avoid omission; the threshold value of the inter-class variance is adopted to judge and remove the false fire points so as to prevent from being misjudged; moreover, information of the underlying surfaces around the fire points is considered fully, so that the method can adapt to different areas and seasons.

The invention discloses a stationary satellite-based fire remote sensing and monitoring method which solves the problem of automatic extraction of fire points in a forest and grassland, monitoring of a fire spreading situation and estimation of fire loss based on a stationary satellite. The method comprises the following steps: acquiring stationary satellite data, and calculating brightness temperature of image elements; judging latent fire point image elements from non-cloud and non-flare image elements in land image elements by using a method of a brightness temperature threshold value, and performing statistical judgment on the fire point image elements according to wave band information of background window image elements; culling out all-year-round high-temperature fire points based on a fixed fire point library and land utilization type data, and extracting positions and brightness temperatures of forest fire and grassland fire; providing a fire spreading situation sequence according to forest fire and grassland fire remote sensing and monitoring results of observation frequencies of the stationary satellite; estimating the loss caused by the forest fire or the grassland fire. The method disclosed by the invention quickly monitors a dynamic change of a disaster target, so that the efficiency for prewarning and emergently handling major disasters in China is further improved.

The invention provides a nonlinear calibrating method for a foundation microwave radiometer, and the nonlinear calibrating method can be used for realizing noise injected multi-point brightness temperature on the basis of foundation microwave radiometer noise injected two-point calibration. In the method, periodical calibration is performed on drifting of noise temperature and gain of a receiver by utilizing multi-point brightness temperature values which are acquired by two known reference targets and the other two known noise injected reference targets, and a calibration equation acquired by nonlinear factors of a foundation microwave radiometer system can be determined for real-time measurement of the target brightness temperature. Meanwhile, the invention also provides a nonlinear calibrating device for the foundation microwave radiometer, and the nonlinear calibrating device comprises a noise injection module and a system built-in calibration blackbody, wherein the noise injection module comprises a noise source, a switch and a directional coupler; and the system built-in calibration blackbody provides standard brightness temperature equivalent to the environment temperature. By utilizing the method, nonlinear errors caused by the nonlinear power characters of a detector diode can be reduced, so that the detection accuracy can be improved.

The invention discloses a forest fire fire point detection method based on an MODIS. Firstly, time sequence data is established for multiple days of MOD02 and MOD03 data from the MODIS, time sequence daily value data of multiple days of NDVI and brightness temperature is obtained, break points in the time sequence daily value data of the NDVI and brightness temperature are recognized through a time sequence break point recognition algorithm, and the recognized break points serve as potential fire points; relative fire points are distinguished from the potential fire points through a space context analysis method; real fire points are extracted from autonomous modular sensor data, the relative fire points are compared with the real fire points, and the correctness and leakage rate of the relative fire points are verified. The potential fire points are distinguished through the method that the time sequence data break points are detected through the BFAST algorithm without determining the potential fire points through fixed threshold values, therefore the algorithm has higher adaptability and flexibility for fire point distinguishing, the defects that the regional difference cannot be met by distinguishing the potential fire points through the fixed threshold values is overcome, and the fire point correctly discriminating rate is increased.

The invention discloses a method for inverting surface soil physical parameters through passive microwave remote sensing. The method comprises the steps as follows: step one, brightness temperature data and MPDIes (microwave polarization difference indexes) of an AMSR-E passive microwave remote sensing brightness temperature image are acquired by a launched satellite-borne passive microwave remote sensing meter AMSR-E; step two, a relationship of smooth surface reflectance, surface soil roughness parameters, MPDIes and vegetation optical thickness is acquired through deduction; step three, surface soil humidity mv and soil roughness h are calculated; and step four, the surface soil temperature TS is calculated. The method is based on a simplified passive microwave radiation transfer equation and combined with the MPDIes, a Q / H surface soil roughness model, a vegetation optical thickness model, a Fresnel equation and the like, so that a physical inversion model for the surface soil determinant attribute parameters including the soil temperature, the surface soil temperature and the soil roughness is constructed; and the method can be used for inverting the surface soil temperature, soil humidity and surface roughness distribution of China and even the global region and has important scientific significance.

The invention relates to a system and method for carrying out automatic detection on small burning spots of forest or grassland fires by using the infrared (IRS) data of an environmental minisatellite HJ. The method comprises the following steps: radiometric correction, brightness temperature calculation, reflectivity calculation, cloud and water recognition, potential burning spot determination,absolute burning spot determination, background characteristic analysis, relative burning spot determination, burning spot confidence, and detecting burning spot output. According to the scheme provided by the invention, based on the characteristics of the environmental minisatellite HJ, the automatic detection on forest or grassland fires can be realized only by using the infrared (IRS) data, thereby enhancing the disaster prevention and reduction capacities of domestically-produced satellites in China, and promoting the industrialized application process of domestically-produced satellites.

The invention discloses a comprehensive application method for integrating forest fire danger forecasting and ground fire point monitoring. The comprehensive application method for integrating the forest fire danger forecasting and the ground fire point monitoring comprises the steps that a forest fire danger class forecasting model is established; forest fire weather indexes are obtained by calculating; the forest fire weather index are divided to obtain forest fire danger classes; a satellite receiving system is adopted to receive polar orbit satellite data and pre-processes the polar orbit satellite data; on the basis of an MODIS fire disaster algorithm, a multiband brightness temperature value and a multiband reflectance value of the MODIS data are used for identifying an image; on the basis of a GIS technique, a forest fire danger class forecasting result and a ground fire point monitoring result are superimposed on an electrified wire netting output line and device and display a range of influence of a fire scene which can be intuitive analyzed. The comprehensive application method for integrating forest fire danger forecasting and ground fire point monitoring combines the forest fire danger class forecasting result and the ground fire point monitoring result with the electrified wire netting output line and device, the disposition efficiency of a forest fire accident is improved, and the monitoring range is wide.

The invention discloses a method for extracting straw burning fire based on a No. 1 environment satellite. The method comprises the following steps: S1, calculating the brightness temperature of each wave band according to the infrared data of the No. 1 environment satellite; S2, judging the average temperature of an image element according to the difference of a third wave band and a fourth wave band of a No. 1 environment satellite infrared camera to the brightness temperature of the same picture element and extracting potential hot outlier according to the temperature difference between a target image element and a surrounding image element; S3, extracting real hot outlier based on the data of the potential hot outlier; S4, utilizing a series of threshold value discrimination methods to remove false fire based on the data of the real hot outlier and other wave band data with the same resolution ratio with that the No. 1 environment satellite; and S5, judging fire in a farmland as the straw burning fire according to the land classification information. The method can utilize the infrared, multi-light spectrum and land utilization data to extract the straw burning fire, thereby improving the low spatial resolution of the original monitoring sensor and realizing the operation of monitoring business of the straw incineration point.

The invention provides an atmospheric temperature and humidity profile processing method and system with active and passive remote sensing combined. The system relates to the following steps that atmospheric microwave radiation brightness temperature data are detected and acquired through a microwave radiometer, cloud vertical structure data are detected and acquired through a cloud radar, and ground surface air temperature, humidity and air pressure data of a detecting area are detected and acquired through a ground surface meteorological element sensor; a data processor builds a three-layer feedforward BP neural network model for processing and training, and the BP neural network model with parameters optimized is obtained; the detected atmospheric microwave radiation brightness temperature data, the detected cloud vertical structure data and the detected ground surface air temperature, humidity and air pressure data are input into the BP neural network model by the data processor to serve as input layers, and the atmospheric temperature and humidity profile is obtained through processing and output of a BP neural network. Due to the atmospheric temperature and humidity profile processing method and system with active and passive remote sensing combined, the error for processing the atmospheric temperature and humidity profile in cloud weather is effectively reduced, and the accuracy and the effectiveness of the atmospheric profile are improved.

The present invention provides an application data processing method of a spaceborne microwave radiometer. According to the application processing method, by inputting the global or regional profile data and the radiation brightness-temperature values in a matrix, adopting a wavelet neural network algorithm to train the data, and comparing the outputted atmosphere humiture profile and ground / sea surface parameters with the preset threshold indexes, the back propagation of the atmosphere humiture profile and ground / sea surface parameter errors is implemented; by correcting each layer of weights in an error gradient descent manner, the work of utilizing a data set to correct the inversion parameters locally is realized, the local correction capabilities of the equipment developers and users to the inversion software parameters are improved, and further the detection and inversion precision is improved further.

The invention relates to an offset correction method for a spaceborne microwave radiometer. The offset of thermal vacuum calibration test data and apparatus backup data before emission of the microwave radiometer, and on-orbit operation remote-measurement data and remote sensing data of the microwave radiometer are corrected to obtain accurate brightness temperature value, so that the on-orbit calibration and accurate analysis of successfully launched satellite can be conducted. The satellite data quality is improved, and can be better applied to climate research and data assimilation.

The invention provides an atmosphere profile inversion method based on a foundation hyperspectral microwave radiometer, and belongs to the technical field of microwave remote sensing. The atmosphere profile inversion method based on the foundation hyperspectral microwave radiometer comprises the following steps of setting system parameters of the foundation hyperspectral microwave radiometer, acquiring background parameters and background field errors, establishing a radiation transfer model, determining atmospheric radiation transmission brightness temperature, and inverting an atmosphere profile by using the iterative process of a variational method. Through the utilization of the atmosphere profile inversion method based on the foundation hyperspectral microwave radiometer, the accurately inverted atmosphere profile can be obtained through the foundation hyperspectral microwave radiometer, and the atmosphere profile inversion method is especially suitable for multi-channel radiometers and detection situations of insufficient prior data, enables a large amount of frequency band information provided by the foundation hyperspectral microwave radiometer to be used completely, and has strong adaptability and small detection errors.

The invention relates to a road accumulated snow calamity monitoring and early warning method based on multisource remote sensing data. According to the method, first of all, a cloud-free accumulated snow area product is obtained by performing cloud removal processing on a MODIS accumulated snow area product by use of a cloud removal algorithm; an accumulated snow depth product is obtained through a snow depth reversing algorithm by use of a passive microwave SSM / I and SSMI / I brightness temperature observation, for calculating a whole-road average annual accumulated snow day number of many years and a multi-year average accumulated snow thickness and determining a key accumulated snow road mileage of a whole road; secondly, by use of Landsat and high-definition satellite remote sensing image data, through a normalization accumulated snow exponent and a high-definition accumulated snow exponent threshold method extraction method, accumulated snow is identified, and accumulated snow spatial distribution of each mileage of a key road is extracted; and finally, through a surface temperature, ASTER GDEM, a gradient, an aspect and a meteorological condition, roads prone to icing and roads prone to avalanches and blowing snow are determined. According to the invention, monitoring of road accumulated snow distribution and the roads prone to icing, avalanches and blowing snow are realized by use of the multisource remote sensing data, and monitoring and early warning of road accumulated calamities are accurately realized.

The invention provides a method and a system for inverting urban surface temperatures. The method comprises the steps of S1, obtaining the TM image data of a region to be tested, S2, preprocessing the TM image data, including atmospheric correction, geometric correction, image enhancement and image clipping, S3, converting the pixel gray level value of the thermal infrared band in the preprocessed TM image data into corresponding radiance, S4, obtaining corresponding a radiation brightness temperature according to the radiance, and S5, converting the radiation brightness temperature into the surface temperature by use of the emissivity of different surface features, wherein the TM image data are multi-band scanning image data obtained by use of an optical earth land satellite Landsat-5 thematic charting instrument TM. The method can be widely applied to monitoring the urban thermal environment, and has the advantages of low cost, high accuracy and wide application range.

The invention relates to the technical field of satellite communication and particularly relates to an acquisition method for sea ice concentration on the basis of the Ocean No. II satellite scanning microwave radiometer. The acquisition method for the sea ice concentration on the basis of the Ocean II satellite scanning microwave radiometer comprises the following steps: according to a first brightness temperature data of a typically characteristic area, a brightness temperature characteristic value is obtained; the typical characteristic area comprises a characteristic area of one-year ice, a characteristic area of multiyear ice and characteristic area of sea water; the first brightness temperature data is acquired through the Ocean II satellite scanning microwave radiometer; according to the brightness temperature characteristic value and a second brightness temperature data of a monitored area, dynamic brightness temperature characteristic value of the monitored area is acquired; the second brightness temperature data is acquired through the Ocean No. II satellite scanning microwave radiometer; according to the dynamic brightness temperature characteristic value of the monitored area, the dynamic sea ice concentration of the monitored area is acquired. The acquisition method for sea ice concentration on the basis of the Ocean II satellite scanning microwave radiometer overcomes the technical problem that the sea ice concentration of a correspondingly monitored area cannot be acquired on the basis of the data acquired by the Ocean II satellite in the prior art.

The embodiment of the invention discloses a control method and system for the forest fire exploration through an unmanned plane. The method comprises the steps: obtaining a fusion image and the brightness temperature values of all pixels in the fusion image through the preprocessing, splicing and fusion and brightness temperature inversion of a visible image, a short-wave infrared image, a mid-wave infrared image and a long-wave infrared image; recognizing the position of an open fire point and the position of a blind fire point according to the fusion image and the brightness temperature values of all pixels in the fusion image; finally analyzing the influence scope of forest fire and the distance between the center of the forest fire and a power transmission line according to the positions of the fire points, determining the level of forest fire, and carrying out the early warning according to the level of forest fire. Compared with the prior art, the method can effectively distinguish the open fire point and the blind fire point, can carry out the early warning timely according to the level of forest fire, can effectively improve the detection rate of the forest fire and the put-out success rate of the forest fire, reduces the safety risk of fire fighters, and guarantees the safe and reliable operation of power transmission lines.

The invention provides a forest fire smoke recognizing method and device. The method includes the steps of recognizing smoke pixels and non-smoke pixels by means of a multi-channel threshold method and the reflective rates or the brightness temperature values of 36 spectrum channels, obtaining input characteristics of a neural network classifier from the reflective rates or the brightness temperature values of the 36 spectrum channels, training the neural network classifier by means of smoke input characteristics of the smoke pixels and non-smoke input characteristics of the non-smoke pixels, obtaining a smoke recognizing and classifying unit, and precisely recognizing the smoke pixels and the non-smoke pixels by means of the smoke recognizing and classifying unit. The method achieves precise classification of the smoke pixels, and the probability that cloud pixels are mistaken for the smoke pixels is greatly reduced due to the fact that the non-smoke pixels include the cloud pixels. Besides, due to the fact that the smoke pixels include thin-smoke pixels, the thin-smoke pixels can be precisely recognized by means of the smoke recognizing and classifying unit, and precision of thin-smoke area recognition is further improved.

The invention relates to a method for identifying industrial thermal anomaly region based on multi-source satellite remote sensing data, which comprises the following steps: acquiring multi-source satellite remote sensing data of pollution monitoring region; obtaining visible light data, near infrared data and thermal infrared sensing data from multi-source satellite remote sensing data; the vegetation index, surface type information and specific surface emissivity of the pollution monitoring area being calculated; calculating the brightness temperature of thermal infrared radiation in the pollution monitoring area; calculating the surface temperature data of the monitoring area; extracting ground image data of monitoring area from multi-source satellite remote sensing data, and dividing the ground image data into a plurality of sub-area image data; extracting region feature information of each sub-region image data; fuzzy classification of sub-region image data to obtain potential industrial region image data; industrial thermal anomaly regions being identified based on surface temperature data and potential industrial region image data.

The invention provides a satellite remote sensing monitoring method and a processing device for straw burning fire points in one embodiment and aims at solving the problems of high difficult of manual burning control and failure in guaranteeing the control efficiency and the control accuracy in the prior art. The method comprises the following steps: the processing device obtains the satellite remote sensing data of a region to be monitored obtained by a remote sensing monitoring device; for each pixel in the satellite remote sensing data, whether the brightness temperature value of the pixel is located within a preset fire point brightness temperature range is judged, and if so, the pixel is marked as the burning fire point; all the pixels marked as the burning fire points are marked in the region to be monitored, and the position information and attribute report form of the burning fire points in the region to be monitored are obtained statistically. The method is capable of remarkably improving the fire point monitoring efficiency and the monitoring accuracy, convenient to implement, and easy to popularize and apply.

The invention provides a sea surface parameter synchronous inversion optimization method which includes the steps: 1) acquiring multi-incident-angle radiation brightness temperature observation valuesand multi-incident-angle backscattering coefficient observation values; 2) performing median filtering, adaptive median filtering and Wiener filtering on the multi-incident-angle radiation brightnesstemperature observation values to obtain three-type radiation brightness temperature observation values after noise reduction; 3) acquiring multi-incident-angle radiation brightness temperature simulation values by a forward radiation brightness temperature theoretical model, and acquiring multi-incident-angle backscattering coefficient simulation values by a backscattering coefficient theoretical model; 4) substituting the three-type radiation brightness temperature observation values, the radiation brightness temperature simulation values, the backscattering coefficient observation values and the backscattering coefficient simulation values into an L / C / K waveband multi-priced function to synchronously obtain three-type sea surface parameters; 5) comparing the three-type sea surface parameters with float measurement sea surface parameters, calculating inversion root-mean-square errors and selecting sea surface parameter values corresponding to the minimum root-mean-square error as final sea surface parameter values.

The invention relates to a microwave remote sensing soil moisture monitoring system and a monitoring method thereof. The monitoring system comprises a detection arm, a signal receiving antenna and a thermal infrared thermometer carried on the detection arm, a microwave detection system electrically connected with the signal receiving antenna and the thermal infrared thermometer, and a data processing device electrically connected with the microwave detection system. According to the monitoring method, a data analysis treatment system in the data treatment device performs the real-time synchronous treatment according to brightness temperature data acquired by the microwave detection system and physical temperature data on the surface of soil obtained by the thermal infrared thermometer to accurately evaluate soil moisture information. The microwave remote sensing soil moisture monitoring system and the monitoring method thereof provided by the invention obtains soil microwave radiation signals by using a passive way, has a simple working model, high precision, and high efficiency, and is not limited by the observation frequency.

Scene-independent baseline signals in output value signals from radiometer or receiver channels used in millimeter wave imaging are eliminated or reduced and an improved image is composed. The scene-independent baseline signals are believed to result from a standing wave which is established between an antenna of the channel and a movable scanning element which scans radiant energy from the scene into each channel. The movable scanning element introduces changes in geometry which change the characteristics of the baseline signals depending upon the position of the movable scanning element. The baseline signals are measured by viewing a scene of uniform brightness temperature, and the baseline signal contribution is subtracted from the output value signals from each channel. The baseline compensated output signals are used to compose an image with better contrast.

The invention provides a sea surface salinity retrieval method and a sea surface salinity retrieval device. The sea surface salinity retrieval method comprises the following steps: simulating atmosphere top brightness temperature so as to acquire a multi-incident angle brightness temperature simulation value of an ionopause above the atmosphere layer at an L wave band, a C wave band and a K wave band of a preset multi-angle radiation model, and a backscatter coefficient simulation value of the L wave band; respectively acquiring a multi-incident angle brightness temperature measuring value of the ionopause above the atmosphere layer at the L wave band, the C wave band and the K wave band, and a backscatter coefficient measuring value of the L wave band; performing sea surface salinity retrieval calculation according to the simulated multi-incident angle brightness temperature simulation value and the backscatter coefficient simulation value of the L wave band, and the acquired multi-incident angle brightness temperature measuring value and the backscatter coefficient measuring value of the L wave band. By adopting the sea surface salinity retrieval method, the accuracy rate of the retrieval calculation result can be improved.

The invention provides a one-dimensional mirror image synthetic aperture radiation imaging method. The method comprises the following steps of: constructing a linear equation set by utilizing the relation among relevant output, the cosine visibility and the scene brightness temperature distribution, solving the linear equation set to obtain a cosine visuality; and then reconstructing scene brightness temperature distribution through a mode of carrying out anti-cosine transformation or solving a generalized inverse matrix of an impulse response matrix. In the invention, the imaging performance of a traditional synthetic aperture imaging system with a large array element number is acquired by using a small array element number, thereby the complexity of the structure of the traditional synthetic aperture imaging system and the complexity of signal processing are effectively reduced.

The invention belongs to the technical field of atmospheric remote sensing, and discloses a method for estimating a near-surface atmospheric temperature by using thermal infrared data of a geostationary meteorological satellite. The satellite is used for observing brightness temperature, meteorological station and numerical prediction model data to obtain a representative thermal infrared observation brightness temperature and near-surface atmospheric temperature; satellite cloud detection products are used for obtaining matched data sets for the observation brightness temperature, the stationactually measured temperature and auxiliary data under cloudless conditions; based on a stepwise regression method, the relationship between the radiation temperature of satellite observation, atmospheric pressure, relative humidity, a satellite observation angle, Julian daily parameters and the like and near-surface atmospheric temperature is analyzed, and key factors used for estimating the atmospheric temperature are determined; and a inversion model of near-surface temperature estimation is constructed by using a neural network technology. The method can realize the purpose of inversion of the near-surface atmospheric temperature under the clear sky condition of the thermal infrared data of the stationary meteorological satellite.

The invention discloses a solar irradiation predicting method based on multiple linear regression. The method comprises the first step of calculating the theoretical value of the solar irradiation value, the second step of calculating the attenuation rate of the solar irradiation, the third step of establishing a month-by-month regression equation of the cloudage, the cloud picture brightness temperature, visible light emissivity and the radiation attenuation rate based on a multiple linear regression model, and the fourth step of calculating the actual solar irradiation intensity predicted value according to the month-by-month regression equation established in the third step and the theoretical value of the solar irradiation value. The month-by-month regression equation of the cloudage, the cloud picture brightness temperature, the visible light emissivity and the radiation attenuation rate is established based on the multiple linear regression model, the characteristic quantities of a cloud picture are predicted according to the linear regression model, and the purpose of accurately predicting the solar irradiation is achieved.

The invention relates to a scaling method of a foundation microwave radiometer. The scaling method comprises: obtaining voltage values output through each channel in different attenuation values, calculating corresponding equivalent brightness temperature values through attenuation values, calculating system non-linear factors and noise source injection noises according to corresponding relation between each equivalent brightness temperature value and the output voltage of each channel, and completing preliminary scaling; according to sounding profile data matching with time and place, establishing a corresponding relation of atmosphere optics thickness and atmosphere quality, substituting scaling parameters, obtaining a scaling relation curve, determining the scaling parameters according to the scaling relation curve; observing cold air and cold air coupling noises at each attenuation value, obtaining voltage values output through each channel at different attenuation values and fitting the attenuation values and the voltage values; calculating the equivalent noise temperature when an antenna aligns with the cold air through the relation of the attenuation values and the output voltage values of the attenuator, and determining the brightness temperature values corresponding to the cold air observed by the foundation microwave radiometer in different time and places.

The invention discloses a forest fireproof monitoring method based on MODIS remote sensing data, and the method comprises the steps: employing the brightness temperature values of a fourth channel and an eleventh channel in each piece of pixel data of the MODIS remote sensing data for determining whether a place corresponding to a single pixel is a high-temperature point or not, and then obtaining the corresponding latitude and longitude when the place corresponding to the single pixel is determined to be the high-temperature point; obtaining a current forest video image of the place corresponding to the latitude and longitude of the determined high-temperature point, enabling the current forest video image to be used in a preset mixed Gaussian background model, and generating a background image corresponding to the current forest video image of the place corresponding to the latitude and longitude of the determined high-temperature point; solving the difference between the current forest video image and the background image corresponding to the current forest video image, and generating a current background difference image; enabling the generated current background difference image to be employed in a preset smoke object detection algorithm, and obtaining the smoke object information. According to the invention, the method can reduce the false alarm rate of fire monitoring and improve the fire monitoring precision.

The invention discloses a super-resolution reestablishment method for a microwave remote sensing image based on an SRCNN (Super-Resolution Convolutional Neural Network) and belongs to the technology of microwave remote sensing and detection. The method comprises the steps of firstly simulating a microwave remote sensing flow, and forwardly producing a high-resolution microwave remote sensing imageTB and a low-resolution microwave remote sensing image TA to form a data set; then preprocessing the data set to generate an SRCNN training set; constructing a five-layer depth convolutional neural network based on the SRCNN training set; and finally inputting the to-be-processed low-resolution microwave remote sensing image to the constructed five-layer depth convolutional neural network, wherein the output of a fourth-layer of the depth convolutional neural network is the reestablished high-resolution microwave remote sensing image. With the super-resolution reestablishment method for the microwave remote sensing image based on the SRCNN, the calculation complexity in reestablishment can be effectively reduced, an accurate antenna pattern is not needed, the method is a novel microwave remote sensing image reestablishment method, and a brightness temperature image of an original scene can be efficiently reestablished in real time.