8 results about "Optical depth" patented technology

The application discloses a vegetation dark background aerosol inversion method and device based on physical and machine learning fusion and a medium, relates to the field of aerosol optical depth inversion, and comprises the following steps: constructing a sample library based on AVHRR / MERSI observation data and MODIS surface reflectance product data; adopting a machine learning algorithm to establish a surface reflectance relationship model with medium / short-wave infrared apparent reflectance, NDVI and multiple angle data as input and label red band surface reflectance as output; determining the label red band surface reflectance by adopting the surface reflectance relationship model for satellite observation data to be processed, calculating the red band apparent reflectance, and then obtaining a set of dark background pixels to be inverted; for any dark background pixel to be inverted, a preset lookup table is called, the red band apparent reflectance is matched with the apparent reflectance in the preset lookup table, and the aerosol optical depth is determined. The application can realize high-precision AOD inversion.

The application discloses a kind of based on deep learning inversion AOD auxiliary atmospheric correction method, device and medium, it is related to atmospheric remote sensing and deep learning cross technical field, including: the multiple type training sample set of fusion measured and physical simulation data is constructed, after quality screening and stratification processing, obtain the pre-training sample of adaptation satellite remote sensing data, fine-tuning sample and extreme scene sample;Create deep learning network model with data loss and anti-operator constraint loss weighted fusion as total loss function, adopt cross-satellite transfer learning strategy, in turn complete pre-training, satellite special fine-tuning and extreme scene enhancement training, obtain the training completed aerosol optical depth inversion model;Radiometric calibration is executed to satellite remote sensing original data, cloud detection, geometric correction preprocessing, input model inference calculation, to output result is post-processed and quality classification, obtain the aerosol optical depth inversion result used for auxiliary atmospheric correction.

This application provides a method, apparatus, electronic device, and medium for aerosol optical thickness data completion, belonging to the field of remote sensing data processing and atmospheric environment monitoring. The method includes: acquiring multi-source data; encoding the multi-source data using a 3D encoding / decoding network to obtain an initial feature map; using auxiliary data as guiding weights for underlying surface geographic features, and combining this with a spatial attention module and the initial feature map to obtain a spatial feature map; obtaining a temporal feature map based on a temporal attention module based on the initial feature map; concatenating the spatial and temporal feature maps, and then adding them element-wise to the initial feature map to obtain a comprehensive feature map; decoding the comprehensive feature map to obtain an initial completion result; converting the initial completion result into corresponding completion values; determining missing regions based on each completion value; and completing the missing regions to obtain the completed aerosol optical thickness data. This application can improve the accuracy of aerosol optical thickness data completion.

A structure light depth sensing system and a structure light depth sensing method using auxiliary time-of-flight sensing are provided. The structure light depth sensing system includes a time-of-flight depth sensing circuit and a structure light depth sensing circuit. The structure light depth sensing method includes: providing similarity values in a predetermined search range, in which the predetermined search range includes a largest similarity value; calculating a compensation range in the predetermined search range in accordance with a target depth value of depth data of the time-of-flight depth sensing circuit; calculating compensated similarity values in accordance with a weighting gain and the similarity values in the compensation range; and determining if a maximum value among the compensated similarity values is greater than the largest similarity value. When the determination result is yes, the maximum value among the compensated similarity values is determined to be a correct similarity value.

This invention discloses a high-resolution distributed acoustic-vibration demodulation method based on sweep-frequency optical deep anti-fading. First, continuous linear sweep-frequency probe light is injected into an optical fiber, and the original backscattered Rayleigh signal containing information distributed along the fiber is acquired through coherent beat frequency acquisition. Then, the original backscattered Rayleigh signal undergoes frequency domain demultiplexing and matched-filter pulse compression processing to reconstruct multiple high-resolution time-domain complex envelope signals from a single sweep. Next, the high-resolution time-domain complex envelope signals are input into a deep anti-fading demodulation network, where amplitude-phase joint modeling achieves adaptive suppression of random fading and noise in the time-domain complex envelope signal, outputting a de-fading phase sequence. Finally, the de-fading phase sequence is used for phase difference demodulation, and combined with an optical time-of-flight positioning model, to achieve highly robust demodulation and spatial positioning of vibration events. This method maintains high resolution and robustness even under strong noise, long distance, and deep fading conditions, making it suitable for distributed acoustic-vibration and ultrasonic monitoring scenarios.

The present application relates to the field of meteorological remote sensing, and specifically discloses a sandstorm source area susceptibility spatial prediction method based on an sp-GSH-EL model; including sandstorm source point extraction, non-sandstorm source point extraction, obtaining a covariant, defining a response variable, integrated learning model training, and sandstorm source area susceptibility prediction; by combining aerosol optical depth (AOD) threshold method and hourly AOD change rate, the dynamic change of dust source point can be more accurately captured, and the timeliness and accuracy of sandstorm source area monitoring can be improved. At the same time, it has the ability to capture spatial heterogeneity: the sp-GSH-EL model combines different spatial proximities to optimize the description ability of local heterogeneity, global similarity and non-linear relationship, and improve the modeling accuracy of multi-scale spatial data.

Embodiments of the present application provide a method for improving unevenness of light intensity of a stripe, the method comprising: forming a grating stripe on a projection surface by a grating projection camera; taking a plurality of calibration points along a straight line perpendicular to the grating stripe, wherein the number of the calibration points is greater than the number of the grating stripe; obtaining a gray value of each of the calibration points; and adjusting a rotation angular velocity of a galvanometer of the grating projection camera according to the gray value. Embodiments of the present application also provide a structured light depth camera, a laser camera, an electronic device, a computer readable storage medium and a computer program.

The application discloses a horizontal visibility estimation method and device, equipment and storage medium, and relates to the technical field of meteorological monitoring. The method comprises the following steps: acquiring pixel correlation data corresponding to an effective pixel image shot by a target stationary satellite, and constructing a first-level pixel feature corresponding to the effective pixel image. According to the first-level pixel feature and a target deviation revision model obtained by pre-training, aerosol optical depth deviation revision processing is performed to obtain a target aerosol optical depth after deviation revision. According to the target aerosol optical depth and the pixel correlation data, a second-level pixel feature corresponding to the effective pixel image is constructed. According to the second-level pixel feature and a target visibility estimation model obtained by pre-training, horizontal visibility estimation processing is performed to obtain a target estimated horizontal visibility corresponding to the effective pixel image in a shooting period, which can greatly improve the accuracy of the horizontal visibility estimation result and provide high-quality data support for atmospheric pollution assessment.