6 results about "Band ratio" patented technology

Embodiments herein provide a method and system for a vicarious calibration of optical data from satellite sensors for urban scene flat fields. Identifying test sites automatically in the urban scene helps in vicarious calibration or on-board calibration of the hyperspectral / multispectral image. An internal average relative reflectance is calculated to get a relative reflectance of the image. Band ratios for various pixels is determined to assess flatness of the spectrum. Flat field candidates are identified from the various pixels having average band ratio nearing zero and a morphological technique is applied to determine a flat field. Finally, the image is calibrated vicariously based on the determined flat field as a test site. The on-board calibration of the remote sensing image may lead to a faster way to get the reflectance image of the scene, with the help of the calibration constants.

This invention provides a method, apparatus, and device for identifying water bodies on a single orbit of a polar-orbiting meteorological satellite, comprising: acquiring first-band data, first-positioning information data, and second-band data, second-positioning information data, and product data at a first spatial resolution from a polar-orbiting meteorological satellite; sampling the data at the second spatial resolution to obtain second-band data, second-positioning information data, and product data at the first spatial resolution; acquiring high-quality binary classification data; training a decision tree model based on the first-band data, second-band data, normalized index data, band difference data, and band ratio data at the first spatial resolution corresponding to high-quality water body pixels and high-quality non-water body pixels within the target area, to obtain a target decision tree model; and obtaining the single-orbit water body identification area based on the target decision tree model. The solution of this invention accurately identifies water bodies in complex background conditions, improving monitoring accuracy and applicability.

This invention provides a method and device for UAV inspection and identification of power transmission towers, belonging to the field of power facility health monitoring technology. The invention obtains the polar coordinates of the guy wire endpoints and calculates the guy wire length using UAV laser ranging to assess the degree of deformation anomaly; based on a 5-channel hyperspectral imaging system, it utilizes orthogonal flight path scanning imaging combined with a CNN algorithm to locate insulators in real time, and analyzes the insulator condition using UV band radiation values, discharge gradients, and temperature differences; it extracts reflectivity data in the 550-700nm band and calculates the corrosion area ratio using the band ratio method; and it comprehensively assesses the tower's health status by considering guy wire deformation, insulator anomalies, and corrosion degree. This method integrates multi-source data and intelligent algorithms to achieve high-precision detection of guy wire deformation, insulator defects, and corrosion area, solving the problems of low efficiency and single parameters in traditional inspections, and significantly improving the automation and reliability of power transmission tower condition assessment.

The application discloses a vegetation reflectance spectrum measurement method and system for forest monitoring, acquires original spectrum collection signals of a target area of a forest and carries out canopy penetration attenuation correction to establish a benchmark reflectance spectrum set, identifies a forest canopy projection area and atmospheric scattering interference of the benchmark reflectance spectrum set to form a ground true reflectance spectrum, carries out red edge feature positioning and band ratio analysis based on the ground true reflectance spectrum to form a vegetation reflectance spectrum index, carries out multi-time spectrum difference detection on the vegetation reflectance spectrum index and removes phenology interference to determine a change confidence interval, screens abnormal spectra based on the change confidence interval and carries out forest health grade labeling after correction by a spectrum evolution coefficient to output forest coverage change measurement results. The application can effectively eliminate the interference of forest canopy shadow and atmospheric scattering, distinguish normal phenology fluctuation from real health abnormality, and provide reliable spectrum data support for forest vegetation state monitoring.

The invention discloses a shallow lake phycocyanin concentration OLCI image remote sensing inversion method, and belongs to the technical field of environmental science and remote sensing monitoring. The method comprises the following steps: firstly, selecting a research range, and acquiring a large amount of actually measured PC concentration data in different periods through lake field sampling; secondly, satellite image acquisition and satellite-ground matching are carried out, a satellite-ground matching data set is obtained, a water body classification method based on a decision tree framework is adopted, fine classification is carried out on different driving types, and water bodies are divided into four types; then, based on a classification result, independently constructing a phycocyanin PC inversion model for each type of water body; and finally, performing inversion and detection on the phycocyanin concentration of the shallow lake by using the phycocyanin prediction model corresponding to each type of water. The method is high in timeliness and simple and convenient to operate, and has a wide business application prospect; a baseline ratio algorithm in the prediction model combines the advantages of a band ratio algorithm and a baseline algorithm in a traditional PC inversion algorithm, generalization and precision are improved, and the baseline ratio algorithm is the key for successful classification of water and successful construction of the model.

This application relates to the field of metal mining technology and proposes a method, apparatus, equipment, and medium for estimating the heavy metal content in soil. The method includes: collecting soil samples from a target area and determining the content of multiple heavy metal elements; acquiring visible-near-infrared hyperspectral reflectance data and X-ray fluorescence spectral data of the soil samples; calculating the competition coefficients between heavy metal elements using the L-V competition model to determine the set of competing elements for the target heavy metal element; constructing a multi-objective optimization function based on the band importance of random forests, with the goal of maximizing the spectral response differences of the target heavy metal element and minimizing the spectral response differences of its competing elements; combining wavelength spacing constraints; using the NSGA-II algorithm to select the optimal set of band ratio features; training a machine learning regression model; and estimating the heavy metal content in the soil of the target area. This scheme significantly improves the inversion accuracy and stability of the model.