26 results about "Green band" patented technology

A condenser lens (21) comprises a first surface (211) and a second surface (212). Light including components of the red band, the green band, and the blue band is incident on the first surface (211). The incident light including the components of the red band, green band, and blue band is emitted from the second surface (212). An aspherical convex lens section (213) is formed in the center of the second surface (212). An aspherical concave lens section (214) is formed around the convex lens section (213).

The embodiment of the invention discloses a water body information extraction method based on a remote sensing image, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a green light wave band value, a red light wave band value and a near-infrared wave band value of a target pixel in a to-be-processed remote sensing image; based on the green light wave band value, the red light wave band value and the near-infrared wave band value, calculating a water and land pixel measurement variable of the target pixel through a preset water body index formula; comparing the land and water pixel metric variable obtained by calculation with a preset threshold value, and classifying the target pixel as a water pixel or a non-water pixel based on a comparison result; the accuracy of land and water pixel measurement can be further improved, the phenomenon that the water body area is confused with other interference areas such as roads, building roofs and bridges is reduced, and water body recognition is more accurate.

The invention discloses a duckweed type eutrophication water body monitoring method based on sentinel No.2 satellite images, and relates to the technical field of water body monitoring, and the method comprises the following steps: carrying out atmospheric correction processing on sentinel No.2 L1 C-level satellite images in a non-duckweed growth season and a duckweed growth season, and then re-sampling all wave bands to the same resolution; performing mean filtering on a blue wave band, a green wave band, a red wave band, a near-infrared wave band and a narrow near-infrared wave band of the multi-wave-band surface reflectance image after re-sampling in the non-duckweed growth season and the duckweed growth season, and then performing wave band synthesis to obtain a non-duckweed growth season wave band synthesis image and a duckweed growth season wave band synthesis image; and calculating the proportion of the duckweed growth range in the water body range by using the non-duckweed growth season waveband synthetic image and the duckweed growth season waveband synthetic image. The duckweed type eutrophication water body can be rapidly monitored in a large range at low cost.

The application discloses a kind of based on sentinel-2 satellite image's duckweed type eutrophication water monitoring method, it is related to water monitoring technical field, including the following steps: after the atmospheric correction processing of sentinel-2 L1 C level satellite image of non-duckweed growing season and duckweed growing season, all wavebands are resampled to the same resolution, after mean filtering to the blue band, green band, red band, near infrared band, narrow near infrared band of multi-band surface reflectance image after resampling of non-duckweed growing season and duckweed growing season, then band synthesis is carried out, obtain non-duckweed growing season band synthesis image and duckweed growing season band synthesis image;The proportion of duckweed growing range in water range is calculated using non-duckweed growing season band synthesis image and duckweed growing season band synthesis image.The present application can realize the fast, large-scale, low-cost duckweed type eutrophication water monitoring.

The invention discloses a beach garbage remote sensing monitoring method based on automatic double-threshold segmentation, and the method comprises the steps: generating a self-adaptive flight task planning scheme through obtaining the monitoring demand information and multi-source environment data of a target beach region, and controlling an unmanned plane platform to collect a high-resolution remote sensing image data set; after a digital orthoimage is generated through preprocessing, a green band component and a blue band component of the digital orthoimage are extracted, a green band segmentation threshold interval and a blue band segmentation threshold interval are calculated based on a local statistical feature adaptive algorithm, and a first candidate garbage distribution binary image and a second candidate garbage distribution binary image are generated through binarization segmentation processing. And obtaining beach garbage distribution vector data through space fusion and noise elimination processing, carrying out space-time change detection analysis and pollution level evaluation, and outputting a beach garbage monitoring report. Intelligent planning is realized through multi-source data fusion, the problem of spectrum overlapping is solved by adopting dual-band local adaptive threshold segmentation, and reliable technical support is provided for beach garbage treatment.

The present invention discloses a superposition fusion method, system, device and storage medium based on generalized vegetation cover, the method comprising: obtaining a satellite remote sensing image having a near-infrared band, a red band, a green band and a blue band; performing inter-spectral fusion of the near-infrared band with one or more of the red band, the green band and the blue band, calculating a characteristic ratio index using the fused band combination as a numerator and the corresponding band combination before fusion as a denominator; constructing a linear transformation function based on the characteristic ratio index; setting a virtual minimum value v of the characteristic ratio index as min =kx min A power function based on generalized vegetation cover is constructed based on the linear transformation function and the virtual minimum. The characteristic power of the power function based on generalized vegetation cover is determined by the power function value corresponding to the characteristic threshold of the characteristic ratio index being equal to ε. According to the principle of superposition enhancement, the power function based on generalized vegetation cover with the determined characteristic power is used to enhance the red and green bands, respectively. This invention improves the standardization of the enhancement process and the consistency of the processing results.

The invention discloses a suaeda heteroptera carbon sequestration amount calculation method, system and device based on an aerial image of an unmanned aerial vehicle and a medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring an unmanned aerial vehicle aerial image of a monitoring area; pre-processing the aerial image to obtain a pre-processed image of which the shadow and interference pixels are removed and which has a floating-point type color value; performing ratio calculation according to the color values of the red light and green light wavebands in the preprocessed image to obtain a ratio image of the red light and green light wavebands; determining a red-green band ratio threshold according to the ratio image of the red-green band, and extracting carbon sequestration information according to the red-green band ratio threshold and a suaeda heteroptera carbon sequestration remote sensing inversion model to obtain a suaeda heteroptera carbon sequestration remote sensing inversion result of the monitoring area; and calculating the area of the monitoring area and the total weight of suaeda heteroptera carbon sequestration according to a suaeda heteroptera carbon sequestration remote sensing inversion result by utilizing ENVI software, and drawing a suaeda heteroptera carbon sequestration remote sensing thematic map. The method can realize unmanned aerial vehicle remote sensing monitoring of the suaeda heteroptera carbon sequestration amount.

A kind of coral reef bottom reflectivity multispectral satellite remote sensing retrieval method belongs to satellite ocean remote sensing application technical field.Based on shallow sea single scattering radiation transfer model, through the collection of adjacent pixel pairs of different depth and different bottom type and the pixel of same bottom type (such as sandy seabed) of different depth, the optimal band rotation coefficient and the ratio of blue-green band diffuse attenuation coefficient are obtained respectively, combined with the optical deep water remote sensing reflectance data of the adjacent shallow sea area, the depth-independent reflectance equation and the bottom type-independent reflectance equation are constructed respectively, and based on the two equations, the coral reef bottom reflectivity image is obtained by joint calculation.It is suitable for high-resolution multispectral satellite remote sensing image, and auxiliary data such as water depth and water property are not required, and the blue-green band reflectivity data of coral reef bottom can be directly calculated and obtained.

The present application provides a light source device capable of improving the use efficiency of the internal space of the light source device by suppressing the increase in the number of components and a projection device provided with the light source device. The light source device is provided with: a rotating wheel device (150) having a rotating wheel (151) including a reflection region that reflects blue band light and a transmission region that transmits at least red band light and green band light; and a dichroic mirror (200) that transmits blue band light and red band light and reflects green band light, the dichroic mirror (200) including an intersection portion (200a) that is crossed in a manner overlapping a portion of the rotating wheel (151) and is irradiated with blue band light and red band light and green band light.

The application discloses a kind of ground water level monitoring methods based on multispectral satellite, it is characterized in that, propose a kind of special water gauge, special water gauge is by red, white grid interval distribution composition, combined satellite remote sensing technology and projection amplification method, using near infrared, green band calculation and classification normalized water index, or combined near infrared, infrared and red band to identify water body.Under the satellite image resolution D2×D2, grid D1×D1 on water gauge can be identified and read, water gauge reading is amplified (H / L) times, water level in water tank is obtained by conversion, and then water area water level is obtained.The application can intuitively identify water body, read water gauge reading, convert the identification of satellite image to the identification of vertical size, and make the water level accuracy meet the use requirements, finally achieve the purpose of satellite intuitive monitoring water level.The application method is simple and effective, and has strong adaptability, and it is very easy to use satellite image to intuitively obtain water level data meeting accuracy requirements.

The present invention discloses a product fusion method, system, device and storage medium based on generalized vegetation cover, the method comprising: obtaining a satellite remote sensing image having a near infrared band, a red band, a green band and a blue band; calculating a characteristic ratio index according to the near infrared band, the red band, the green band and the blue band; constructing a linear transformation function according to the characteristic ratio index; setting a virtual minimum value v of the characteristic ratio index to be 0. min =kx min ; According to the linear transformation function and the virtual minimum, a power function based on generalized vegetation cover is constructed; let v min+ =v min +ρ; by v min+ The corresponding power function value based on generalized vegetation cover and v min+ The power function based on generalized vegetation cover is determined to have a characteristic power; and according to the principle of product enhancement, the power function based on generalized vegetation cover with the determined characteristic power is used to enhance the red band and the green band respectively. The present invention improves the standardization of the product enhancement process and the consistency of the processing results.

The embodiment of the invention provides a remote sensing image cloud distribution discrete degree detection method, device and equipment, and is applied to the technical field of remote sensing image processing. The method comprises the steps of calculating a synthetic wave band pixel gradient value based on a blue-green wave band gray value of a to-be-detected remote sensing image; based on a Sobel operator, calculating a pixel normalization synthesis waveband gradient value according to the synthesis waveband pixel gradient value; generating a cloud mask binary image based on the to-be-detected remote sensing image; removing cloud spots of which the area is smaller than a first area threshold value on the cloud mask binary image, extracting and calculating the mean value of normalized synthesis waveband gradient values of edge pixels of the cloud spots of which the area is larger than a second area threshold value, and calculating the total mean value of the mean values; and judging whether the total mean value is smaller than a first threshold value, and if yes, determining that the cloud distribution dispersion degree of the to-be-detected remote sensing image is cloud distribution relative aggregation. On the basis, the discrete degree of the thin cloud can be accurately described, so that the discrete degree of the cloud in the image is detected.

The invention relates to the technical field of remote sensing image processing, in particular to a small beach shoreline accurate extraction method and system based on bare soil-water body double-index collaboration, and the method comprises the following steps: obtaining a multi-temporal Landsat surface reflectance image set which covers a target area and is subjected to radiometric calibration and wave band unified naming; obtaining a clean image set; generating a binary beach mask based on the surface reflectance of each pixel in the clean image set in a blue band, a green band, a red band, a near-infrared band and a short-wave infrared band; performing morphological closing operation on the binary beach mask to fill the fine holes to obtain a continuous mask; vectorizing and geometrically simplifying the continuous mask to obtain an initial beach face vector; and performing post-processing operation on the initial beach face vector to obtain a final beach boundary vector. According to the technical scheme, the beach can be effectively distinguished from adjacent water bodies, vegetation, buildings and other ground objects, and false lifting and missing lifting are remarkably reduced.

The invention discloses an underwater imaging method and system based on light spot distance measurement and a neural network, and belongs to the technical field of underwater imaging, and the method comprises the steps: S100, carrying an adjustable polaroid through underwater camera equipment, adjusting the angle of the polaroid, and collecting a multi-angle polarization image sequence of a target scene; s200, controlling the blue-green wave band laser to project a nonlinear light spot pattern to the target for distance measurement; s300, constructing a refraction correction model according to the refraction offset vector, and restoring the real form of the light spot; s400, fusing the spatial information of the multi-angle polarization image and the target distance data to generate a depth map; s500, registering the polarization texture features and the light spot geometric features based on a graph optimization algorithm, and minimizing a re-projection error; s600, inputting the registered features and the improved depth map into a pre-trained depth enhanced neural network; and S700, further post-processing optimization is carried out on the depth map output by the deep neural network based on a Transform network fusion algorithm. According to the invention, a finer absolute depth map can be provided.

The invention relates to the technical field of remote sensing images, and provides a multispectral remote sensing image tobacco vegetation index calculation method and an image optimization method, and the method comprises the following steps: inputting a multispectral remote sensing image; performing radiometric calibration and atmospheric correction on the multispectral image; acquiring reflectivity of a near-infrared wave band, a red wave band, a green wave band and a blue wave band; and calculating the tobacco vegetation index VIT based on the reflectivity of the near-infrared band, the red band, the green band and the blue band. The tobacco vegetation index VIT is suitable for a multispectral image and a hyperspectral image acquired by an unmanned aerial vehicle and a multispectral image acquired by a satellite, the tobacco vegetation is remarkably highlighted, the tobacco vegetation is remarkably distinguished from other vegetation, and the blank that the vegetation index does not aim at the tobacco plant is filled.

This invention provides a method and system for shallow water depth inversion based on multi-temporal satellite imagery guided by laser bathymetry. The method includes: extracting water body pixels from Sentinel-2 imagery; extracting water depth photons from satellite-borne laser bathymetry data and performing refraction correction to obtain water depth values ​​along the orbital direction; utilizing the strong correlation between the water depth values ​​along the orbital direction and the reflectivity of the blue and green bands of Sentinel-2 imagery to calculate multi-temporal image fusion weights and generate a weighted fused image; based on water column layer-by-layer inversion and / or seabed classification inversion strategies, dividing the weighted fused image into multiple homogeneous regions according to the water penetration capabilities of blue and green light; using LLM and / or LRM to invert water depth layer by layer in the multiple homogeneous regions; and fusing the results of different regions through a buffer zone to obtain the water depth inversion result for the entire shallow sea area. This invention can achieve high-precision and high-stability shallow water depth inversion.

The invention relates to a vegetation identification method and device based on an unmanned aerial vehicle image, a readable medium and a program, and the method comprises the steps: obtaining the unmanned aerial vehicle image of a to-be-identified region, and calculating a constructed RGBSI vegetation index based on the brightness values or reflectivity of the red light wave band, the green light wave band and the blue light wave band of the image, and extracting a vegetation region from the image of which the RGBSI vegetation index is calculated by using a threshold segmentation method. The RGBSI vegetation index constructed by the method combines the strong reflection of green vegetation in a green light wave band and the absorption characteristics of the green vegetation in red and blue wave bands, when the method is used for vegetation identification, an image histogram of a vegetation index grey-scale map image calculated by RGBSI has a good double-peak form under the condition of medium and high vegetation coverage, a threshold can be rapidly determined by using a maximum entropy threshold method, and the method can be applied to vegetation identification. And the threshold value is generally close to 0. The overall extraction precision of identifying the vegetation-containing image by using the RGBSI index is more than 90%, the extraction precision is high, and the extraction effect of vegetation with medium and high vegetation coverage is better.

The invention discloses a high-altitude vegetation degradation quantification method based on unmanned aerial vehicle remote sensing data, and belongs to the technical field of environment monitoring. The objective of the invention is to realize accurate quantification of high-altitude vegetation degradation. The method comprises the steps of collecting basic data of high-altitude vegetation based on unmanned aerial vehicle remote sensing; performing pixel-level vegetation image denoising enhancement on original multispectral data, generating pixel-level enhanced waveband data retaining vegetation features, quantifying textures through a ratio of a weighted sum to a global maximum value, and fusing blue-green waveband texture information to obtain pixel-level high-altitude self-adaptive vegetation index fused pixel-level topographic gradient; generating a pixel-level terrain-vegetation coupling degradation risk factor, fusing the pixel-level terrain-vegetation coupling degradation risk factor into pixel-level historical NDVI time sequence data, averagely quantifying the long-term trend of vegetation degradation through a sliding window, obtaining a pixel-level time sequence degradation trend factor, integrating the pixel-level time sequence degradation trend factor with a pixel-level high-altitude environmental factor, calculating a pixel-level environmental stress correction factor, and calculating a high-altitude environmental stress correction factor. According to the method, a complex driving mechanism for comprehensively analyzing high-altitude vegetation degradation is realized.

The invention provides a general product generation method and device based on a remote sensing satellite, equipment and a medium, and relates to the technical field of remote sensing data, and the method comprises the steps: obtaining remote sensing data of a target area through a geological resource environment hyperspectral microsatellite, the remote sensing data comprises blue wave band data, green wave band data, yellow wave band data, red light wave band data, red edge wave band data, near infrared wave band data and short wave infrared wave band data; determining a vegetation detection product according to the blue wave band data, the green wave band data, the red light wave band data, the red edge wave band data, the near infrared wave band data and the short wave infrared wave band data; according to the blue wave band data, the green wave band data, the red light wave band data, the near-infrared wave band data and the short-wave infrared wave band data, determining a water body and ice and snow detection product; and determining a geological exploration product according to the green band data, the yellow band data, the red band data, the near-infrared band data and the short-wave infrared band data. The product precision is the same.

A light-emitting structure and an illumination device. The light-emitting structure generates a full-spectrum illumination light source, and comprises at least one light-emitting diode chip (MC), the light-emitting diode chip (MC) comprises an N-type semiconductor layer (101), a P-type semiconductor layer (104), a first light-emitting layer (103), and a second light-emitting layer (102); the first light-emitting layer (103) is located on the side of the second light-emitting layer (102) close to the P-type semiconductor layer (104); the first light-emitting layer (103) generates at least one band of light by electroluminescence; the light generated by the first light-emitting layer (103) excites the second light-emitting layer (102) to generate at least one band of light; and each band of light comprises at least one wavelength. Bands corresponding to all light generated by the light-emitting structure at least comprise a blue band, a cyan band, a green band, a yellow band, and a red band.

The present invention provides a method and system for remote sensing detection of shallow-water multi-band bottom reflectance. This method combines four-band multispectral data and ICESat‑2 laser bathymetry data. By selecting characteristic points and iteratively searching for the optical parameters of the water body, the method uses the exponential relationship between the semi-analytical bottom reflectance and the spectral parameters to globally constrain the radiation transfer model, and constructs a quadratic polynomial model between the chlorophyll concentration of the characteristic points and the remote sensing reflectance of the blue and green bands, achieving large-scale rapid inversion of the optimal water body attenuation coefficient; further combined with the surface remote sensing reflectance product, a climatological monthly average deep-water reflectance data set is constructed, achieving large-scale and efficient detection of optical shallow-water multi-band bottom reflectance without the support of measured data. The present invention improves the application efficiency of four-band multispectral data, which has the richest historical data. The method is highly practical and has important significance for the protection and management of the benthic ecology of shallow-water coral reefs.

The present invention discloses an intelligent identification method for underwater dredging pipelines based on multi-temporal remote sensing images, including: obtaining multi-temporal remote sensing images of the same study area; extracting the blue band, green band and near-infrared band, or the blue band, green band and short-wave infrared band; calculating the optical remote sensing water body index and displaying it using a rainbow color map; determining the optimal threshold for water body segmentation of the optical remote sensing water body index; clarifying the water body and non-water body areas; using a Lee filter for image enhancement; using a custom convolution kernel for directional filtering enhancement; using a double-threshold sobel edge detection method to obtain an edge detection result map; superimposing the edge detection result map on the rainbow color map, patching the discontinuous parts at the middle position of the continuous line of the edge detection result map, and removing the debris non-segment edges to form a complete display map of the distribution of dredging pipelines; it can achieve efficient, large-scale scanning and precise positioning of the pipelines without disturbing the normal operation of the reservoir.

A color vision correction resin lens coating material and a preparation method thereof, the coating material comprising: an isocyanate compound, 30-40 parts by weight; a thiol compound, 15-30 parts by weight; polyvinyl butyral, 5-25 parts by weight; a photosensitive color-changing solution, 1-5 parts by weight; an initiator; a defoaming agent; a wetting agent. The photosensitive color-changing nanocomposite microspheres contained therein have a core composed of two spiropyran compounds and a shell coated with a modified acrylate material. The coating material has high flexibility and firmness; on the other hand, the coating material can automatically perform light filtering and color complementation, the color purity of the red and green band spectrum and the dimension of the color vision vector space are synergistically enhanced through the complementary color change of the two color-changing materials, the color vision abnormality is regulated, and the color discrimination ability is improved, thereby avoiding the over-reliance on one color caused by long-term wearing of color blindness glasses by color weak and color blind patients, maintaining visual quality, and having the advantages of high color saturation, good color complementation effect, comfortable wearing, etc.

This invention provides a dynamic monitoring technology for characteristic parameters of reservoirs in sediment-rich rivers based on remote sensing interpretation and multi-source data fusion, belonging to the field of water conservancy engineering monitoring technology. The technology includes the following steps: S1: Remote sensing image acquisition and preprocessing; S2: Water area extraction, fusing green band and shortwave infrared band, and calculating the improved normalized difference water index; S3: Constructing a water level inversion model, integrating preprocessed daily-scale measured water level data with the concurrent water area extracted in step S2, and dividing it into training and validation sets; S4: Calculating the dynamic water storage of the reservoir; S5: Constructing a sediment concentration inversion model, integrating preprocessed daily-scale measured sediment concentration data to construct a sediment concentration inversion model. This invention uses remote sensing interpretation to achieve automated, long-term, and large-scale continuous monitoring of water and sediment parameters in sediment-rich reservoirs, significantly improving monitoring efficiency; reducing manpower and material costs, and providing technological innovation for watershed-scale dynamic water and sediment perception.

The invention discloses a satellite green wave band water bottom trend normalization model inversion water depth aiming at water surface fluctuation, water body suspended matter scattering, non-uniform water bottom substrate reflection and non-linear noise superposition characteristics. According to the characteristics that low-resolution water depth data points are lack of underwater topographic data but can provide the underwater trend and water area satellite images are interfered by noise but have high-resolution remote sensing data, advantages are complementary, noise is suppressed, an underwater trend green band normalized image is constructed, and in combination with priori water depth point data, an underwater trend green band normalized image is obtained. And inverting the parameters of the linear water depth inversion model through a least square method to invert the water depth. Through verification of a Sailey island test area, compared with a single-band model (RMSE = 4.19 m, R = 0.79), a logarithmic ratio model (RMSE = 5.62 m, R = 0.62), a ratio logarithmic model (RMSE = 6.02 m, R = 0.56) and a multi-band model (RMSE = 3.88 m, R = 0.82), the method has the advantages that the RMSE is 1.40 m (the precision improvement rate is 63.9%-76.7%) and the R is 0.97, the water depth inversion precision is improved, and a new technology is provided for navigation safety and engineering construction application water depth acquisition.

This invention relates to the field of light-emitting diode (LED) technology, specifically disclosing a blue-green dual-band LED epitaxial wafer and its fabrication method, as well as an LED. The blue-green dual-band LED epitaxial wafer includes a substrate, on which a buffer layer, an N-type semiconductor layer, a stress relief layer, a green multi-quantum-well emitting layer, a quantum-well stress modulation layer, a blue multi-quantum-well emitting layer, an electron blocking layer, and a P-type semiconductor layer are sequentially disposed. The blue-green dual-band LED epitaxial wafer provided by this invention can simultaneously excite LED light sources in both blue and green bands, thereby reducing the cost of RGB-LED backlight sources and reducing the use of phosphors in white LED backlight sources.