13 results about "Medium resolution" patented technology

The present application relates to the technical field of remote sensing information extraction and snow monitoring, and particularly relates to a sub-pixel snow mapping method based on medium-resolution remote sensing data, comprising the following steps: obtaining medium-resolution remote sensing images, a digital elevation model, and ground meteorological station snow observation data, and preprocessing the remote sensing images to obtain surface reflectivity; identifying cloud-covered pixels, filling in the cloud-free dataset by temporal and spatial interpolation and ground object spectral similarity matching; selecting snow, vegetation, and water as spectral endmembers and extracting features; constructing a linear mixing model, and using the least square method to retrieve the snow proportion in the pixel to obtain preliminary data; and generating a high temporal and spatial accuracy snow distribution map through meteorological station data verification, digital elevation model terrain correction, and model parameter optimization, which solves the problems of mixed pixels, cloud coverage, terrain errors, and the lack of accuracy verification.

This invention discloses an intelligent detection method for ecological violations in water source protection areas based on multi-source heterogeneous remote sensing spatiotemporal collaborative perception. The method includes: acquiring time-series data of medium-resolution multispectral satellite imagery and high-resolution imagery data; calculating spectral indices and establishing ecological base fingerprints based on the medium-resolution time-series data, and extracting suspected change hotspots through time-series anomaly detection; employing a cross-scale guided Transformer to perform cross-attention fusion using high-resolution texture features as queries and medium-resolution spectral features as keys and values; using a semantic segmentation network with distance field constraints to perform pixel-level fine segmentation of the violations; and vectorizing the segmentation results and overlaying them with the protection area zoning for analysis, outputting compliance assessment results. This invention employs a cascade mechanism of macroscopic scanning-targeted acquisition-fine analysis, significantly reducing monitoring costs, achieving effective identification of camouflaged targets, supporting high-frequency, routine integrated land-water monitoring, and meeting the operational needs of hierarchical management of water source protection areas.

A computer-implemented method for generating synthetic mammographic images using a modified diffusion model has as novelty three training phases in which modified diffusion is performed, namely: in a second stage 200 of local context generations, over an image representing a third channel, in a step 208 of adding noise to the image patch, and in a step 209 of training the neural network to remove the noise, and also modified diffusion is performed in a third phase 300 of generating full-resolution patches, in a step 308 of denoising the image patch, and in a step 310 of training a neural network to remove noise. Also, the method includes working with patches in the image in the second phase 200 in steps 204-210 and in a third phase 300 in steps 304-311. The method includes training in phase 100 on an image representing a single channel, a global context, with steps 101-106, then training over the images representing the first, second, and third channel: in phase 200 with steps 201-210 and in phase 300 with steps 301-311 and finally phase 400 of generating a full-resolution mammogram image. Phase 400 includes: subphase 401 where noise is programmatically generated and in step 403 the neural network removes it and provides an output image at a resolution of 256x256; subphase 405 with step 406 where the input image from the first phase is loaded and steps 407 are then performed-411; step 412 of integrating patches with local context in a way that ensures smooth transitions during integration, which is an innovative step; then subphase 413 where, in step 414, the medium-resolution image obtained by integrating the patches from the second phase 200 is loaded, after which steps 415-420 are performed. then step 421 of integrating the patches in the image follows, and finally, the full-resolution output image is obtained in step 422. The patch integration in step 412 is innovative and ensures smooth transitions in the image.

The embodiment of the application provides a kind of river and lake shore zone remote sensing monitoring method and product, the river and lake shore zone remote sensing monitoring method includes: obtaining historical medium resolution remote sensing image data and historical high resolution remote sensing image data;From the historical medium resolution remote sensing image data, the end-member spectrum of typical ground object is extracted;The end-member spectrum is corrected according to the high resolution remote sensing image data and the interference of shadow or cloud layer is eliminated, and the target end-member spectrum matrix is obtained;According to the target end-member spectrum matrix, pixel unmixing is carried out on the medium resolution remote sensing image data to be decomposed, and the pixel unmixing result is obtained;According to the pixel unmixing result and water flooding frequency, water level amplitude area is determined;The shore zone monitoring range is determined according to the water level amplitude area;The image data corresponding to the shore zone monitoring range is input into wavelet transform U-Net deep convolutional neural network model, and the shore zone land use classification result is obtained;According to the shore zone land use classification result, the ecological shore zone ratio result of each river section is quantified.

The application relates to a satellite image-based cross-domain ground object classification fine-tuning sample automatic selection method, which comprises the following steps: obtaining an initial interpretation model, to-be-interpreted images and medium-resolution ground surface cover classification results; obtaining an interpretation result and an interpretation category probability distribution; constructing a high-value sample selection framework; selecting candidate images from the to-be-interpreted images as a to-be-labeled sample set according to a sample screening strategy, artificially labeling the candidate samples to obtain a fine-tuning sample set; fine-tuning the initial interpretation model according to the fine-tuning sample set and model evaluation, and obtaining an optimal interpretation model; and obtaining a final fine-tuning sample set after a screening criterion is met. On the basis of reasonably utilizing an existing model, the application constructs diversified, representative and differentiated fine-tuning samples through ground surface cover prior knowledge, fully utilizes the prior knowledge, reduces the input of manpower and material resources, and can better complete a current satellite remote sensing image ground object classification task.

The application provides a super-structured optical fiber for generating a spindle beam and an optical acoustic endoscopy imaging system, the super-structured optical fiber comprising a fiber body and a super-structured optical structure arranged at an exit section of the fiber body, the super-structured optical structure comprising an optical expander and a super-structured structure array arranged at the end of the optical expander; the super-structured structure array is periodically arranged by super-structured units in a sub-wavelength scale, and the super-structured unit is in the form of a regular quadrangular prism; by clear phase modulation and parameter optimization, the super-structured optical structure is integrated at the end face of the fiber body, the emission light wave front is accurately regulated, a long focal depth spindle beam is obtained under the condition of a limited aperture and a compact structure, and the problem that the focal depth is limited in optical acoustic endoscopy imaging is effectively relieved; based on the combination of Bessel beams and the lateral spot size, the application maintains a high lateral resolution ability while extending the focal depth, avoids the defect that the resolution is significantly reduced in the traditional zooming scheme, and improves the imaging consistency and the signal-to-noise ratio.

This application discloses a method and device for monitoring a near-infrared band stable pixel simulated reference source. The method includes the following steps: performing a two-layer screening of primary data from a medium-resolution spectral imager, involving day / night determination and stable pixel site matching, to obtain target observation data; selecting atmospheric reanalysis data as the background field, completing spatiotemporal matching with the target observation data, and performing interpolation preprocessing; inputting the preprocessed atmospheric reanalysis data into a radiative transfer model, and performing synchronous reflectance simulation based on the screened stable pixel sites to obtain simulated reflectance data; calculating the average relative deviation between the observed reflectance and the simulated reflectance, and performing multi-dimensional statistical monitoring and accuracy evaluation on the average relative deviation.

The application discloses a terrain correction-based multi-modal remote sensing tea garden segmentation and area estimation method and system and a medium. The method comprises the following steps: constructing a multi-modal time series dataset, which comprises high-resolution remote sensing image data, medium-resolution time series remote sensing image data and digital elevation model data; inputting the dataset into a terrain correction-based multi-modal tea garden segmentation model, extracting time series phenology aggregation features through a lightweight time series attention encoder, introducing a learnable C correction coefficient through a terrain attention correction module to correct terrain by wave band and fuse the core spatial features, fusing the features through a multi-modal feature fusion module, outputting a tea garden area segmentation result through a multi-scale segmentation module; and correcting terrain projection distortion based on the segmentation result and slope information to obtain a corrected total tea garden area. The application realizes accurate image recognition and area estimation of tea gardens under complex terrain.

This invention discloses a method and system for clear sky synthesis of FY-3D medium-resolution imager images. First, FY-3D MERSI time-series image data is preprocessed, and a time-series clear sky / cloud mask is obtained through fully automated cloud detection based on time-series analysis. Then, based on object-oriented principles, the clear sky / cloud mask is segmented to obtain clear sky and cloud mask objects. The cloud mask objects are then synthesized with FY-3D MERSI time-series data that has undergone radiometric consistency processing to create a clear sky image. Finally, the synthesized clear sky image undergoes post-processing including feathering, color enhancement, and texture enhancement to obtain the final clear sky image. This invention enables fully automated clear sky synthesis of images and boasts high parallel processing efficiency.