Multi-level optimization method for remote sensing image thumbnail self-generation

Abstract

The invention discloses a multi-level optimization method for remote sensing image thumbnail self-generation, and the method comprises the following steps: S1, analyzing remote sensing image metadata, obtaining four-point coordinate information, and obtaining coordinates of a lower left corner and an upper right corner through calculation according to radiation parameters; s2, unifying spatial resolution, and performing zooming processing on the remote sensing image according to specified pixels to obtain target pixels; s3, light common channel correction is carried out; s4, reading remote sensing image waveband data; s5, removing a black edge; and S6, fusing multiple wavebands, and rendering a thumbnail. According to the method, multi-channel remote sensing image data is processed, and a thumbnail generation process is automatically adjusted and optimized so as to adapt to pixel value ranges and multi-channel characteristics of different data sources. The thumbnail can quickly provide an overall view of the image, so that a user is helped to quickly browse and screen large-scale image data, and the loading and processing time is saved.

Description

Technical Field

[0001] The invention relates to the fields of remote sensing technology and image processing, and in particular to a multi-level optimization method for self-generating remote sensing image thumbnails. Background Art

[0002] With the development of computer technology, computer processing technology has been increasingly used in remote sensing image processing. After the optical image is converted into a digital image, or after the digital remote sensing image is directly obtained through the remote sensing sensor, the remote sensing image data can be processed by computer. This processing technology is called remote sensing image digital processing method. The digital processing method is simple to operate and can easily build a remote sensing image processing system that meets specific processing tasks. At the same time, it can also be seamlessly integrated with other computer systems (such as geographic information systems and GPS systems) to form a comprehensive application of 3S technology.

[0003] The main feature of remote sensing images is that they are large. Generally, a picture is 5-10GB, and a single satellite adds TBs every day, and the world adds PBs every day. As the accuracy of satellite sensors increases, single image files are getting larger and larger. Remote sensing image data usually has high resolution and multi-channel characteristics, which brings the problem that it is not easy to preview and view; Since different data sources may have different pixel value ranges and data differences, traditional thumbnail generation methods often cannot effectively handle these differences, resulting in inconsistencies in the visualization and information expression of the generated thumbnails. Therefore, it is necessary to develop a new technology that can efficiently generate thumbnails that adapt to different data differences. Summary of the invention

[0004] In order to solve the existing problems, the present invention provides a multi-level optimization method for self-generation of remote sensing image thumbnails. The specific scheme is as follows: A multi-level optimization method for self-generating remote sensing image thumbnails includes the following steps: S1, analyze remote sensing image metadata, obtain four-point coordinate information, and calculate the coordinates of the lower left corner and upper right corner according to the radiation parameters; S2, unify the spatial resolution and scale the remote sensing image according to the specified pixels to obtain the target pixels; S3, perform optical channel calibration; S4, read remote sensing image band data; S5, remove black edges; S6, fuses multiple bands and renders thumbnails.

[0005] Preferably, in step S2, since it is uncertain whether the remote sensing image is longer in X or Y, compatibility processing is required during scaling to ensure that the pixel value after scaling is within the specified pixel range.

[0006] Preferably, step S5 reads the pixel values ​​of tile positions corresponding to different bands through gdal.ReadRaster, then merges the pixel values ​​of different bands into one image, and fills in the missing ones with a transparent background to solve the black edge problem.

[0007] The present invention also discloses a computer-readable storage medium, wherein a computer program is stored on the medium, and after the computer program is run, any of the above methods is executed.

[0008] The present invention also discloses a computer system, including a processor and a storage medium, wherein a computer program is stored on the storage medium, and the processor reads and runs the computer program from the storage medium to execute any of the methods described above.

[0009] The beneficial effects of the present invention are: Remote sensing image data comes from different sensors and platforms, which may have different pixel value ranges, resolutions, spectral channels and other characteristics. Automatically analyze the pixel values ​​of remote sensing images, identify the data differences and features in the images, and dynamically adjust the pixel value settings based on the analysis results to adapt to different data ranges and channel characteristics, achieve adaptive adjustment to different data differences, and ensure that the generated thumbnails can accurately reflect the multi-channel information of the image.

[0010] Use an optimized thumbnail generation algorithm to map the adjusted pixel values ​​to the specified thumbnail size and resolution, and adopt fast calculation technology to ensure that the thumbnail generation process is efficient and resource-intensive.

[0011] Design an adaptive adjustment module to adjust the processing methods of different data sources and channels in real time. The characteristics of remote sensing image data may change over time and conditions, such as different lighting conditions, sensor settings, etc. New technologies can provide automated and adaptive adjustment functions to automatically adjust the processing methods according to the actual characteristics of the image, thereby generating more accurate thumbnails.

[0012] In short, the multi-level optimization technology of remote sensing image thumbnail self-generation to generate thumbnails that adapt to different data differences can not only improve the efficiency of data processing, but also ensure the consistency of the generated thumbnails in visual and information expression. It greatly improves the user experience when browsing and analyzing remote sensing images, saves time and improves work efficiency, so as to better meet the needs of practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A schematic diagram of a unified thumbnail resolution of the present invention; Figure 2 A thumbnail before black edges are removed in an embodiment of the present invention; Figure 3 This is a thumbnail with black edges removed in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] The present invention relates to a data processing technology based on remote sensing images, aiming to efficiently self-generate thumbnails and adapt to different pixel value settings and data differences.

[0017] This method processes multi-channel remote sensing image data and automatically adjusts and optimizes the thumbnail generation process to adapt to the pixel value range and multi-channel characteristics of different data sources. The main purpose of generating remote sensing image thumbnails is to improve data processing efficiency and visualization effects. Thumbnails can quickly provide an overall view of the image, help users quickly browse and filter large-scale image data, and save loading and processing time.

[0018] Specifically, the present invention provides an algorithm that can automatically select and adjust appropriate pixel value settings according to the characteristics of the input image, and perform efficient thumbnail generation to ensure the consistency of the visualization effect and information integrity of the thumbnail under different data conditions. The efficient self-generated thumbnail technology can greatly enhance the user experience when browsing and analyzing remote sensing images. Users can preview and select areas of interest more quickly, and then conduct detailed analysis, which improves the efficiency of data processing, and can also ensure the consistency of the generated thumbnails in visual and information expression, so as to better meet the needs of practical applications.

[0019] The present invention is applicable to multiple fields such as visualization of remote sensing image data, geographic information system (GIS), environmental monitoring, agricultural monitoring, etc., and is particularly important when large-scale multi-channel remote sensing data needs to be processed.

[0020] The core technology of the patent of this invention lies in: 1. It can generate thumbnails of specified resolution for image data with different pixel value ranges and resolutions; 2. It can unify the algorithm for image data of different bands and channels to ensure consistent visualization effect and information integrity. The specific process steps of the method are as follows: A multi-level optimization method for self-generating remote sensing image thumbnails includes the following steps: S1, analyze the remote sensing image metadata, obtain the four-point coordinate information, and calculate the coordinates of the lower left corner and the upper right corner according to the radiation parameters; the calculation formula is as follows: / / Affine parameters (six-parameter array) double[]geoTransform =dataset.GetGeoTransform(); / / Set the alliance slice size and geographic range double minX = geoTransform[0]; double maxX= minX + dataset.getRasterXsize()* geoTransform[1]; double maxY = geoTransform[3]; double minY = maxY + dataset.getRasterYsize()* geoTransform[5]; S2, unify the spatial resolution, scale the remote sensing image according to the specified pixels to obtain the target pixel; since it is uncertain whether the remote sensing image is longer in X or Y, it is necessary to process it in a compatible way during scaling to ensure that the pixel value after scaling is within the specified pixel range. Figure 1 As shown, it is a schematic diagram of unifying the resolution of the scaled image.

[0021] S3, perform optical path calibration.

[0022] Remote sensing images generally include information of different bands such as red, green, and blue. In color remote sensing images, these bands are combined in a specific way to present a color image. In most cases, single-band images are black and white images, and multi-band images are color images. The common color models are: The RGB color model is a way of representing colors using three color channels: red, green, and blue. It is the most commonly used color model in computers. CMYK color is a color model based on printing ink color, where C stands for cyan, M stands for magenta, Y stands for yellow, and K stands for black. This color model is mainly used in the field of printing and printing; The grayscale color model is a color model that only has brightness information and is used to represent black and white images or convert color images into black and white images.

[0023] The color description of the band can be obtained through band.GetColorInterpretation() to obtain the corresponding color type. The corresponding values ​​are as follows: GCI_GrayIndex: Independent grayscale image - 1 GCI_RedBand: Red band of RGB or RGBA image - 3 GCI_GreenBand: Green band of RGB or RGBA image - 4 GCI_BlueBand: Blue band of RGB or RGBA image - 5 GCI_AlphaBand: Alpha band of RGB or RGBA image - 6 GCI_CyanBand: Cyan band of CMY or CMYK image - 10 GCI_MagentaBand: Magenta band of CMY or CMYK image - 11 GCI_YellowBand: Yellow band of CMY or CMYK image - 12 Since RGB is the three primary colors, different band types need to be mapped to the three colors of RGB. The mapping logic is as follows:

[0024] S4, read remote sensing image band data.

[0025] Specifically, gdal.ReadRaster is one of the methods used to read raster data in the GDAL (Geospatial Data Abstraction Library) library. GDAL is an open source library for reading, writing, and processing geospatial data, supporting remote sensing image data in multiple formats.

[0026] The gdal.ReadRaster method is used to read the pixel values ​​of a specified area from a dataset. Its basic syntax is: data = dataset.ReadRaster(offsetX, offsetY, width, length) Among them, dataset is the dataset object to read data from. offsetX and offsetY are the offsets of the starting pixel to read data from. When getting thumbnails, they are fixed to 0. width and length are the pixel width and length of the data to be read. The width and height after scaling have been calculated above.

[0027] This method returns the pixel values ​​of remote sensing images in different bands.

[0028] S5, remove the black edges. Use gdal.ReadRaster to read the pixel values ​​of the tile positions corresponding to different bands, then merge the pixel values ​​of different bands into one image, and fill in the missing transparent background to solve the black edge problem. The scaled image before removing the black edges is shown in the figure below. Figure 2 As shown; the thumbnail after removing the black border is as follows Figure 3 shown.

[0029] S6, merge multiple bands and render thumbnails, that is, write the image pixels of the merged bands into the picture, scale it to a specified size, and use it for browsing.

[0030] / / Create an image object BufferedImage image = new BufferedImage(titlesize, titlesize,BufferedImage.TYPE_INT_ARGB); / Write pixel values ​​to image image,setRGB(startX, startY, titlesizeX, titlesizeY, rgbImage, 0,titlesizeX).

[0031] The present invention is based on data processing technology of remote sensing images, and aims to efficiently generate thumbnails and adapt to different pixel value settings and data differences.

[0032] Remote sensing image data comes from different sensors and platforms, and these data may have different pixel value ranges, resolutions, spectral channels and other characteristics. The present invention automatically analyzes the pixel values ​​of remote sensing images, identifies the data differences and features in the images, and dynamically adjusts the pixel value settings according to the analysis results to adapt to different data ranges and channel characteristics, thereby achieving adaptive adjustment to different data differences and ensuring that the generated thumbnails can accurately reflect the multi-channel information of the images.

[0033] At the same time, the present invention uses an optimized thumbnail generation algorithm to map the adjusted pixel values ​​to the specified thumbnail size and resolution, and adopts fast calculation technology to ensure that the thumbnail generation process is efficient and minimizes resource usage.

[0034] The present invention also has an adaptive adjustment module to adjust the processing methods of different data sources and channels in real time. The characteristics of remote sensing image data may change over time and conditions, such as different lighting conditions, sensor settings, etc. The new technology can provide automated and adaptive adjustment functions to automatically adjust the processing method according to the actual characteristics of the image, thereby generating more accurate thumbnails.

[0035] In short, the multi-level optimization technology of remote sensing image thumbnail self-generation to generate thumbnails that adapt to different data differences can not only improve the efficiency of data processing, but also ensure the consistency of the generated thumbnails in visual and information expression. It greatly improves the user experience when browsing and analyzing remote sensing images, saves time and improves work efficiency, so as to better meet the needs of practical applications.

[0036] The present invention also discloses a computer-readable storage medium and a computer system. A computer-readable storage medium stores a computer program, and after the computer program is run, the method described above is executed. A computer system includes a processor and a storage medium, wherein the storage medium stores a computer program, and the processor reads and runs the computer program from the storage medium to execute the method described above.

[0037] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. The technician may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0038] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0039] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0040] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, a server, or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0041] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

[0042] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-level optimization method for self-generation of remote sensing image thumbnails, characterized in that: The following steps are involved: S1, analyze remote sensing image metadata, obtain four-point coordinate information, and calculate the coordinates of the lower left corner and upper right corner according to the radiometric parameters; S2, unify the spatial resolution and scale the remote sensing image according to the specified pixels to obtain the target pixels; S3, perform optical channel calibration; S4, read remote sensing image band data; S5, remove black edges; S6, fuses multiple bands and renders thumbnails.

2. The method according to claim 1, characterized in that: In step S2, since it is uncertain whether the remote sensing image is longer in X or Y, compatibility processing must be performed during scaling to ensure that the pixel value after scaling is within the specified pixel range.

3. The method according to claim 1, characterized in that: Step S5 reads the pixel values ​​of the tile positions corresponding to different bands through gdal.ReadRaster, then merges the pixel values ​​of different bands into one image, and fills in the missing ones with a transparent background to solve the black edge problem.

4. A computer-readable storage medium, characterized in that: A computer program is stored on the medium, and after the computer program is run, the method according to any one of claims 1 to 3 is executed.

5. A computer system, characterized in that: The method comprises a processor and a storage medium, wherein a computer program is stored in the storage medium, and the processor reads and runs the computer program from the storage medium to execute the method as claimed in any one of claims 1 to 3.

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