CN112800992A - Extraction method based on remote sensing data high-temperature abnormal information - Google Patents
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Abstract
The invention discloses a method for extracting high-temperature abnormal information based on remote sensing data, which comprises the following steps: taking remote sensing data, screening, and performing data splicing on the screened data; calculating the maximum value of the temperature; projection conversion; performing grid operation; adjusting a resolution of the generated raster data; and performing neighborhood analysis, cluster statistical analysis, analysis and filtration and format conversion on the grid data. The method is used for rapidly generating large-area high-temperature abnormal data based on a large amount of real-time historical remote sensing data and providing basic analysis data for fire monitoring and early warning; compared with the traditional manual on-site acquisition method, the method has the advantages of rapid generation, wide range and historical data reproduction.
Description
Technical Field
The invention belongs to the technical field of remote sensing data, and particularly relates to a method for extracting high-temperature abnormal information based on remote sensing data.
Background
The remote sensing image processing is a technology for performing a series of operations such as radiation correction and geometric correction, image finishing, projection transformation, mosaic, feature extraction, classification, various thematic processing and the like on a remote sensing image so as to achieve the expected purpose. The technique of using a computer to perform a series of operations on a remotely sensed digital image to obtain a certain desired result is called remote sensing digital image processing.
The remote sensing image automatic interpretation is that the identification and classification result of the ground object target is automatically output through computer processing according to the difference and change of the data characteristics of the remote sensing image, and the method is the specific application of the computer mode identification technology in the field of remote sensing.
The high temperature abnormal point is one of the main manifestations of abnormal disaster information. The remote sensing technology has the characteristics of large-range observation and high space-time resolution, can accurately describe the earth surface process, and can make up the defects of informatization and spatialization of statistical data. However, an extraction method of abnormal information in the remote sensing information is lacked, so that accuracy and effectiveness of fire risk assessment cannot be guaranteed.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for extracting high-temperature abnormal information from remote sensing data, comprising the steps of:
s1, taking remote sensing data, screening and splicing the screened data;
s2, calculating the maximum value of the temperature by adopting a wave band operation tool;
s3, performing projection conversion on the generated maximum value data, and cutting projected data according to the boundary vector data of the target area range;
s4, performing grid operation on the cut data to obtain preliminary high-temperature abnormal data;
s5, adjusting the resolution of the generated raster data to optimize the high-temperature abnormal data;
s6, performing neighborhood analysis on the raster data;
s7, performing cluster statistical analysis on the data, and aggregating the pixels with the same value and adjacent pixels into a pattern spot;
s8, analyzing and filtering to generate optimized high-temperature abnormal grid data;
and S9, performing format conversion on the raster data to obtain high-temperature abnormal vector data.
The invention has the beneficial effects that: the method is used for rapidly generating large-area high-temperature abnormal data based on a large amount of real-time historical remote sensing data and providing basic analysis data for fire monitoring and early warning; compared with the traditional manual on-site acquisition method, the method has the advantages of rapid generation, wide range and historical data reproduction.
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FIG. 1 is a flow diagram of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
as shown in the attached figure 1, the method for extracting the high-temperature abnormal information based on the remote sensing data comprises the following steps:
s1, taking remote sensing data, screening and splicing the screened data;
s2, calculating the maximum value of the temperature by adopting a wave band operation tool;
s3, performing projection conversion on the generated maximum value data, and cutting projected data according to the boundary vector data of the target area;
s4, performing grid operation on the cut data to obtain preliminary high-temperature abnormal data;
s5, adjusting the resolution of the generated raster data to optimize the high-temperature abnormal data;
s6, performing neighborhood analysis on the raster data;
s7, performing cluster statistical analysis on the data, and aggregating the pixels with the same value and adjacent pixels into a pattern spot;
s8, analyzing and filtering to generate optimized high-temperature abnormal grid data;
and S9, performing format conversion on the raster data to obtain high-temperature abnormal vector data.
Specifically, the projection conversion of the generated maximum value data in S3 employs a behrmann projection.
Specifically, the specific method for performing the grid operation on the clipped data in S4 is as follows: and assigning a value smaller than the first set value to be 0, and assigning a value larger than or equal to the second set value to be 1 to obtain preliminary high-temperature abnormal data.
Specifically, the specific process of analyzing and filtering to generate the optimized high-temperature abnormal grid data is as follows: and (4) performing filtering analysis by adopting a sieve tool, setting the data which is smaller than the first pixel value after clustering statistics to be 0, and generating optimized high-temperature abnormal grid data.
The specific implementation process of the invention is as follows:
s1, selecting the remote sensing data MCD64A1 month between 3, month 15 and 7, month 15 and 2015-2019, screening the remote sensing data of the month including a target area such as a province, and performing data splicing on the screened temperature remote sensing data according to the month by adopting an MRT tool;
s2, adopting a wave band calculation tool of ERDAS software, and converting the lattice value into a floating point type numerical value according to a calculation formula float (b1> b2> b3> b4> b5 >) > bn, wherein n is the number of data; bn is the value of the temperature remote sensing data, and the maximum grid of the calculated temperature is set as max;
s3, performing projection conversion on the calculated data, adopting behrmann projection in the embodiment, and cutting the projected data according to the boundary vector data of the target area;
and S4, performing raster operation on the clipped data: assigning a value less than 1 to 0, and assigning a value greater than or equal to 1 to obtain preliminary high-temperature abnormal data;
s5: data optimization processing, namely changing the resolution of raster data generated by raster operation by adopting a sample tool, and improving the resolution from 500 meters to 100 meters;
s6: adopting a neighborwood tool, selecting 7x7 in size, ignoring 0 value statistics, and performing field analysis;
s7, clustering statistical analysis is carried out by using a column tool, and pixels with the same value and adjacent pixels are aggregated into a pattern spot; eliminating the influence of noise of remote sensing data, eliminating undersized areas and combining adjacent pixels with the same value.
S8, adopting a sieve tool for filtering analysis, setting the data after the cluster statistics to be less than 500 pixel values to be 0, and generating optimized high-temperature abnormal grid data;
and S9, converting the high-temperature abnormal raster data into an shp format to obtain high-temperature abnormal vector data.
The method solves the problem of rapid extraction of large-range high-temperature abnormal data, calculates the maximum value of monthly high-temperature fire point data through a wave band operation tool, and generates the grid data of a high-temperature abnormal area through the maximum temperature grid data; and GIS analysis optimization is carried out on the preliminarily generated high-temperature abnormal grid data, so that the accuracy of monitoring the fire points in the high-temperature abnormal area is improved.
The invention can improve the speed and objectivity of extracting information from remote sensing data, quickly extract high-temperature abnormal data in real time and provide basic spatial analysis data for fire monitoring and early warning. And the change detection and the abnormal information identification are realized through the feature parameters of the ground features extracted from the long-time sequence remote sensing images. Compared with the traditional manual on-site acquisition method, the method has the advantages of rapid generation, wide range and historical data reproduction.
The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.
Claims (4)
1. The method for extracting the high-temperature abnormal information based on the remote sensing data is characterized by comprising the following steps of:
s1, taking remote sensing data, screening and splicing the screened data;
s2, calculating the maximum grid value of the temperature by adopting a wave band operation tool;
s3, projection conversion is carried out on the generated maximum value data, and the maximum value data after projection is cut according to the vector data of the target area range;
s4, performing grid operation on the clipped maximum value data to obtain preliminary high-temperature abnormal data;
s5, adjusting the resolution of the generated raster data to optimize the high-temperature abnormal data;
s6, performing neighborhood analysis on the raster data;
s7, performing cluster statistical analysis on the data, and aggregating the pixels with the same value and adjacent pixels into a pattern spot;
s8, analyzing and filtering to generate optimized high-temperature abnormal grid data;
and S9, performing format conversion on the raster data to obtain high-temperature abnormal vector data.
2. The method for extracting high-temperature abnormal information based on remote sensing data according to claim 1, wherein the projection transformation of the generated maximum value data in the step S3 adopts behrmann projection.
3. The method for extracting high-temperature abnormal information based on remote sensing data according to claim 1, wherein the specific method for performing grid operation on the cut data in the step S4 is as follows: and assigning a value smaller than the first set value to be 0, and assigning a value larger than or equal to the second set value to be 1 to obtain preliminary high-temperature abnormal data.
4. The method for extracting high-temperature abnormal information based on remote sensing data according to claim 1, wherein the specific process of analyzing and filtering and generating the optimized high-temperature abnormal raster data is as follows: and (4) performing filtering analysis by adopting a sieve tool, setting the data which is smaller than the first pixel value after clustering statistics to be 0, and generating optimized high-temperature abnormal grid data.
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| US20190114745A1 (en) * | 2016-04-01 | 2019-04-18 | Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences | Extraction method for time-space-spectrum four-dimensional remote sensing data |
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