Remote sensing image quality determination method and system

Abstract

The invention discloses a remote sensing image quality judgment method. The method comprises the steps of obtaining a to-be-judged remote sensing image; carrying out band division on the to-be-judged remote sensing image; calculating a pixel average deviation index, a pixel repetition interval index, a highlight pixel value index and a noise index of each wave band; calculating a comprehensive quality evaluation index of the to-be-judged remote sensing image according to the obtained index; and performing quality judgment on the to-be-judged remote sensing image according to the comprehensive quality evaluation index. The invention also discloses a system for realizing the sensory image quality judgment method. According to the method, multi-aspect comprehensive influence calculation is carried out on the pixel value of the remote sensing image, so that judgment of the quality of the remote sensing image is realized, the subjectivity problem of manual judgment is solved, judgment of the quality of the remote sensing image is realized, and the method is higher in reliability, better in accuracy, more objective and more scientific.

Description

Technical Field

[0001] The invention belongs to the field of remote sensing technology, and in particular relates to a remote sensing image quality determination method and system. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] At present, remote sensing images are widely used in many aspects of power system line planning, line inspection, line early warning, etc.; therefore, ensuring the reliability of remote sensing images is of great significance to the power system.

[0004] The quality of remote sensing images is the key to remote sensing imaging technology. Once remote sensing images that do not meet the quality requirements are used in corresponding technical occasions, it may lead to the pollution of original data, thereby making the effect of remote sensing imaging technology worse, and ultimately affecting the safe and stable operation of the power system.

[0005] At present, the traditional remote sensing image quality determination scheme often still adopts the method of manual sampling or manual inspection, that is, the quality of the acquired remote sensing images is subjectively determined by manual determination or manual sampling. However, it is obvious that this detection scheme is not only time-consuming and laborious, but also has poor reliability and strong subjectivity. Summary of the invention

[0006] One of the purposes of the present invention is to provide a remote sensing image quality determination method which is highly reliable, accurate, objective and scientific.

[0007] A second objective of the present invention is to provide a system for implementing the remote sensing image quality determination method.

[0008] The remote sensing image quality determination method provided by the present invention comprises the following steps:

[0009] S1. Obtaining remote sensing images to be determined;

[0010] S2. dividing the remote sensing image to be determined obtained in step S1 into bands;

[0011] S3. For each band divided in step S2, the pixel average deviation index, pixel repetition interval index, highlight pixel value index and noise index of each band are calculated respectively;

[0012] S4. Calculate the comprehensive quality assessment index of the remote sensing image to be determined according to the index calculated in step S3;

[0013] S5. According to the comprehensive quality evaluation index obtained in step S4, the quality of the remote sensing image to be judged is judged.

[0014] Step S2 of dividing the remote sensing image to be determined obtained in step S1 into bands specifically includes the following steps:

[0015] Set the background fill of the remote sensing image to be judged to 0;

[0016] Set the valid pixel value of the remote sensing image to be determined to be between 1 and 255;

[0017] The acquired remote sensing images to be judged are divided into four bands, namely B1, B2, B3 and B4.

[0018] The pixel average deviation index described in step S3 is specifically calculated using the following steps:

[0019] Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively;

[0020] The pixel value interval of the current band is divided into the following five intervals: interval 1-[1,49], interval 2-[50,120], interval 3-[121,150], interval 4-[151,200] and interval 5-[201,255];

[0021] Calculate the average value of all valid pixel values ​​in the current band Bi

[0022] Determine the average DN n The interval in which it is located, and the corresponding pixel average deviation index A is calculated i :

[0023] If the average is in interval 1, then A i =2;

[0024] If the average is in interval 2, then A i =1;

[0025] If the average is in interval 3, then A i =3;

[0026] If the average is in interval 4, then A i =4;

[0027] If the average is in interval 5, then A i =5.

[0028] The pixel repetition interval index described in step S3 is specifically calculated using the following steps:

[0029] Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively;

[0030] Divide 1 to 255 into 26 statistical intervals, where the first interval is [1,10], the second interval is [11,20], the third interval is [21,30], ..., the 25th interval is [241,250], and the 26th interval is [251,255];

[0031] Count the percentage of pixels in each interval to the total number of valid pixels in the current band. i , the value of i ranges from 1 to 26;

[0032] Get p i The maximum value p imax , and make a judgment:

[0033] If p imax ≥T1, then set the judgment percentage p j For p j =p imax ;

[0034] If p imax <T1, then set the judgment percentage p j For p j =p1+p 26 ; T1 is the set threshold;

[0035] According to the obtained judgment percentage P j The corresponding pixel repetition interval index C is calculated i :

[0036] If P j ∈[0%,2%), then C i =1;

[0037] If P j ∈[2%,5%), then C i =2;

[0038] If P j ∈[5%,10%), then C i =3;

[0039] If P j ∈[10%,15%), then C i =4;

[0040] If P j ∈[15%,100%], then Ci =5.

[0041] The highlight pixel value index described in step S3 is specifically calculated using the following steps:

[0042] Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively;

[0043] Count the number of pixels W with valid pixel values ​​greater than 220 in the current band Bi i ;

[0044] Calculate the number of pixels W i The percentage of all valid pixels in the current band Bi i :

[0045] According to the calculated percentage Pr i , calculate the corresponding highlight pixel value index M i :

[0046] If Pr i ∈[0%,1%), then M i =1;

[0047] If Pr i ∈[1%,2%), then M i =2;

[0048] If Pr i ∈[2%,5%), then M i =3;

[0049] If Pr i ∈[5%,15%), then M i =4;

[0050] If Pr i ∈[15%,100%], then M i =5.

[0051] The noise index described in step S3 is specifically calculated using the following steps:

[0052] Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively;

[0053] Construct the second-order difference operator Arr as

[0054] Calculate the convolution result Q of the effective pixel value in the current band Bi and the second-order difference operator Arr i ;

[0055] Statistical convolution result Qi The number of pixels whose absolute value is greater than 3 Psr i ;

[0056] According to the statistical pixel number Psr i , calculate the corresponding noise index L i :

[0057] If Psr i ∈[0%,2%), then L i =1;

[0058] If Psr i ∈[2%,5%), then L i =2;

[0059] If Psr i ∈[5%,8%), then L i =3;

[0060] If Psr i ∈[8%,15%), then L i =4;

[0061] If Psr i ∈[15%,100%], then L i =5.

[0062] The step S4 calculates the comprehensive quality assessment index of the remote sensing image to be determined based on the index calculated in step S3, and specifically includes the following steps:

[0063] Calculate the quality assessment coefficient R of the i-th band Bi of the remote sensing image to be judged i For R i =A i +C i +M i +L i ;

[0064] According to the quality evaluation coefficient R of Bi of the i-th band i , the comprehensive quality evaluation index R of the remote sensing image to be judged is calculated to be R=R1+R2+R3+R4.

[0065] Step S5, according to the comprehensive quality assessment index obtained in step S4, performs quality assessment on the remote sensing image to be assessed, and specifically includes the following steps:

[0066] According to the comprehensive quality evaluation index obtained in step S4, the quality of the remote sensing image to be judged is judged using the following rules:

[0067] If R∈[16,30), the quality of the remote sensing image to be judged is high;

[0068] If R∈[30,45), the quality of the remote sensing image to be judged is judged to be medium;

[0069] If R∈[45,60), the quality of the remote sensing image to be judged is judged to be low;

[0070] If R∈[60,80], the quality of the remote sensing image to be judged is judged to be poor.

[0071] The present invention also provides a system for implementing the remote sensing image quality determination method, comprising a data acquisition module, a band division module, an index calculation module, a quality calculation module and a quality determination module; the data acquisition module, the band division module, the index calculation module, the quality calculation module and the quality determination module are connected in series in sequence; the data acquisition module is used to acquire the remote sensing image to be determined, and upload the data information to the band division module; the band division module is used to divide the remote sensing image to be determined into bands according to the received data information, and upload the data information to the index calculation module; the index calculation module is used to calculate the pixel average deviation index, pixel repetition interval index, highlight pixel value index and noise index of each band for each divided band according to the received data information, and upload the data information to the quality calculation module; the quality calculation module is used to calculate the comprehensive quality evaluation index of the remote sensing image to be determined according to the received data information and the calculated index, and upload the data information to the quality determination module; the quality determination module is used to make a quality determination of the remote sensing image to be determined according to the received data information and the comprehensive quality evaluation index.

[0072] The remote sensing image quality determination method and system provided by the present invention realizes the determination of remote sensing image quality by performing comprehensive influence calculations on the pixel values ​​of the remote sensing image in various aspects. Therefore, the present invention not only gets rid of the subjective problem of manual determination and realizes the determination of remote sensing image quality, but also has higher reliability, better accuracy, and is more objective and scientific. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 The figure is a schematic diagram of the method flow of the present invention.

[0074] Figure 2 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0075] The remote sensing image quality determination method provided by the present invention comprises the following steps:

[0076] S1. Obtaining the remote sensing image to be determined;

[0077] S2. Perform band division on the remote sensing image to be determined obtained in step S1; specifically, the steps include:

[0078] Set the background fill of the remote sensing image to be judged to 0;

[0079] Set the valid pixel value of the remote sensing image to be determined to be between 1 and 255;

[0080] The acquired remote sensing images to be judged are divided into four bands, namely B1, B2, B3 and B4;

[0081] S3. For each band divided in step S2, the pixel average deviation index, pixel repetition interval index, highlight pixel value index and noise index of each band are calculated respectively;

[0082] In specific implementation, the pixel average deviation index is calculated by the following steps:

[0083] Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively;

[0084] The pixel value interval of the current band is divided into the following five intervals: interval 1-[1,49], interval 2-[50,120], interval 3-[121,150], interval 4-[151,200] and interval 5-[201,255];

[0085] Calculate the average value of all valid pixel values ​​in the current band Bi

[0086] Determine the average value The interval in which it is located, and the corresponding pixel average deviation index A is calculated i :

[0087] If the average is in interval 1, then A i =2;

[0088] If the average is in interval 2, then A i =1;

[0089] If the average is in interval 3, then A i =3;

[0090] If the average is in interval 4, then A i =4;

[0091] If the average is in interval 5, then A i =5;

[0092] The pixel repetition interval index is specifically calculated using the following steps:

[0093] Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively;

[0094] Divide 1 to 255 into 26 statistical intervals, where the first interval is [1,10], the second interval is [11,20], the third interval is [21,30], ..., the 25th interval is [241,250], and the 26th interval is [251,255];

[0095] Count the percentage of pixels in each interval to the total number of valid pixels in the current band. i , the value of i ranges from 1 to 26;

[0096] Get p i The maximum value p imax , and make a judgment:

[0097] If p imax ≥T1, then set the judgment percentage p j For p j =p imax ;

[0098] If p imax <T1, then set the judgment percentage p j For p j =p1+p 26 ; T1 is the set threshold, the preferred value is 15%;

[0099] According to the obtained judgment percentage P j The corresponding pixel repetition interval index C is calculated i :

[0100] If P j ∈[0%,2%), then C i =1;

[0101] If P j ∈[2%,5%), then C i =2;

[0102] If P j ∈[5%,10%), then C i =3;

[0103] If P j ∈[10%,15%), then C i =4;

[0104] If P j ∈[15%,100%], then Ci =5;

[0105] The highlight pixel value index is specifically calculated using the following steps:

[0106] Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively;

[0107] Count the number of pixels W with valid pixel values ​​greater than 220 in the current band Bi i ;

[0108] Calculate the number of pixels W i The percentage of all valid pixels in the current band Bi i :

[0109] According to the calculated percentage Pr i , calculate the corresponding highlight pixel value index M i :

[0110] If Pr i ∈[0%,1%), then M i =1;

[0111] If Pr i ∈[1%,2%), then M i =2;

[0112] If Pr i ∈[2%,5%), then M i =3;

[0113] If Pr i ∈[5%,15%), then M i =4;

[0114] If Pr i ∈[15%,100%], then M i =5;

[0115] The noise index is specifically calculated using the following steps:

[0116] Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively;

[0117] Construct the second-order difference operator Arr as

[0118] Calculate the convolution result Q of the effective pixel value in the current band Bi and the second-order difference operator Arr i ;

[0119] Statistical convolution result Q iThe number of pixels whose absolute value is greater than 3 Psr i ;

[0120] According to the statistical pixel number Psr i , calculate the corresponding noise index L i :

[0121] If Psr i ∈[0%,2%), then L i =1;

[0122] If Psr i ∈[2%,5%), then L i =2;

[0123] If Psr i ∈[5%,8%), then L i =3;

[0124] If Psr i ∈[8%,15%), then L i =4;

[0125] If Psr i ∈[15%,100%], then L i =5;

[0126] S4. Calculate the comprehensive quality assessment index of the remote sensing image to be determined according to the index calculated in step S3; specifically, the steps include:

[0127] Calculate the quality assessment coefficient R of the i-th band Bi of the remote sensing image to be judged i For R i =A i +C i +M i +L i ;

[0128] According to the quality evaluation coefficient R of Bi of the i-th band i , the comprehensive quality evaluation index R of the remote sensing image to be judged is calculated as R=R1+R2+R3+R4;

[0129] S5. According to the comprehensive quality evaluation index obtained in step S4, the quality of the remote sensing image to be judged is judged; specifically, the following steps are included:

[0130] According to the comprehensive quality evaluation index obtained in step S4, the quality of the remote sensing image to be judged is judged using the following rules:

[0131] If R∈[16,30), the quality of the remote sensing image to be judged is high;

[0132] If R∈[30,45), the quality of the remote sensing image to be judged is judged to be medium;

[0133] If R∈[45,60), the quality of the remote sensing image to be judged is judged to be low;

[0134] If R∈[60,80], the quality of the remote sensing image to be judged is judged to be poor.

[0135] like Figure 2 The functional module schematic diagram of the system of the present invention is shown as follows: the system for realizing the remote sensing image quality determination method disclosed in the present invention comprises a data acquisition module, a band division module, an index calculation module, a quality calculation module and a quality determination module; the data acquisition module, the band division module, the index calculation module, the quality calculation module and the quality determination module are connected in series in sequence; the data acquisition module is used to acquire the remote sensing image to be determined, and upload the data information to the band division module; the band division module is used to divide the remote sensing image to be determined into bands according to the received data information, and upload the data information to the index calculation module; the index calculation module is used to calculate the pixel average deviation index, the pixel repetition interval index, the highlight pixel value index and the noise index of each band respectively for each divided band according to the received data information, and upload the data information to the quality calculation module; the quality calculation module is used to calculate the comprehensive quality evaluation index of the remote sensing image to be determined according to the received data information and the calculated index, and upload the data information to the quality determination module; the quality determination module is used to make a quality determination of the remote sensing image to be determined according to the received data information and the comprehensive quality evaluation index.

Claims

1. A remote sensing image quality determination method comprising the following steps: S1. Obtaining the remote sensing image to be determined; S2. dividing the remote sensing image to be determined obtained in step S1 into bands; S3. For each band divided in step S2, the pixel average deviation index, pixel repetition interval index, highlight pixel value index and noise index of each band are calculated respectively; S4. Calculate the comprehensive quality assessment index of the remote sensing image to be determined according to the index calculated in step S3; S5. According to the comprehensive quality evaluation index obtained in step S4, the quality of the remote sensing image to be judged is judged.

2. The remote sensing image quality determination method according to claim 1, characterized in that Step S2 of dividing the remote sensing image to be determined obtained in step S1 into bands specifically includes the following steps: Set the background fill of the remote sensing image to be judged to 0; Set the valid pixel value of the remote sensing image to be determined to be between 1 and 255; The acquired remote sensing images to be judged are divided into four bands, namely B1, B2, B3 and B4.

3. The remote sensing image quality determination method according to claim 2, characterized in that The pixel average deviation index described in step S3 is specifically calculated using the following steps: Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively; The pixel value interval of the current band is divided into the following five intervals: interval 1-[1,49], interval 2-[50,120], interval 3-[121,150], interval 4-[151,200] and interval 5-[201,255]; Calculate the average value of all valid pixel values ​​in the current band Bi Determine the average value The interval in which it is located, and the corresponding pixel average deviation index A is calculated i : If the average is in interval 1, then A i =2; If the average is in interval 2, then A i =1; If the average is in interval 3, then A i =3; If the average is in interval 4, then A i =4; If the average is in interval 5, then A i =5.

4. The remote sensing image quality determination method according to claim 3, characterized in that The pixel repetition interval index described in step S3 is specifically calculated using the following steps: Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively; Divide 1 to 255 into 26 statistical intervals, where the first interval is [1,10], the second interval is [11,20], the third interval is [21,30], ..., the 25th interval is [241,250], and the 26th interval is [251,255]; Count the percentage of pixels in each interval to the total number of valid pixels in the current band. i , the value of i ranges from 1 to 26; Get p i The maximum value p imax , and make a judgment: If p imax ≥T1, then set the judgment percentage p j For p j =p imax ; If p imax <T1, then set the judgment percentage p j For p j =p1+p 26 ; T1 is the set threshold; According to the obtained judgment percentage P j The corresponding pixel repetition interval index C is calculated i : If P j ∈[0%,2%), then C i =1; If P j ∈[2%,5%), then C i =2; If P j ∈[5%,10%), then C i =3; If P j ∈[10%,15%), then C i =4; If P j ∈[15%,100%], then C i =5.

5. The remote sensing image quality determination method according to claim 4, characterized in that The highlight pixel value index described in step S3 is specifically calculated using the following steps: Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively; Count the number of pixels W with valid pixel values ​​greater than 220 in the current band Bi i ; Calculate the number of pixels W i The percentage of all valid pixels in the current band Bi i : According to the calculated percentage Pr i , calculate the corresponding highlight pixel value index M i : If Pr i ∈ [0%, 1%), then M i = 1; If Pr i ∈ [1%, 2%), then M i = 2; If Pr i ∈ [2%, 5%), then M i = 3; If Pr i ∈ [5%, 15%), then M i = 4; If Pr i ∈ [15%, 100%], then M i = 5.

6. The remote sensing image quality determination method according to claim 5, characterized in that The noise index described in step S3 is specifically calculated using the following steps: Set the current band to Bi; i ranges from 1 to 4, corresponding to bands B1, B2, B3 and B4 respectively; Construct the second-order difference operator Arr as Calculate the convolution result Q of the effective pixel value in the current band Bi and the second-order difference operator Arr i ; Statistical convolution result Q i The number of pixels whose absolute value is greater than 3 Psr i ; According to the statistical pixel number Psr i , calculate the corresponding noise index L i : If Psr i ∈[0%,2%), then L i =1; If Psr i ∈[2%,5%), then L i =2; If Psr i ∈[5%,8%), then L i =3; If Psr i ∈[8%,15%), then L i =4; If Psr i ∈[15%,100%], then L i =5.

7. The remote sensing image quality determination method according to claim 6, characterized in that The step S4 calculates the comprehensive quality assessment index of the remote sensing image to be determined based on the index calculated in step S3, and specifically includes the following steps: Calculate the quality assessment coefficient R of the i-th band Bi of the remote sensing image to be judged i For R i =A i +C i +M i +L i ; According to the quality evaluation coefficient R of Bi of the i-th band i , the comprehensive quality evaluation index R of the remote sensing image to be judged is calculated to be R=R1+R2+R3+R4.

8. The remote sensing image quality determination method according to claim 7, characterized in that Step S5, according to the comprehensive quality assessment index obtained in step S4, performs quality assessment on the remote sensing image to be assessed, and specifically includes the following steps: According to the comprehensive quality evaluation index obtained in step S4, the quality of the remote sensing image to be judged is judged using the following rules: If R∈[16,30), the quality of the remote sensing image to be judged is high; If R∈[30,45), the quality of the remote sensing image to be judged is judged to be medium; If R∈[45,60), the quality of the remote sensing image to be judged is judged to be low; If R∈[60,80], the quality of the remote sensing image to be judged is judged to be poor.

9. A system for implementing the remote sensing image quality determination method according to any one of claims 1 to 8, characterized in that It includes a data acquisition module, a band division module, an index calculation module, a quality calculation module and a quality judgment module; the data acquisition module, the band division module, the index calculation module, the quality calculation module and the quality judgment module are connected in series in sequence; the data acquisition module is used to acquire the remote sensing image to be judged, and upload the data information to the band division module; the band division module is used to divide the remote sensing image to be judged into bands according to the received data information, and upload the data information to the index calculation module; the index calculation module is used to calculate the pixel average deviation index, pixel repetition interval index, highlight pixel value index and noise index of each band respectively according to the received data information, and upload the data information to the quality calculation module; The quality calculation module is used to calculate the comprehensive quality evaluation index of the remote sensing image to be judged according to the received data information and the calculated index, and upload the data information to the quality judgment module; The quality judgment module is used to make quality judgment on the remote sensing image to be judged according to the received data information and the comprehensive quality evaluation index.

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