CN104463169B - A kind of Ship Target area-of-interest rapid extracting method of overlap processing - Google Patents

A kind of Ship Target area-of-interest rapid extracting method of overlap processing Download PDF

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CN104463169B
CN104463169B CN201410705774.4A CN201410705774A CN104463169B CN 104463169 B CN104463169 B CN 104463169B CN 201410705774 A CN201410705774 A CN 201410705774A CN 104463169 B CN104463169 B CN 104463169B
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陈瑞
孙文方
张守娟
李晓博
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Xian Institute of Space Radio Technology
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Abstract

The present invention proposes a kind of Ship Target area-of-interest (ROI) rapid extracting method of overlap processing.The present invention carries out overlap partition to remote sensing images first, and the pixel average of each sub-block, Estimation of Mean value, pixel criterion deviation and standard deviation estimate are calculated；Then preliminary screening is carried out to Ship Target ROI using pixel Estimation of Mean value；It is otherwise marine site if standard deviation estimate outside 3 σ confidential intervals, concludes that the region is Ship Target ROI finally according to the σ rules of standard deviation estimate 3 in preliminary screening region to judging.The inventive method is simple, differentiates that the degree of accuracy is high, and has certain adaptability, can be applied to the fields such as sea traffic monitoring, ship search and rescue, fisheries management and marine site Situation Awareness.

Description

A kind of Ship Target area-of-interest rapid extracting method of overlap processing
Technical field
The invention belongs to space remote sensing field, it is related to a kind of Ship Target ROI rapid extracting methods of overlap processing.
Background technology
The method of Ship Target ROI (area-of-interest) rapid extraction is the serviceability from remotely-sensed data, directly will Ship Target region interested is detected, and rejects invalid data, improving data transmission efficiency.With remotely sensed image technology Development, how from background complexity, the visible remote sensing image that target signature is unstable, data volume is huge, fast and accurately sieve Ship Target ROI is selected, is an extremely challenging job.
It is relatively more to the Ship Target ROI extracting methods of remote sensing images at present, it can be divided mainly into four major classes:(1) based on ash Spend the method for statistical nature；(2) method based on marginal information；(3) method based on fractal model and fuzzy theory；(4) base In the method for visually-perceptible mechanism.And for the method for visible images Ship Target ROI extractions, usually carried out on ground , compared with in-orbit application on star, cloud detection equipment in ground must not constrained by space power consumption, weight, volume etc., therefore detection side Method is to complexity without strict demand.In addition, satellite remote sensing images Ship Target Detection method is specifically defended for certain mostly at present Star, detection method is with strong points, without versatility.
The content of the invention
Present invention solves the technical problem that it is：A kind of overcome the deficiencies in the prior art, there is provided Ship Target of overlap processing The method of ROI rapid extractions, solve the problems, such as the in-orbit Ship Target ROI quick detections of satellite remote sensing images.
The technical scheme is that：A kind of Ship Target area-of-interest rapid extracting method of overlap processing, step It is as follows：
1) weight for being divided into image size by four kinds of modes according to different image resolution ratios for the original image of (M, N) Folded subimage block, each subimage block size is (BM,BN)；Wherein M and N can divide exactly B respectivelyMAnd BN, then segmentation figure is as number of blocks ForThe starting point of the upper left corner sub-block of four kinds of partitioning schemes be respectively (0,0), (BM/ 2,0), (0, BN/ 2) and (BM/2,BN/2)；
2) calculate respectively and obtain each (B in four segmentationsM/2,BN/ 2) the pixel average of subimage block, Estimation of Mean value, Pixel criterion deviation and standard deviation estimate；Wherein, the Estimation of Mean value of each subimage block appears in four times for it The arithmetic mean of instantaneous value of subimage block pixel average in partitioning scheme；The standard deviation estimate of each subimage block is it Appear in the arithmetic mean of instantaneous value of the pixel criterion deviation in four partitioning schemes；
3) judgement is compared with the threshold value T1 and T2 of precondition to the Estimation of Mean value obtained in step 2), if full Sufficient decision condition：T1≤Estimation of Mean value≤T2, then the preliminary judgement region is Ship Target ROI, is otherwise marine site；
4) threshold value is carried out using standard deviation estimate to the region that preliminary judgement in step 3) is Ship Target ROI to sentence It is disconnected, if meeting decision condition：The threshold value T3 of Estimation of Mean value >=precondition, then the region is confirmed for Ship Target ROI, it is no It is then marine site.
The present invention compared with prior art the advantages of be：A kind of Ship Target sense of overlap processing proposed by the present invention is emerging Interesting region rapid extracting method, method is simple, meets the needs of in-orbit application, and inventive method uses simple average and standard deviation Feature, and computationally intensive threshold parameter is chosen and then completed by ground.Therefore, whole method is simply easy to hardware realization；Hair Bright method accuracy in detection is high, by showing the classification results of satellite remote sensing images, detection of this method to Ship Target ROI Rate is more than 90%；Simultaneously adaptable, by choosing different satellite imaging datas, experiment test multi-satellite image should Method can preferably detect Ship Target ROI, have and well adapt to performance.
Brief description of the drawings
Fig. 1 is the flow chart of the present invention；
Fig. 2 (a) -2 (d) is respectively four seed block partitioning scheme figures；
Fig. 3 is subimage block neighborhood relationships figure.
Embodiment
The present invention proposes a kind of low complex degree Ship Target ROI rapid extracting methods, and its specific implementation process is as follows：
First, overlap partition is carried out to original image first, image size is divided into below figure for the original image of (M, N) Size shown in 2 (a) is (BM,BN) subimage block, it is desirable to M and N can divide exactly B respectivelyMAnd BN, segmentation figure is as number of blocksThe starting point of upper left corner sub-block is (0,0)；Press the method shown in Fig. 2 (b) and carry out image block, upper left corner sub-block Starting point be (BM/2,0)；Press the method shown in Fig. 2 (c) and carry out image block, the starting point of upper left corner sub-block is (0, BN/2)； Press the method shown in Fig. 2 (d) and carry out image block, the starting point of upper left corner sub-block is (BM/2,BN/2).Above-mentioned four kinds of images point Cut and constitute a kind of overlapping image segmentation result, wherein four sizes at four angles are (BM/2,BN/ 2) sub-block does not have segmentation It is overlapping, four edgesIndividual size is (BM/2,BN/ 2) sub-block is appeared in segmentation twice, and middle 'sIndividual size is (BM/2,BN/ 2) sub-block is appeared in four segmentations.Engineering experience shows, 2m The image of~5m resolution ratio is relatively adapted to do Ship Target ROI extractions, and in actual process, true resolution is transformed into 2m, and the hull size detected as required carries out piecemeal.
2nd, each (B in four segmentations is calculated respectivelyM/2,BN/ 2) characteristic value of sub-image, including average, variance.Note Θk(i, j) is the individual segmentation subimage block region of kth kind dividing method (i, j), and its size is (BM/2,BN/ 2), its subgraph Block neighborhood relationships are as shown in Figure 3.
Remember Ωk(i, j) is the individual sub- image block areas of kth kind dividing method (i, j), and its pixel average is：
Wherein：P (m, n) is that (m, n) puts pixel value；
Pixel criterion deviation is
Remember that Θ (i, j) is that (i, j) individual size is (BM/2,BN/ 2) subimage block region, for appearing in 4 segmentations Subgraph it is fast, its pixel Estimation of Mean value is：
Fast for appearing in the subgraph in 2 segmentations, its pixel Estimation of Mean value is：
(up-and-down boundary)
(right boundary)
Fast for appearing in the subgraph in 1 segmentation, its pixel Estimation of Mean value is：
(corner)
Fast for appearing in the subgraph in 4 segmentations, its pixel criterion estimation of deviation value is：
Fast for appearing in the subgraph in 2 segmentations, its pixel criterion estimation of deviation value is：
(up-and-down boundary)
(right boundary)
Fast for appearing in the subgraph in 1 segmentation, its pixel criterion estimation of deviation value is：
(corner)
3rd, for obtaining in step 2Carry out threshold value judgement, it is desirable toThen tentatively it is judged as The region is Ship Target ROI, is otherwise marine site；According to engineering experience, the reflectivity on naval vessel is between 10%~50%, then T1 =10%R, T2=50%R (specific percentage can adjust), wherein R are maximum corresponding to image pixel bit wide.
4th, threshold value judgement is carried out for obtaining the preliminary standard deviation estimate for judging region in step 3, it is desirable toThen the region decision is Ship Target ROI, is otherwise marine site；Wherein T3 determines according to 3 σ rules, i.e.,Θ (i, j) herein is the region just sentenced in step 3.
The content not being described in detail in description of the invention belongs to professional and technical personnel in the field's known technology.

Claims (1)

  1. A kind of 1. Ship Target area-of-interest exacting method of overlap processing, it is characterised in that：Step is as follows：
    1) image size is divided into according to different image resolution ratios for the original image of (M, N) by four kinds of partitioning schemes overlapping Subimage block, each subimage block size are (BM,BN)；Wherein M and N can divide exactly B respectivelyMAnd BN, then segmentation figure be as number of blocksThe starting point of the upper left corner sub-image of four kinds of partitioning schemes be respectively (0,0), (BM/ 2,0), (0, BN/ 2) and (BM/2,BN/2)；Four sizes at four angles are (BM/2,BN/ 2) sub-image do not have segmentation it is overlapping, four edgesIndividual size is (BM/2,BN/ 2) sub-image is appeared in segmentation twice, and middleIndividual size is (BM/2,BN/ 2) sub-image is appeared in four segmentations；
    2) calculate respectively and obtain each (B in four segmentationsM/2,BN/ 2) the pixel average of sub-image, Estimation of Mean value, pixel Standard deviation and standard deviation estimate；Wherein, each (BM/2,BN/ 2) the Estimation of Mean value of sub-image occurs for it The arithmetic mean of instantaneous value of sub-image pixel average in four partitioning schemes；Each (BM/2,BN/ 2) mark of sub-image Quasi- estimation of deviation value appears in the arithmetic mean of instantaneous value of the sub-image pixel criterion deviation in four partitioning schemes for it；Specific mistake Cheng Wei：
    Remember Ωk(i, j) is the individual sub-block image-region of kth kind dividing method (i, j), and its pixel average is：
    <mrow> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mi>k</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msub> <mi>B</mi> <mi>M</mi> </msub> <msub> <mi>B</mi> <mi>N</mi> </msub> </mrow> </mfrac> <munder> <mo>&amp;Sigma;</mo> <mrow> <mo>(</mo> <mi>m</mi> <mo>,</mo> <mi>n</mi> <mo>)</mo> <mo>&amp;Element;</mo> <msub> <mi>&amp;Omega;</mi> <mi>k</mi> </msub> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </munder> <mi>P</mi> <mrow> <mo>(</mo> <mi>m</mi> <mo>,</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
    Wherein：P (m, n) is that (m, n) puts pixel value；
    Pixel criterion deviation is
    <mrow> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mi>k</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mn>1</mn> <mrow> <msub> <mi>B</mi> <mi>M</mi> </msub> <msub> <mi>B</mi> <mi>N</mi> </msub> </mrow> </mfrac> <munder> <mo>&amp;Sigma;</mo> <mrow> <mo>(</mo> <mi>m</mi> <mo>,</mo> <mi>n</mi> <mo>)</mo> <mo>&amp;Element;</mo> <msub> <mi>&amp;Omega;</mi> <mi>k</mi> </msub> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </munder> <msup> <mi>P</mi> <mn>2</mn> </msup> <mo>(</mo> <mrow> <mi>m</mi> <mo>,</mo> <mi>n</mi> </mrow> <mo>)</mo> <mo>-</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mi>k</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mn>0.5</mn> </msup> <mo>;</mo> </mrow>
    Remember that Θ (i, j) is that (i, j) individual size is (BM/2,BN/ 2) sub-image region, for appearing in the son in 4 segmentations Block image, its pixel Estimation of Mean value are：
    <mrow> <msub> <mover> <mi>E</mi> <mo>^</mo> </mover> <mrow> <mi>&amp;Theta;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>4</mn> </mfrac> <mrow> <mo>(</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>4</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
    For appearing in the sub-image in 2 segmentations, its pixel Estimation of Mean value is：
    <mrow> <msub> <mover> <mi>E</mi> <mo>^</mo> </mover> <mrow> <mi>&amp;Theta;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
    <mrow> <msub> <mover> <mi>E</mi> <mo>^</mo> </mover> <mrow> <mi>&amp;Theta;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
    For appearing in the sub-image in 1 segmentation, its pixel Estimation of Mean value is：
    <mrow> <msub> <mover> <mi>E</mi> <mo>^</mo> </mover> <mrow> <mi>&amp;Theta;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <msub> <mi>E</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> </mrow>
    For appearing in the sub-image in 4 segmentations, its pixel criterion estimation of deviation value is：
    <mrow> <msub> <mover> <mi>&amp;delta;</mi> <mo>^</mo> </mover> <mrow> <mi>&amp;Theta;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>4</mn> </mfrac> <mrow> <mo>(</mo> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>4</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
    For appearing in the sub-image in 2 segmentations, its pixel criterion estimation of deviation value is：
    <mrow> <msub> <mover> <mi>&amp;delta;</mi> <mo>^</mo> </mover> <mrow> <mi>&amp;Theta;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
    <mrow> <msub> <mover> <mi>&amp;delta;</mi> <mo>^</mo> </mover> <mrow> <mi>&amp;Theta;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
    For appearing in the sub-image in 1 segmentation, its pixel criterion estimation of deviation value is：
    <mrow> <msub> <mover> <mi>&amp;delta;</mi> <mo>^</mo> </mover> <mrow> <mi>&amp;Theta;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;delta;</mi> <mrow> <msub> <mi>&amp;Omega;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>;</mo> </mrow>
    3) judgement is compared with the threshold value T1 and T2 of precondition to the Estimation of Mean value obtained in step 2), if meeting to sentence Fixed condition：T1≤Estimation of Mean value≤T2, then the preliminary judgement region is Ship Target ROI, is otherwise marine site；
    4) threshold decision is carried out using standard deviation estimate to the region that preliminary judgement in step 3) is Ship Target ROI, such as Fruit meets decision condition：The threshold value T3 of standard deviation estimate >=precondition, then the region is confirmed for Ship Target ROI, it is no It is then marine site；Wherein, T1=10%R, T2=50%R, R are maximum corresponding to image pixel bit wide；T3 is true according to 3 σ rules It is fixed, i.e.,Θ (i, j) is the region just sentenced in step 3).
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