CN101546431B - Extraction method of water body thematic information of remote sensing image based on sequential nonlinear filtering - Google Patents
Extraction method of water body thematic information of remote sensing image based on sequential nonlinear filtering Download PDFInfo
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Abstract
The invention belongs to the technical field of remote sensing, and in particular relates to an extraction method of water body thematic information of remote sensing images based on sequential nonlinear filtering. The method comprises the following four steps: preprocessing remote sensing images and completing sequential nonlinear filtering; calibrating filtering result image areas and calculating the feature values of each calibrated area; establishing the selection criteria of water body target areas; and carrying out automatic extraction of water body areas and post-processing of water body thematic extraction information based on morphology so as to generate thematic information vector data meeting the requirements of common GIS software finally. The extraction method, starting with the basic operation of grey-scale mathematical morphology, establishes a sequential nonlinear filtering model to directly carry out nonlinear filtering and extract water body areas in remote sensing images; according to relevant knowledge of water body, feature values of different water body areas extracted through sequential nonlinear filtering are calculated; according to water body imaging characteristics and relevant knowledge of water body areas, the selection criteria of the water body target areas are established to realize automatic and quick extraction of water body thematic information on remote sensing images. The extraction method is less influenced by noise and error and has strong robustness; moreover, the method realizes fully automatic processing of extraction water body without manual intervention, can process extensive data, and has less time consumption.
Description
Technical field
The invention belongs to the remote sensing technology field, be specifically related to a kind of extraction method of water body thematic information of remote sensing image based on sequential nonlinear filtering.
Background technology
Water body is important thematic information, and it has significant values for geoanalysis, is the thematic information data on a class basis.Management for each Geographic Information System is significant with construction.Utilizing remote sensing image to carry out the water body information extraction is a kind of quick and good mode of the trend of the times.The method of utilizing remote sensing image to extract thematic information of towns at present has: manual method, semiautomatic tracing extracting method, and automatic mode classifying identification method; Manual method, semi-automatic acquisition methods need a large amount of manually-operated efficient low in these methods; Utilizing multispectral information classification recognition methods is a kind of important method, but existing classifying identification method is not studied at the water body information extraction on the remote sensing image specially, and this method for identifying and classifying is just more suitable for the multiband remote sensing image of general resolution (spatial resolution 20m, or lower); For high resolving power (resolution reach 5m and more than) and wave band less (4 s' wave band or still less) remote sensing image, its Classification and Identification to water body is extracted result and can not be satisfied follow-up one-tenth figure and application requirements.At present, aspect the high-resolution remote sensing image Classification and Identification, the sorting technique of the eCognition system of Germany is reasonable a kind of, it is based on multi-scale division, divide time-like to need bracket protocol, Fen Lei bracket protocol is used for other minute time-like and need finally realizes the robotization of assorting process through revising each time, and the bracket protocol here is similar to the knowledge support.Be characterized in the refinement of classifying, but bracket protocol is had bigger dependence (the protocols having storehouse is supported better), and neither carry out at this category information of water body specially, so efficient is not high when being applied to the water body information extraction.For improving the water body extraction efficiency, avoid the artificial interference in the leaching process, realize a kind of full automatic water body extracting method, this method has produced at above-mentioned situation and has utilized gray scale mathematics morphology operations, constitute a kind of sequential nonlinear filtering model, directly carry out nonlinear filtering, effectively extract the water body zone in the remote sensing image; On this basis, relevant knowledge according to water body, to the water body zone that extracts above, calculate different various features values (area, area grayscale average, the grey level histogram in zone) of demarcating the zone, according to water body imaging characteristic and water body zone relevant knowledge, set up water body target area selection criterion, carry out the selective extraction automatically of water body zone, obtain water body thematic information with this.
Summary of the invention
The objective of the invention is to, at shortcomings such as existing being subjected to of utilizing that high-resolution remote sensing image carries out that extraction method of water body thematic information exists various detailed information serious interference, extraction efficiency are low, weak effects, a kind of extraction method of water body thematic information of remote sensing image based on sequential nonlinear filtering is proposed, the present invention is when reservation utilizes remote sensing image to carry out strong, the repeated advantages of higher of the water body thematic information extraction trend of the times, water body information extraction to high resolving power multiband remote sensing image is more suitable, and its efficient is higher.Overcome general extracting method and needed priori, shortcoming to noise and details interfere information sensitivity, utilize gray scale mathematics morphology operations to constitute a kind of sequential nonlinear filtering model, directly high-resolution remote sensing image is carried out nonlinear filtering, the eigenwert in statistics spotting zone, according to the feature that water body had, carry out water body thematic information of remote sensing image and discern automatically.
The extraction method of water body thematic information of remote sensing image that the present invention proposes based on sequential nonlinear filtering, by starting with from the fundamental operation of gray scale mathematical morphology, constitute a kind of sequential nonlinear filtering model, directly carry out nonlinear filtering, extract the water body zone in the remote sensing image; On this basis, relevant knowledge according to water body, the water body zone that sequential nonlinear filtering is extracted, calculate the various features value (area of zones of different, the area grayscale average, the grey level histogram in zone), according to water body imaging characteristic and water body zone relevant knowledge, set up water body target area selection criterion, thereby the automatic rapid extraction of water body thematic information on the realization remote sensing image, specifically comprise: the pre-service of remote sensing image and sequential nonlinear filtering, filtering imagery zone demarcation as a result and each are demarcated the calculating of regional various features value, the foundation of water body target area selection criterion, the automatic extraction in water body zone reach based on four steps of morphologic water body special topic information extraction aftertreatment, and is specific as follows:
(1) pre-service of remote sensing multiband image and sequential nonlinear filtering
According to water body spectral characteristic and remote sensing image band setting, select infrared or red wave band, indigo plant or green wave band as alternative wave band, carry out pre-service according to formula (1), and the pre-service result is done histogram equalization, generate pending data plot layer,
BW
0＝B
L+k×B
L/(B
H+w) (1)
Wherein, B
LRepresent infrared or red wave band brightness value, B
HBlue or the green wave band brightness value of expression, BW
0Represent pretreated pixel brightness value, k is the transformation of scale coefficient, and w is for fear of removing zero problem, and to B
HA very little positive number that adds.
Adopt the sequential nonlinear filtering method, at first carry out the maximal value Filtering Processing to generating pending data plot layer, eliminate the dark pixel of discontinuous planar distribution in the image, the water body zone that is planar continuous distribution simultaneously is also weakened; Carry out medium filtering then and carry out noise reduction process; And then adopt the minimum value Filtering Processing to recover weakened water body zone in the maximal value Filtering Processing.
(2) to filtering as a result image carry out region labeling, calculate the various features value that each demarcates the zone
Adopt the region growing algorithm, the image as a result in the step (1) is demarcated candidate's water body pixel earlier form the continuum, calculate every statistical nature in each zone then: region area, area grayscale average, area grayscale histogram,
The area grayscale histogram is:
Region area A is:
The area grayscale average:
The histogram peak gray level:
GP＝PI，H[PI]＝MAX{H[i]，i＝0，1，...，255} (5)
Wherein, R[i] for gray-scale value in the zone is the collection of pixels of i, H[i] be the number of pixels of histogram i level gray scale, A is a region area, and GM is the area grayscale average, and GP is the histogram peak gray level;
(3) foundation of water body target area selection criterion
1. the zone of region area A＜A0 is non-water body zone; 2. the zone of area grayscale average GM＞GM0 is non-water body; 3. the zone of regional peak gray GP＞regional average gray scale GM is non-water body zone.Wherein A0 and GM0 value all have bigger redundance, desirable A0=100, GM0=64.
(4) the automatic extraction in water body zone is based on morphologic water body special topic information extraction aftertreatment
Water body region criterion according to step (3) is set up reaches each regional eigenwert, each zone of mark is judged, thereby rejected clutter and other targets automatically, obtains the water body target area; Pass through modal of morphological opening and closing operation, the thematic map that generates is looked like to carry out filtering, remove the specific image details littler, on the basis of morphologic filtering, the vector that extracts carried out length and area statistics than structural element, to the vector length that extracts and enclose area rejecting less than threshold value.Threshold value can be adjusted, and is 25 for the general length threshold of high resolving power aviation remote sensing image, and area threshold is 500, and the thematic information layer representation of Ti Quing is the shape file of ArcGIS software at last, i.e. the shp file layout.
Below the inventive method is done further to limit, specific as follows:
1, the pre-service of remote sensing multiband image and sequential nonlinear filtering
According to water body spectral characteristic and remote sensing image band setting, select infrared (IR) and blue (B) two wave bands as alternative wave band; At different remote sensing images,, also can select red (R) to replace infrared (IR) or green (G) to replace blue (B) wave band if do not have band setting or lack above-mentioned wave band.Then can carry out pre-service and the pre-service result is made histogram equalization, generate pending data plot layer according to following formula.
BW
0＝B
L+k×B
L/(B
H+w)
Wherein, B
LRepresent infrared (IR) or red (R) wave band brightness value, B
HBlue (B) or green (B) wave band brightness value of expression, BW
0Represent pretreated pixel brightness value, k is the transformation of scale coefficient, and w is for fear of removing zero problem, and to B
HA very little positive number that adds.
Use gray scale morphological operator and non-linear statistical filtering operator, special structural element of definition in the gray scale morphology operations, the gray scale morphology operations is converted into nonlinear filtering to be handled, pass through sequential nonlinear filtering, morphology gray scale dilation operation is converted into the maximal value Filtering Processing, and the gray scale erosion operation is converted into the minimum value Filtering Processing.Adopt the combined treatment step, remote sensing image is at first carried out the maximal value Filtering Processing, eliminate the dark pixel of discontinuous planar distribution in the image, the water body zone that is planar continuous distribution simultaneously is also weakened; Carry out medium filtering then and carry out noise reduction process; And then adopt the minimum value Filtering Processing to recover weakened water body zone in the maximal value Filtering Processing.The neighborhood size can be chosen flexibly in the processing procedure, also can carry out repeatedly maximal value filtering continuously, also carry out the processing of same number during minimum value filtering continuously, then the water body target for continuous planar distribution can obtain keeping, and eliminate most of non-water body zone, extract water body target area in the remote sensing image.
2, to filtering as a result image carry out region labeling, calculate the various features value that each demarcates the zone
Adopt the region growing algorithm, the image as a result in (1) is demarcated candidate's water body pixel earlier form the continuum, calculate every statistical nature in each zone then: region area, area grayscale average, area grayscale histogram.Prepare as setting up each regional judgment rule automatically, avoided the image global threshold to set the problem of difficulty.
3, the foundation of water body target area selection criterion
According to water body imaging characteristic and water body zone relevant knowledge, set up water body target area selection criterion, be continuous planar distribution according to water body, set up the region area decision rule, be non-water body zone to area less than the regional determination of specified pixel; More unified according to the water body regional luminance, its histogram should be unimodal shape, sets up the intensity profile decision rule, determines non-water body zone.Here brightness is judged can set looser standard, in conjunction with the unimodal feature of histogram, excludes obvious non-water body zone earlier.According to the relation of region histogram peak gray and regional average gray scale, judge the water body zone automatically again.Automatically determine following water body selection criterion: 1) zone of region area A＜A0 is non-water body zone; 2) zone of area grayscale average GM＞GM0 is non-water body; 3) zone of regional peak gray GP＞regional average gray scale GM is non-water body zone.Wherein A0 and GM0 value all have bigger redundance, desirable A0=100, GM0=64.
4, the automatic extraction in water body zone is based on morphologic water body special topic information extraction aftertreatment
According to the water body region criterion of above foundation, and each regional eigenwert, each zone of mark is judged, thereby rejected clutter and other target automatically, obtain the water body target area; On this basis, in order to eliminate on the result because of tiny atural object and noise effect, and the tiny line map spot that produces, pass through modal of morphological opening and closing operation, the thematic map that generates is looked like to carry out filtering, and (size of structural element can select 3 * 3,5 * 5,7 * 7,15 * 15), remove the specific image details littler than structural element, guarantee not produce the set distortion of the overall situation simultaneously, on the basis of morphologic filtering, the vector that extracts is carried out length and area statistics, to the vector length that extracts and enclose area rejecting less than threshold value.Threshold value can be adjusted (for the general length threshold of high resolving power aviation remote sensing image is 25, and area threshold is 500), and the thematic information layer representation of Ti Quing is the shape file of ArcGIS software at last, i.e. the shp file layout.
Compared with prior art, the present invention has adopted the sequential nonlinear filtering method, avoided the threshold calculations in the classic method binaryzation to determine, speed is faster, and more suitable to high-resolution satellite image, and efficient is higher, adopt region histogram shape and gray scale peak-to-average relativeness determination methods, threshold value problem when effectively having avoided water body to judge is avoided the manual intervention of operator in the leaching process, has better robustness and adaptive characteristic.The present invention can be applied to the fast automatic extraction of water body thematic information of high resolving power multiband remote sensing image, obtains aspect change-detection at water body thematic information to play a role.
Description of drawings
Fig. 1 is other spectral coverage 1,2,3,4,5,7 beyond TM in the embodiment of the invention 1 (thematic imager) the view data heat extraction infrared band, wherein: (a) be 1 wave band spectral coverage (indigo plant), (b) be 2 wave band spectral coverages, (c) be 3 wave band spectral coverages, (d) be 4 wave band spectral coverages, (e) being 5 wave band spectral coverages, (f) is 7 wave band spectral coverages (infrared).
Embodiment
Below in conjunction with examples of implementation the specific implementation method is described, promptly will carry out water body thematic information identification, obtain to satisfy the water body thematic information vector data (.shp file) of ArcGIS software requirement a panel height resolution multiband remote sensing image.
Embodiment 1:
Here get that a certain TM (thematic imager) data--image size is 2516 * 2395 pixels, contain 7 wave bands, it has had geocoding information--and visual upper left corner point coordinate is (285950,3475350), pixel resolution is 25 meters, and what Fig. 1 showed is heat extraction infrared band other spectral coverage 1,2,3,4,5,7 in addition.Because data volume is very big, the video data in the example in (1), (2), (3) is the result of this image part from 16 * 16 sized images pieces of visual pixel ranks coordinate (753,723) to (768,738) scope.
(1) pre-service of remote sensing multiband image and sequential nonlinear filtering
According to water body spectral characteristic and remote sensing image band setting, select infrared (IR) and blue (B) two wave bands as alternative wave band; At different remote sensing images,, also can select red (R) to replace infrared (IR) or green (G) to replace blue (B) wave band if there is not band setting to lack above-mentioned wave band.Definition B
LRepresent above-mentioned infrared (IR) or red (R) wave band brightness value, B
HRepresent above-mentioned indigo plant (B) or green (B) wave band brightness value, BW
0Represent pretreated pixel brightness value.Then can carry out pre-service by following formula, and the pre-service result is generated pending data plot layer as histogram equalization.
BW
0＝B
L+k*B
L/(B
H+w)
Wherein, parameter k and w value all have very big redundance, get k=20, w=0.1 here.If BW
0＞255, then limit BW
0=255.
Then, adopt the sequential nonlinear filtering method, at figure layer after the above-mentioned pre-service, maximal value filtering, medium filtering and the minimum value filtering that can have nonlinear characteristic successively according to formula (6)～(8).
BW
1＝MAX{U
8(BW
0)} (6)
BW
2＝MED{U
8(BW
1)} (7)
BW
3＝MIN{U
8(BW
2)} (8)
Wherein, U
88 neighborhoods of () remarked pixel, MAX (), MED (), MIN () represent to get maximal value, intermediate value and the minimum value of neighborhood interior pixel, BW respectively
0, BW
1, BW
2, BW
3Represent after the pre-service after figure layer, the maximal value filtering respectively, behind the medium filtering and the filtered pixel brightness value of minimum value.
Here, select for use the 1st wave band (indigo plant) of TM image and the 7th wave band (infrared) as B
HAnd B
LThe wave band gradation data.Table 1 and table 2 a 16x16 size area image wave band data example for choosing.Table 3 and table 4 are result data behind linear transformation and the histogram equalization.Table 5 wherein will be changed to background gray scale 255 greater than 128 pixel grey scale for carry out result data behind the sequential nonlinear filtering according to formula (6)～(8).
Table 1 blue wave band
102?104?108?110?107?105?106?103?105?110?109?103?102?100?100?102
100?104?107?107?106?106?105?102?104?107?107?103?104?102?100?103
100?105?110?110?107?106?108?110?107?110?109?104?102?101 99?102
97?103?107?105?105?106?107?106?104?106?107?104?101?101?102?103
101?102?101 98?103?105?104?104?103?101?103?105?104?102?103?102
101?100?100?102?104?103?100?100?101?101?101?104?104?103?104?102
97 99?102?103?103?103?100 99 98 99?103?103?100?102?104?102
97?100?100 99 99?102?103?102?101?101?101?102 99?101?103?101
99 99?100 98 97?100?103?104?101?102?102?102?101?102?102?100
100?100?100 98 99?100?102?101?102?103?102?101?104?105?102 99
100 99 99 98?100 99 99?100?102?101?102?102?103?103?101 99
99 99?101?101 99 99 99?100?100?101?104?101?100?104?102?101
100?101?101?100 99?101?100?100?101?100 99?101?102?105?103?104
99?100 99 99?100?100?100 99?100?102?100?102?103?102?101?101
99?100?101?101?100?101?100?100?100?102?102?100?102?102?102?102
99?100?103?101?100?102?104?103?102?102?102 99?101?101?102?103
Table 2 infrared band
22?33?53?60?61?53?46?51?59?57?50?33?25?27?30?26
33?39?59?68?64?64?54?42?49?49?60?43?20?33?29?21
32?42?61?68?57?48?53?52?48?51?60?38?15?25?15?12
29?38?51?60?54?49?52?54?51?56?57?33?13?15 9 8
31?34?27?30?41?49?41?34?30?30?36?37?25?17?14 9
24?26?24?13?21?30?27?25?23?19?25?39?33?16?14?22
10?13?18?14?12 9?12?17?20?24?21?21?21?11 8?17
5 3 7?16?19?15?10?18?25?25?12 3 3 4 3 4
3 3 0 4?15?20?19?26?26?14 5 0 0 2 1 0
2 4 2 3 7?16?21?26?16 4 2 5 2 2 1 1
2 3 3 1 0 5?13?13 6 1 2 4 0 2 3 0
1 3 4 0 1 3 3 3 2 3 1 0 1 1 1 2
2 1 3 2 1 4 2 0 2 2 2 2 3 3 1 2
0 0 2 1 0 0 1 1 2 1 2 2 1 2 2 0
0 3 3 0 1 2 1 3 4 0 1 1 1 2 2 1
3 3 0 0 3 5 1 1 3 2 2 0 3 4 1 3
Result after table 3 linear transformation
26?39?62?70?72?63?54?60?70?67?59?39?29?32?35?31
39?46?70?80?76?76?64?50?58?58?71?51?23?39?34?25
38?49?72?80?67?57?62?61?56?60?70?45?17?29?18?14
34?45?60?71?64?58?61?64?60?66?67?39?15?17?10 9
37?40?32?36?48?58?48?40?35?35?42?44?29?20?16?10
28?31?28?15?25?35?32?29?27?22?29?46?39?19?16?26
12?15?21?16?14?10?14?20?24?28?25?25?25?13 9?20
6 3 8?19?22?17?11?21?29?29?14 3 3 4 3 4
3 3 0 4?18?23?22?30?31?16 5 0 0 2 1 0
2 4 2 3 8?19?25?31?19 4 2 5 2 2 1 1
2 3 3 1 0 6?15?15 7 1 2 4 0 2 3 0
1 3 4 0 1 3 3 3 2 3 1 0 1 1 1 2
2 1 3 2 1 4 2 0 2 2 2 2 3 3 1 2
0 0 2 1 0 0 1 1 2 1 2 2 1 2 2 0
0 3 3 0 1 2 1 3 4 0 1 1 1 2 2 1
3 3 0 0 3 5 1 1 3 2 2 0 3 4 1 3
Table 4 linear transformation and behind histogram equalization the result
60?110?182?203?207?185?157?176?203?195?173?110?72 85?96?81
110?132?203?221?214?214?187?144?170?170?205?147?50?110?92?56
106?141?207?221?195?167?182?179?164?176?203?129?41 72?42?38
92?129?176?205?187?170?179?187?176?193?195?110?39 41?34?33
103?113 85?100?138?170?138?113 96 96?120?126?72 45?40?34
67 81 67 39 56 96 85 72 64 49 72?132?110?44?40?60
35 39 47 40 38 34 38 45 53 67 56 56 56?37?33?45
29 27 32 44 49 41 34 47 72 72 38 27 27?28?27?28
27 27 0 28 42 50 49 76 81 40 28 0 0?25?22 0
25 28 25 27 32 44 56 81 44 28 25 28 25?25?22?22
25 27 27 22 0 29 39 39 30 22 25 28 0?25?27 0
22 27 28 0 22 27 27 27 25 27 22 0 22?22?22?25
25 22 27 25 22 28 25 0 25 25 25 25 27?27?22?25
0 0 25 22 0 0 22 22 25 22 25 25 22?25?25 0
0 27 27 0 22 25 22 27 28 0 22 22 22?25?25?22
27 27 0 0 27 28 22 22 27 25 25 0 27?28?22?27
Result behind table 5 sequential nonlinear filtering
255?255?255?255?255?255?255?255?255?255?255?255?255?255?255?255
255?255?255?255?255?255?255?255?255?255?255?255?255?255?255?255
255?255?255?255?255?255?255?255?255?255?255?255?255?255?255?255
255?255?255?255?255?255?255?255?255?255?255?255?255?255?255 33
255?255?255?255?255?255?255?255?255?255?255?255?255?255?255 34
255?255?255?255?255?255?255?255?255?255?255?255?255?255 40 60
255?255?255?255?255?255?255?255?255?255?255?255?255?255 33 45
29 27 32 44 49?255?255?255?255?255?255?255 27 28 27 28
27 27 0 28 42 50?255?255?255?255 28 0 0 25 22 0
25 28 25 27 32 44?255?255?255 28 25 28 25 25 22 22
25 27 27 22 0 29?255?255?30 22 25 28 0 25 27 0
22 27 28 0 22 27 27 27 25 27 22 0 22 22 22 25
25 22 27 25 22 28 25 0 25 25 25 25 27 27 22 25
0 0 25 22 0 0 22 22 25 22 25 25 22 25 25 0
0 27 27 0 22 25 22 27 28 0 22 22 22 25 25 22
27 27 0 0 27 28 22 22 27 25 25 0 27 28 22 27
(2) to filtering as a result image carry out region labeling, calculate the various features value that each demarcates the zone
Nonlinear filtering image employing as a result region growing algorithm is carried out region labeling, add up the grey level histogram that each demarcates the zone then,, calculate each regional area, gray average and histogram peak gray scale respectively according to following 4 formulas.
GP＝PI，H[PI]＝MAX{H[i]，i＝0，1，...，255}
Wherein, R[i] for gray-scale value in the zone is the collection of pixels of i, H[i] be the number of pixels of histogram i level gray scale, A is a region area, and GM is the area grayscale average, and GP is the histogram peak gray level.
(3) foundation of water body target area selection criterion
According to (2) step calculated feature values, can determine following water body selection criterion automatically: 1) zone of region area A＜A0 is non-water body zone; 2) zone of area grayscale average GM＞GM0 is non-water body; 3) zone of regional peak gray GP＞regional average gray scale GM is non-water body zone.Wherein A0 and GM0 value all have bigger redundance, get A0=100, GM0=64 here.
(4) the automatic extraction in water body zone is based on morphologic water body special topic information extraction aftertreatment
According to water body region criterion and each the regional eigenwert that (3) are set up, each zone of automatic benchmarking's note judges, thereby rejects clutter and other target automatically, selects the water body zone from alternative area.On this basis, pass through modal of morphological opening and closing operation, morphologic filtering (size of structural element can select 3 * 3,5 * 5,7 * 7,15 * 15) is carried out in the water body zone of selecting, remove small vector branch (removing the specific image details littler) than structural element, guarantee not produce the set distortion of the overall situation simultaneously, on the basis of morphologic filtering, extract the edges of regions vector, the vector that extracts is carried out length and area statistics, vector length and enclose area rejecting less than threshold value.Threshold value can be adjusted (for the general length threshold of high resolving power aviation remote sensing image is 25, and area threshold is 500), and the thematic information layer representation of Ti Quing is the shape file of ArcGIS software at last, i.e. the shp file layout.The a certain atural object special topic vector data example of table 6 for generating.
The a certain atural object special topic vector data that table 6 generates
Period special topic vector node coordinate (X, Y)
1~4 300600，3452250 300550，3452200 300475，3452200 300475，3452075
5~8 300450，3452050 300450，3451950 300425，3451925 300400，3451925
9~12 300350，3451875 300350，3451825 300325，3451800 300325，3451750
13~16 300350，3451725 300350，3451600 300325，3451575 300325，3451525
17~20 300350，3451500 300350，3451450 300375，3451425 300375，3451150
21~24 300350，3451125 300350，3451075 300375，3451050 300475，3451050
25~28 300500，3451075 300550，3451075 300575，3451100 300600，3451100
29~32 300625，3451125 301000，3451125 301025，3451150 301175，3451150
33~36 301200，3451125 301250，3451125 301250，3451225 301300，3451275
37~40 301375，3451275 301450，3451350 301575，3451350 301700，3451475
41~44 301900，3451475 301925，3451450 302150，3451450 302175，3451475
45~48 302175，3451550 302150，3451550 302100，3451600 302100，3451650
49~52 302075，3451675 302075，3451725 302100，3451750 302150，3451750
53~56 302150，3451850 302125，3451875 302075，3451875 302050，3451900
57~60 302000，3451900 301900，3452000 301900，3452025 301825，3452100
61~64 301800，3452100 301775，3452125 301750，3452125 301725，3452150
65~68 301700，3452150 301675，3452175 301600，3452175 301575，3452150
69~72 301550，3452150 301525，3452125 301500，3452125 301450，3452075
73~76 301425，3452075 301400，3452050 301375，3452050 301350，3452025
77~80 301300，3452025 301275，3452050 301250，3452050 301225，3452075
81~84 301025，3452075 301000，3452050 300850，3452050 300775，3452125
85~88 300775，3452150 300750，3452175 300750，3452250 300600，3452250
Claims (1)
1. extraction method of water body thematic information of remote sensing image based on sequential nonlinear filtering is characterized in that concrete steps are as follows:
(1) pre-service of remote sensing multiband image and sequential nonlinear filtering
According to water body spectral characteristic and remote sensing image band setting, select infrared or red wave band, indigo plant or green wave band as alternative wave band, carry out pre-service according to formula (1) and generate pending data plot layer,
BW
0＝B
L+k×B
L/(B
H+w) (1)
Wherein, B
LRepresent infrared or red wave band brightness value, B
HBlue or the green wave band brightness value of expression, BW
0Represent pretreated pixel brightness value, k is the transformation of scale coefficient, and w is for fear of removing zero problem, and to B
HA very little positive number that adds;
Adopt the sequential nonlinear filtering method, generate pending data plot layer formula (1) according to pre-service, remote sensing image is at first carried out the maximal value Filtering Processing, eliminate the dark pixel of discontinuous planar distribution in the image, the water body zone that is planar continuous distribution simultaneously is also weakened; Carry out medium filtering then and carry out noise reduction process; And then adopt the minimum value Filtering Processing to recover weakened water body zone in the maximal value Filtering Processing;
(2) to filtering as a result image carry out region labeling, calculate the various features value that each demarcates the zone
Adopt the region growing algorithm, image as a result in the step (1) is demarcated candidate's water body pixel earlier form the continuum, utilize formula (2)～(5) then, calculate every statistical nature in each zone: region area, area grayscale average, area grayscale histogram
The area grayscale histogram is:
Region area A is:
The area grayscale average:
The histogram peak gray level:
GP＝PI，H[PI]＝MAX{H[i]，i＝0，1，...，255} (5)
Wherein, R[i] for gray-scale value in the zone is the collection of pixels of i, H[i] be the number of pixels of histogram i level gray scale, A is a region area, and GM is the area grayscale average, and GP is the histogram peak gray level;
(3) foundation of water body target area selection criterion
1. the zone of region area A＜A0 is non-water body zone;
2. the zone of area grayscale average GM＞GM0 is non-water body;
3. the zone of histogram peak gray level GP＞area grayscale average GM is non-water body zone; Wherein A0 and GM0 value all have bigger redundance;
(4) the automatic extraction in water body zone is based on morphologic water body special topic information extraction aftertreatment
Water body region criterion according to step (3) is set up reaches each regional eigenwert, each zone of mark is judged, thereby rejected clutter and other targets automatically, obtains the water body target area; Pass through modal of morphological opening and closing operation, the thematic map that generates is looked like to carry out filtering, remove the specific image details littler than structural element, on the basis of morphologic filtering, the vector that extracts is carried out length and area statistics, to the vector length that extracts and enclose area rejecting less than threshold value, the thematic information layer representation of Ti Quing is the shape file of ArcGIS software at last, i.e. the shp file layout.
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