CN102663394A - Method of identifying large and medium-sized objects based on multi-source remote sensing image fusion - Google Patents
Method of identifying large and medium-sized objects based on multi-source remote sensing image fusion Download PDFInfo
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Abstract
The invention provides a method of identifying large and medium-sized objects based on multi-source remote sensing image fusion. The method comprises that multispectral and SAR images are segmented into image areas according to spectrum distribution between different objects and differences of electromagnetic radiation, and a potential area of an object is extracted according to experience and knowledge of object distribution, so that characteristic extraction and object identification are more targeted while identification efficiency and identification accuracy of the system are improved; that based on the above, object areas are determined by extracting object contour, spatial layout and the like, and sub-object areas are divided according to spatial layout relation of the object contour and sub-object spatial layout; and that sub-object characteristics are extracted, and with the guidance of object characteristic set and object prior knowledge, sub-object identification and object verification are finally realizedby execution of identification rules and matching of characteristics.
Description
Technical field
The present invention relates to a kind of remote sensing images target automatic identifying method, relate in particular to and a kind ofly realize the automatically method of identification of big-and-middle-sized target, belong to the digital image processing techniques field based on the multi-source remote sensing image feature extraction techniques.
Background technology
The application of remote sensing technology has contained many aspects such as environmental monitoring, resource exploration, the reallocation of land, diaster prevention and control, ground mapping, weather forecast, for great contribution has been made in the national economic development.Wherein the identification of big-and-middle-sized terrain object such as airport, harbour, bridge, oil depot, boats and ships, aircraft is one of research focus of remote sensing technology application.Along with the fast development of aviation/spacer remote sensing technology, the image of various airborne, visible lights that satellite borne sensor obtains, radar, type such as multispectral is that Target Recognition provides abundant information source, also makes the utilization of information become complicated.There is complementarity between information, can more intactly reflects the characteristic of target, help improving discrimination, get rid of false-alarm same target different sensors acquisition.
Multi-source image merges from information sign level says that can be divided into Pixel-level, characteristic level and three levels of decision level, identification belongs to feature level fusing method based on the multi-source image fusion goal, and these class methods are carried out analysis-by-synthesis and processing with the characteristic information that extracts.In the current target automatic identifying method, the most representative have based on statistics, based on knowledge and based on the target automatic identifying method of model.Target automatic identifying method based on statistics is on the basis that great amount of samples is trained, and obtains the statistical study of target property, adopts the characteristic matching of space length tolerance to come recognition objective.This method relatively effectively, but is difficult to solve problem such as object construction variation under the low situation of target background complexity.Based on the target automatic identifying method of knowledge mainly be with expert system application in automatic target identification, overcome the limitation of classical statistics pattern-recongnition method to a certain extent, but be difficult in scene, find effectively and organization knowledge.Based on the target automatic identifying method of model, on basis, extract certain target signature to object modeling, in conjunction with the model parameter that some supplementary knowledges come target-marking, identifying is the matching process of model and target signature.The changeable structure that is difficult to be adapted to complex target based on the method for model.
Remote sensing images have the characteristics of wide coverage, and full figure extraction target signature calculated amount is big, and interference is many, and the location in the potential zone of target helps to dwindle the scope of target's feature-extraction and analysis.And pay little attention to for the potential zone of target in the existing method.In the existing target's feature-extraction process,, be that extraction target signature as much as possible is analyzed especially based on the method for statistics.And in the realistic objective identifying for certain specific objective, the validity of its different characteristic is different, selects effective characteristic, helps reducing the complexity of target's feature-extraction and analysis.Although the version of target is changeable, generally speaking, the formation of target (sub-goal) is relatively stable, and the spatial relationship between the parton target also is certain.Existing recognition methods fails to bring into play the formation and the effect of the spatial relationship between sub-goal in identification of target.The complementarity of combining target characteristic in the multi-source remote sensing image, the present invention proposes a kind of big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration from the aspects such as effective checking of effective extraction of effective extraction in the potential zone of target, target signature and analysis, target.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of big-and-middle-sized target identification method based on multi-source remote sensing image (panchromatic, multispectral, SAR) Feature Fusion.This method synthesis utilizes the Feature Extraction Technology and the fusion method of multi-source remote sensing image; Realization has been accomplished big-and-middle-sized Automatic identification of targets such as airport, harbour, bridge, oil depot, boats and ships, aircrafts from a series of images treatment steps such as Region Segmentation, feature extraction and analysis, Target Recognition checkings.
For realizing above-mentioned purpose, the present invention adopts following technical proposals:
Based on the big-and-middle-sized target identification method of multi-source remote sensing image co-registration, its characteristics are to comprise the steps:
(1) feature hierarchyization and relationship analysis: combine human cognitive, target signature is carried out stratification divide, set up the selection criterion of high-level characteristic centering low-level feature;
(2) the potential extracted region of Region Segmentation and target: utilize multispectral and SAR remote sensing images to carry out Region Segmentation; The zone at combining target and targeted environment place; Confirm image segmentation zone (terrain classification type), and carry out Region Segmentation to multispectral respectively with the SAR image according to the spectrum and the electromagnetic scattering characteristic of all kinds of atural objects; Combining target geographic distribution knowledge analyzes two ways and possibly have target to be identified in the cut zone of multispectral and SAR image, obtain the potential zone of target.
(3) extract and the sub-goal area dividing target area: according to the potential regional location of target, in full-colour image, utilize characteristics such as space layout and profile further to draw a circle to approve the target area; Utilize the spatial relationship of sub-goal in the concept characteristic, target is decomposed into separate sub-goal zone;
(4) sub-goal identification and target checking: the selection criterion that utilizes attributive character, topological structure characteristic centering low-level feature; In panchromatic, multispectral and SAR image, extract the local feature of sub-goal respectively; And characteristic analyzed, thereby judge whether it is sub-goal; According to the sub-goal that identifies, and the spatial relationship between sub-goal, target configuration knowledge etc., target is verified.
The recognition methods that the present invention proposes has different as follows with classic method: entire identification process is divided into Region Segmentation in the present invention and extract and sub-goal area dividing, sub-goal identification and target checking the potential extracted region of target, target area.Under the guidance of target concept characteristic; Extract the attributive character of target etc., realize the Region Segmentation of image, obtain the attributive character of targeted environment and the attributive character of target self; Thereby obtain the potential zone of target, dwindled target signature and further extracted and the scope of analyzing.Through contour feature and space layout characteristic in the potential extracted region target of target; Mark off the sub-goal zone; And discern through the middle low-level feature antithetical phrase target area of sub-goal, accomplish the checking of target through the identification of sub-goal and the space layout relationship analysis between sub-goal.Coming evaluating objects from the space layout between target configuration (sub-goal) and target configuration is that other recognition methods institutes are NM; The validity of low-level feature extraction and analysis in so not only helping; The false-alarm that does not meet target configuration and formation space layout can also be effectively got rid of, the efficient and the accuracy of Target Recognition can be improved.
Description of drawings
Below in conjunction with accompanying drawing and embodiment the present invention is done further detailed description.
Fig. 1 is this big-and-middle-sized target identification method process flow diagram based on the multi-source remote sensing image co-registration;
Fig. 2 is in this big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration, sets up feature selecting criterion process example;
Fig. 3 is in this big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration, multispectral image Region Segmentation decision tree;
Fig. 4 is in this big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration, the potential extracted region process flow diagram of target;
Fig. 5 is in this big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration, the main type of space layout characteristic;
Fig. 6 is in this big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration, utilizes the process synoptic diagram of profile and space layout feature extraction sub-goal;
Fig. 7 is this reasoning flow process based on the big-and-middle-sized target identification method of multi-source remote sensing image co-registration.
Embodiment
As shown in Figure 1, it is four key steps that realization of the present invention is divided into, and is respectively: extract and sub-goal area dividing, sub-goal identification and target checking the potential extracted region of feature hierarchyization and relationship analysis, Region Segmentation and target, target area.Be example with big-and-middle-sized Target Recognition such as the airport in the multi-source remote sensing image (multispectral, SAR, panchromatic), harbours below, practical implementation step of the present invention is carried out detailed explanation.
(1) feature hierarchyization and relationship analysis: target signature is divided into five levels, and, analyzes the selection effect that high-level characteristic centering low-level feature has, set up the selection criterion of high-level characteristic centering low-level feature according to the correlation experience knowledge of target;
1) multiple stratification of target signature is represented
The target correlated characteristic is many and complicated; For can better the descriptive analysis target signature; The present invention proposes a kind of multi-level method for expressing of target signature of five layer architectures; Be specially: characteristic is divided into low-level feature, middle level characteristic, topological structure characteristic, attributive character and five levels of concept characteristic, every layer of characteristic mainly comprise in have:
1. low-level feature; The characteristic relevant with single pixel concluded low-level feature, typically comprise atural object radiation value in tone, brightness, gray-scale value, the remote sensing images (in multispectral image, show as the pixel spectral value, in full-colour image, show as the pixel gray scale);
2. middle level characteristic; To conclude the middle level characteristic with the relevant characteristic of block of pixels (a plurality of pixels are assembled the zone that forms), typically comprise the edge in texture (like gray scale symbiosis square, gradient, entropy etc.), region shape (like area, length and width, length breadth ratio, geometric moment, skeleton etc.) and zone (interior) in the zone etc.;
3. the topological structure characteristic reduces topological structure level high-level characteristic with the spatial relationship between single object or a plurality of object, typically comprise space layout relation between contours of objects, target configuration part (as adjacent, mutually from, parallel etc.) and direction etc.;
4. attributive character; The characteristic that reflection object physical attribute, state and imaging mechanism is relevant reduces attributive character, the polarization mode the when wave band when typically comprising time, the remote sensing images imaging of atural object material (like water, plant, culture etc.), remote sensing images imaging, SAR image imaging etc.;
5. concept characteristic reduces concept characteristic with object entity and the entity knowledge relevant with the object entity;
Topological structure characteristic, attributive character and concept characteristic belong to high-level characteristic, and Pixel-level low-level feature and region class middle level characteristic belong to middle low-level feature.The main foundation that level is divided is: by the visual information of low layer, progressively rise to semantic information.
2) set up the selection criterion of target high-level characteristic centering low-level feature
Mainly set up the feature selecting criterion according to the level division of characteristic used in the Target Recognition experimental knowledge, characteristic correspondence and the relation between characteristic.Mainly set up the selection criterion of concept characteristic to attributive character, topological structure characteristic and middle low-level feature, and the selection criterion of attributive character, topological structure characteristic centering low-level feature.
Preferentially extract the significant attributive character of target, topological structure characteristic and middle low-level feature according to target concept, wherein the relation of target attributive character significant with it, topological structure characteristic and middle low-level feature is as shown in table 1.In like manner, according to the relation between middle low-level feature and high-level characteristic, set up the selection criterion of attributive character, topological structure characteristic centering low-level feature, typical criterion is as shown in table 2.In Fig. 2; Aircraft is selected attributive character, topological structure characteristics such as slickness, metal, adjacent, symmetry under the guidance of experimental knowledge; Smooth attributive character selects textural characteristics such as gradient to extract, and adjacently extracts with symmetric space layout relationship feature selecting edge and shape facility.
The relationship analysis of table 1 target concept characteristic and attributive character, topological structure characteristic, middle low-level feature
| Target | Attributive character | The topological structure characteristic | Middle low-level feature |
| The airport | Smooth, material | Parallel, adjacent | Area |
| The harbour | Material | Parallel, vertical | Do not have |
| Bridge | Material | Parallel, adjacent | Do not have |
| Oil depot | Material | Be separated by, array | Do not have |
| Ship | Material | Do not have | Area, shape |
| Aircraft | Material, smooth | Symmetrical, adjacent | Area |
The relationship analysis of table 2 attributive character, topological structure characteristic and middle low-level feature
| Middle low-level feature | |
| Smooth attributive character | Texture, backscatter intensity |
| The material properties characteristic | Spectrum, backscatter intensity |
| The topological structure characteristic | Edge, shape |
(2) the potential extracted region of Region Segmentation and target
At first, utilize multispectral and SAR remote sensing images to carry out Region Segmentation, wherein multi-spectral remote sensing image adopts the partitioning scheme of cutting apart decision tree based on the multispectral image of spectral signature, and the SAR remote sensing images adopt the Markov dividing method.
The Multi-spectral Remote Sensing Data that invention is mainly cut apart is the multispectral image of the 8bit Quickbird of 2.44 meters resolution; Selected part water; Vegetation and culture wait for that cut zone is as sample areas; To these sample areas from each band spectrum value; Spectral ratio and NDVI characteristics such as (normalized differential vegetation indexs) is carried out statistical study; Obtain the multispectral image based on spectral signature as shown in Figure 3 and cut apart decision tree; B wherein; G; R and N represent blue wave band respectively; Green wave band; The spectral value of red wave band and near-infrared band
Min and max represent 4 minimum and maximum spectral values in the wave band, R respectively
MaxRepresent maximum ratio between 4 wave bands.Utilize multispectral image to cut apart decision tree multispectral image is cut apart, obtain water, vegetation, common culture, large-sized artificial building and Gao Liang etc. and cut apart the zone.
To the characteristics of SAR image, the present invention adopts the image partition method based on MRF, and the zone in the SAR image is divided into weak back scattering zone, common back scattering zone and strong back scattering zone.
Then, utilize the priori such as environmental characteristic and attributive character of target, in the potential zone that the big regional center that has extracted sets the goal.In the potential extracted region process of target, involved multispectral and SAR image segmentation zone and part principal character are as shown in table 3, and idiographic flow is as shown in Figure 4.For not omission target, the used qualifications of its analytic process is often not strong.
Cut zone and characteristic that the potential regional analysis of table 3 target mainly utilizes
| Target | Multispectral image | The SAR image |
| The airport | The large-sized artificial construction area, area is greater than 100,000 square meters | Weak back scattering zone |
| The harbour | The large-sized artificial construction area is positioned at the land and water boundary place | Strong back scattering zone |
| Bridge | Common artificial construction area, both sides are the waters | Strong back scattering zone |
| Oil depot | Highlight regions, circle | Strong back scattering zone |
| Ship | Non-aqua region is in the waters | Strong back scattering zone |
| Aircraft | Highlight regions is positioned at the airport | Strong back scattering zone |
(3) extract and the sub-goal area dividing target area: according to the potential regional location of target, in full-colour image, utilize characteristics such as profile and space layout further to draw a circle to approve the target area; Utilize the spatial relationship of sub-goal in the concept characteristic, target is decomposed into separate sub-goal zone;
1) extract the target area
The level set method that at first adopts based target and background contrasts to strengthen is extracted contour feature.Its step is following:
1. utilize wavelet transformation that image mapped is wavelet low frequency component (LL) and level, vertical, diagonal high fdrequency component (HL, LH, HH).
2. set up the disaggregated model of Wavelet Component, specific as follows:
Wherein, c is a profile, c
iBe the target area, c
0Be the background area, Length (c) is the length of profile, u
K, 1Be the Wavelet Component value of target area, u
K, 2Be background area Wavelet Component value, u
k(x y) is respectively LL, HL, LH and the HH component of wavelet transformation, μ, λ
1, λ
2, λ
3And λ
4Be weighting coefficient, the weights λ of Wavelet Component
1, λ
2, λ
3And λ
4Set according to its conforming power.
Then profile is carried out vector quantization, utilize the method for perception marshalling that the vector line segment is analyzed, comprise mainly that the line segment on the same direction connects, the vertical relation between line segment between parallel relation, line segment etc.
2) sub-goal Region Decomposition
Result in conjunction with the contour vector marshalling; Confirm the contour edge of remarkable sub-goal earlier according to the length of remarkable sub-goal, curvature, rim space relation etc.; Thereby obtain remarkable sub-goal, utilize sub-goal space layout characteristic and objective contour edge again, obtain other non-remarkable sub-goals.Wherein significantly sub-goal is meant to have that area is big, the edge long, the target configuration part of regular shape characteristic, and other target configurations partly are called non-remarkable sub-goal.Table 4 is seen in remarkable sub-goal of the part of target and non-remarkable sub-goal definition.The object space spatial layout feature comprise in abutting connection with, intersect, leave mutually, comprise, parallel, vertical, pass through, array etc., see Fig. 5.Fig. 6 has provided airport target, by contour feature, in conjunction with the space layout characteristic, obtains the process in sub-goal zone.
The main sub-goal of table 4 target
| Target | Remarkable sub-goal | Non-remarkable sub-goal |
| The airport | Runway, secondary runway | Hardstand, taxiway |
| The harbour | The berth | Access bridge |
| Bridge | Bridge floor | Do not have |
| Oil depot | Oil drum | Do not have |
| Ship | Hull | Pilothouse |
| Aircraft | Fuselage, wing | Tail |
(4) sub-goal identification and target checking: the selection criterion that utilizes attributive character, topological structure characteristic centering low-level feature; In panchromatic, multispectral and SAR image, extract the local feature of sub-goal respectively; And characteristic analyzed, thereby judge whether it is sub-goal; According to the sub-goal that identifies, and the spatial relationship between sub-goal, target configuration knowledge etc., target is verified.The characteristic of sub-goal comprises spectral value, the backscatter intensity in the SAR image and the shape facility in the full-colour image of each wave band in the multispectral image.Adopt bottom-up target verification mode, low-level feature is discerned sub-goal in promptly utilizing, and utilizes topological structure characteristic and attributive character etc. to the target checking, and gets rid of false-alarm.
1) sub-goal identification
Utilize the attributive character in the embodiment (1), the selection criterion of topological structure characteristic centering low-level feature; The attributive character of combining target, topological structure characteristic; In panchromatic, multispectral and SAR image, extract the local feature of sub-goal respectively; And, sub-goal is discerned according to local feature.
The middle low-level feature that in full-colour image, extracts mainly comprises characteristics such as the area of target, external square length, form parameter, and wherein, the account form of form parameter is P
2/ (4 π S), P is the girth of target, S is the area of target.
The middle low-level feature that in multispectral image, extracts mainly comprises spectral value, the spectral ratio between wave band, NDVI value, each band spectrum average of zone of each wave band etc.
The middle low-level feature that in the SAR image, extracts mainly comprises gray-scale value, regional average, gradient etc.
2) target checking
According to the sub-goal that identifies, and the spatial relationship between sub-goal, target configuration knowledge etc., target is verified.Identification like the airport; If the sub-goal that identifies comprises runway, secondary runway, hardstand and connecting taxiway; Then thinking the formation knowledge that meets target, further, is to meet space layout relation between sub-goal between runway and secondary runway if runway that identifies and secondary runway are parallel space layout relationship, connecting taxiway; Then think to meet the space layout relation between sub-goal, thereby realize the checking of target.
3) reasoning process
For the knowledge base that realizes the support effect of above characteristic and knowledge, constructed being used to support the Target Recognition reasoning process to the Target Recognition process, be used to store the intermediate result that reasoning process produces factbase, be used to store the feature database of each category feature empirical value of target.
Knowledge base is represented knowledge with the form of production, and the expression structure of rule is described below with the BNF normal form:
< rule>:=RULE < regular number >
PREMISE ($AND{ < condition>})
ACTION{ < action>}
< condition>:=< simple condition>| ($OR{ < simple condition>})
Wherein, simple condition representes that with two types of simple function ASK_FACT and ASK_CHARACTER its BNF normal form is described below:
(ASK_FACT < object>< attribute>< value >)
(ASK_CHARACTER < object>< attribute>< value >)
When inference machine makes an explanation execution to rule; ASK_FACT function indication inference machine inquiry factbase confirms whether this object has this property value, and ASK_CHARACTER function indication inference machine query characteristics storehouse confirms whether the characteristic (feature set) of this object meets definition in advance.
Action in the rule representes that with simple function CONCLUDE its property value with object is added factbase as the reasoning conclusion, and the BNF normal form of CONCLUDE function is described below:
(CONCLUDE < object>< attribute>< value >)
The present invention shows with above-mentioned form shfft and has collected 45 rules in knowledge base that wherein, 19 of rules are detected in potential target zone and target area, and are as shown in table 5; 26 of identification proof rules, as shown in table 6.
Rule is detected in potential target zone and target area in table 5 knowledge base
| Regular number | Former piece | Consequent |
| R27 | Inquire target type to be identified | The potential zone of search related objective |
| R28 | The potential zone of search airport target | The large-sized artificial building |
| R29 | The searching code head marks potential zone | The land and water boundary place |
| … | … | … |
Target Recognition proof rule in table 6 knowledge base
| Regular number | Former piece | Consequent |
| R01 | There is target to be declared | Check target type to be declared |
| R02 | Target type to be declared is the airport | Whether this airport is waited to declare target and can be divided |
| R03 | Target type to be declared is a harbour | Whether this harbour is waited to declare target and can be divided |
| … | … | … |
Potential target zone and target area are detected rule and are mainly used in the extraction of instructing the potential zone of target, waiting to declare target, and the extraction of relevant middle low-level feature; The major function of identification proof rule is that low-level feature is discerned each ingredient of big-and-middle-sized target in utilizing, and utilizes the composition of target and the completion of spatial relationship characteristic to treat the checking of declaring target.
Be example wherein, represent as follows with sub-goal recognition rule R11:
Rule 11:
If: the airport waits that the sub-goal of declaring target meets the primary runway characteristic;
So: this specific item is designated as primary runway.
Its formalized description is following:
RULE11
PREMISE(ASK_CHARACTER?SUB_TARGET?EIGENVECTOR
RUNWAY_CHARACTER)
ACTION(CONCLUDE?SUB_TARGET?TYPE?RUNWAY)
Wherein, whether the feature set that regular former piece is illustrated in inquiry primary runway in the feature database conforms to the feature set of this sub-goal, and rreturn value is 0 or 1; The rule consequent is represented the type mark of this sub-goal for " primary runway " and deposit in the factbase.
The matched rule of characteristic is true in the min/max interval of priori eigenwert for the eigenwert of input, otherwise is false.Through the eigenwert statistics to related objective in all kinds of images, the present invention has summed up the empirical features value scope of each class targets and has been stored in the feature database, and is as shown in table 4.
The empirical features value of each class targets in table 4 feature database
Reasoning process adopts the inference method that knowledge drives and data-driven combines, in conjunction with depth-first search strategy completion search and identification to intended target in multi-source image.Reasoning process is constantly searched for the correlated characteristic of target to be identified through the feature selecting criterion, activates the corresponding characteristic extraction operator.The result that the action that these regular consequents comprise produces can be used as the new fact again, activates other rules and comes further to find new fact.So analogize, the formation rule chain is finally accomplished identifying.Identification reasoning with airport target is an example, and is as shown in Figure 7 by the reasoning flow process that said process causes.
Big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration provided by the present invention mainly proposes for big-and-middle-sized object recognition rate is special in the raising high-resolution remote sensing image.But obviously, method provided by the present invention can also be expanded and be used for other type identification of targets, and the beneficial effect of being obtained also is similar.
More than the big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration provided by the present invention has been carried out detailed explanation, but obviously concrete way of realization of the present invention is not limited thereto.For the those skilled in the art in present technique field, the various conspicuous change of under the situation that does not deviate from claim scope of the present invention, it being carried out is all within protection scope of the present invention.
Claims (7)
1. based on the big-and-middle-sized target identification method of multi-source remote sensing image co-registration, it is characterized in that:
(1) on the basis that the target signature level is divided; Set up the selection criterion of target high-level characteristic centering low-level feature; Comprise the selection criterion of concept characteristic to attributive character, topological structure characteristic and middle low-level feature, and the selection criterion of attributive character, topological structure characteristic centering low-level feature;
(2) utilize the selection criterion of the concept characteristic of target to attributive character; Spectral distribution and electromagnetic scattering characteristic according to atural object are carried out Region Segmentation to multispectral respectively with the SAR image, in multispectral and SAR image segmentation zone, obtain the potential zone of airport, harbour, oil depot, bridge, aircraft, boats and ships class targets;
(3) in the corresponding potential zone of target of full-colour image, utilize space layout and contour feature to extract the target area, utilize the spatial relationship of sub-goal in the concept characteristic again, target is decomposed into separate sub-goal;
(4) utilize the selection criterion of attributive character, topological structure characteristic centering low-level feature, in the target area of panchromatic, multispectral and SAR image, extract the characteristic of sub-goal respectively, and carry out the identification of sub-goal and the checking of target.
2. the big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration as claimed in claim 1 is characterized in that:
In the said step (1), the target signature level is divided clarification of objective is divided into five levels: low-level feature, middle level characteristic, topological structure characteristic, attributive character and concept characteristic.
3. the big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration as claimed in claim 1 is characterized in that:
In the said step (2), the Region Segmentation type is waters, vegetation territory, common culture territory, large-sized artificial building territory and highlight regions.
4. the big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration as claimed in claim 1 is characterized in that:
In the said step (3), space layout is divided into: in abutting connection with, intersect, leave mutually, comprise, parallel, vertical, pass through, array type; The level set method that the method for utilizing contour feature to extract the target area adopts based target and background contrasts to strengthen.
5. the big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration as claimed in claim 1 is characterized in that:
In the said step (3); Utilize the method for perception marshalling that objective contour is described; Confirm the contour edge of remarkable sub-goal earlier in conjunction with length, curvature, the spatial relationship of contour edge; Thereby obtain remarkable sub-goal, utilize sub-goal spatial relationship and objective contour edge again, obtain other non-remarkable sub-goals.
6. the big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration as claimed in claim 1 is characterized in that:
In the said step (4), the characteristic of sub-goal comprises: the middle low-level feature that in full-colour image, extracts mainly comprises the area of target, external square length, form parameter, edge curvature characteristic; The middle low-level feature that in multispectral image, extracts mainly comprises spectral value, the spectral ratio between wave band, NDVI value, each band spectrum average of zone of each wave band; The middle low-level feature that in the SAR image, extracts mainly comprises gray-scale value, regional average, gradient.
7. the big-and-middle-sized target identification method based on the multi-source remote sensing image co-registration as claimed in claim 1 is characterized in that:
In the said step (4), adopt bottom-up target verification mode, low-level feature is discerned sub-goal in promptly utilizing, and utilizes topological structure characteristic and attributive character that target is verified, and gets rid of false-alarm.
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