theremotesensingimagefusionmethod正文

1、The Remote Sensing Image Fusion Method ABSTRACT Remote Sensing Image Fusion is one of the key techniques in the Remote Sensing (RS) domain. With the rapid development of the RSinformation fusion has been playing an increasingly important role. After a brief introduction to the RS Tech no logy and RS

2、 in formati on fusi on, this paper describes the multi-spectrum image fusion in detail, with the emphasis on the PCA Fusion, the HIS Fusion and the Wavelet Fusion approach, whose mathematical foundation, principle and traits are explored in turn. Fin ally in the traditi onal image fusi on is propose

3、d on the basis of a new fusi on method based on PCA and HIS, such as the new image fusi on method fusi on, and through experime nt verificati on an alyzed the new method is feasible. KEY WORDS Remote Sensing Remote Sen si ng mage Fusion K-LFusion ； HIS Fusi on 1 Introduction Information Representati

4、on different level, multi-sensor remote sensing image fusion can be divided into the pixel level fusion, feature fusion and decision levelfusion.Pixel level fusion is the process of comprehensive information directly toobtain the various pieces of remote sensing image pixels, so the image segmentati

5、on, feature extraction work on the basis of more accurate and better visual effect.Pixellevelfusion is intuitive, simple to operate, and the application of the most widely used. Pixel-level fusion method IHS transform, wavelet transform, principal comp onent an alysis (PCA) and Brovey tran sform met

6、hod. Feature-level fusi on is the imagefeatureextracti on, edge,shape, con tour, texture, and other in formatio n will be extracted to a comprehe nsive an alysis of the fusi on process. Decisi on-level fusi on is the integration of a high level, often application-oriented decision support services.

7、2 The pixel-level image fusion Pixel level fusion process can be divided into four general steps: pretreatment, tran sform and inv erse tran sform (rec on structed image). Papers assumethat most of the studies pixel level fusi on the fused image registratio n, but there are a nu mber of research pap

8、ers specializ ing in registrati on process ing the tran sformatio n stage using the main square: PCA, sometimes also known as the PCT; HIS transform; multiresolution method pyramid algorithms and multi-resolution wavelet transform. Integrated phases will be fused to the integrated processing of the

9、result of the transformation of the image, to obtain the final fused image. Integrated approach can be divided into: the selectio n method. I.e., accord ing to certa in rules, respectively, to select the tran sform coefficie nt with the image is fused to form a new set of tran sform coefficients; we

10、ighting method. I.e. some weighted average algorithm to the transform coefficients of the different fusion image consolidated into a new set of transform coefficients; optimization method. That is different depending on the applicati on, to con struct a performa nee evaluati on of fusi on effect, an

11、d the con solidated results of the performa nee optimal. I nv erse tran sform stage is based on a transform coefficient obtained by the synthesis stage of the inverse transform operati on, to obta in a fus ing image. 3 Feature-level fusion The feature fusion between pixel-level fusion and decision l

12、evel fusion in termediate-level fusi on. Feature-level fusi on is based on the pixel level fusi on, usingthe parameter template, statistical an alysis, mode associated geometry associated target recog niti on, feature extracti on, fusi on method to exclude false characteristics to facilitate system

13、judgment. Feature-level fusion in two studies, integration of the goal state data fusion and target characteristics. The layer fusion advantage achieved con siderable in formatio n compressi on, to facilitate real-time process ing, to maximize its fusi on results give n feature in formati on n eeded

14、 by the decisi on an alysis. Research methods comme nee from the cluster an alysis, Dempster-Shafer reas oning method, Bayesia n estimati on method, n eural n etwork method. For multi-source images of the same surface features target feature images were extracted from the edge area, spectral, textur

15、e feature in formati on; thus associatio n betwee n the image features as well as the location and description of the fusion features form a feature vector, which is more accurate land reflect the essential characteristics of the target, and to improve the remote sensing image classificati on and ta

16、rget descripti on accuracy. Feature-level image fusion method: A, Dempster-shafer reas oning method: DS method of reas oning structure,bottom-up divided into three levels: The first level is the update (updated) information to be combined with a full time in depe ndent of a set of reports from the s

17、ame sen sor, the sen sor in order to reduce the ran dom error. The second level is inferred logic sensor reports a certain credibility in some credible target report. The third level is the syn thesis, the syn thesis of reports from several in depe ndent sen sor for a total output of cluster an alys

18、is: mai nly used for target ide ntificati on and classificati on. B, Bayesian estimation method: Bayesian inference given a priori likelihood estimation and additional evidenee of conditions. Can update a hypothetical likelihood function. However, the method requires prior knowledge, and when the nu

19、 mber of solvable assumpti ons and con diti ons related, it is very complicated. C, en tropy method: a new tech no logy as a fusi on the Con tact in formati on content of the measure, calculated with the assumpti on. Feature-level target state data fusi on is mai nly used in the field of multi-se ns

20、or target tracking. Fusion System first preprocessing on the sensor data in order to complete the data calibrati on, the n mainly achieved related to the parameter estimates of the state vector. The feature level fusion advantages: to achieve a considerable information compressi on, is con ducive to

21、 real-time process ing, and because the characteristics are provided directly with the decision analysis, and thus can maximize fusion results give n feature in formatio n n eeded by the decisi on an alysis. Feature-level image fusi on Disadva ntages: poor tha n pixel level fusi on precisi on. Most

22、C3I systems, data fusion research are expanded in the level. 4 The decision-making level image fusion Decision-level fusion is the integration of a high level, it first for each data attribute specification, then the results of the fusion, the fusion property description of the target or the en vir

23、onment. The results provide the basis for comma nd and con trol decisi on-mak ing. Therefore, the decisi on-mak ing level fusi on must proceed from the needs of the specific decision-making problems, and take full advantage of the measureme nt object extracted feature fusi on of feature in formatio

24、n using appropriate fusi on tech no logy to achieve. Decisi on level fusi on is the final result of three fusi on is true then pecificdecision-making objectives, the fusion results directly affect the decisi on-mak ing level. 5 The image Fusion Method Comparison As the research object, differe nt pu

25、rposes, image fusi on method can also arievd, its main steps are summarized as follows: (1) Pretreatme nt: The selected features should be the same for the two types of image acquisition processing such data, unified data format, select registration feature point resolution. Denoising of a sequenee,

26、 and enhance the tomographic image, created accordi ng to the target characteristics that the tran sformed error of the two data sets of a mathematical model of the minimum criterion; (2) The data fusi on database used: two-dime nsional or three-dime nsional case, the target object or area of in ter

27、est is divided. The features should be selected to achieve a certain error criterion corresponding to the two points on the image, which may be labeled the physical mark, it can be an atomical feature point; (3) feature points for image registration: regarded as a three-dimensional recon struct ion

28、and display the nu mber of data sets betwee n lin ear or non li near tran sformatio n that tran sformed two data sets qualitative and qua ntitative an alysis of the same physical marker error reaches the minimum ; (4) integration of image creation: after registration of the two modes in the same coo

29、rdinate system images will each useful information fusion expression into a mathematical model; (5) Extract ion: extract from the fusi on image and measureme nt parameters to obta in the respective determ in ati on result. Feature fusion has the advantage of fusion results thus achieved considerable

30、 in formatio n compressi on to facilitate real-time process ing. Si nee the extracted features and decisi on an alysis directly related to in formatio n compressi on to achieve a considerable decision analysis gives the characteristics of the information required. Most curre nt data fusi on systems

31、are expa nded on that level. Decisi on level fusi on has the adva ntage of image fusi on tech no logy through various characteristics of in put in formatio n and the results described in the decisi on-mak ing, so the decisi on level fusi on small amount of data, an ti-i nterfere nee ability. The mai

32、n advantage of decision level fusion can be summarized as follows: (a) Communication and transmission requirements low, it is determined less by the data. (b) High fault tolera nee. Both robust ness and accuracy of the in tegrati on, through appropriate fusi on method be elimi nated. (c) Data requir

33、eme nts are lower. Can effectively reflect the full range of the target can be homogeneous or heterogeneous, the dependence of the sensor and requireme nts reduced. (d) Analysis capability is very strong. Research data fusion architecture and en vir onmen tal in formatio n to meet differe nt applica

34、ti on n eeds. Because of the pre-process ing and feature extracti on have higher requireme nts, so the decision level fusion is costly. We should remember that the purpose of fusi on is to get higher resolutio n image, making the image more abundant spectral information, so we should be more practic

35、al applicati ons utilize three image fusi on method, dogmatism can not commit an error. Each other, interspersed with three fusion methods applied so that we applied research in remote sensing image fusion efficiency optimized to maximize efficie ncy. 6 Summary and Outlook Remote sensing image fusio

36、n is an important tool in remote sensing image analysis. Multi-scale remote sensing image fusi on by multiscale in formatio n compleme ntary to elim in ate red undancy and con tradicti ons, can improve the accuracy and reliability of remote sensing information extraction to improve data utilization. Can improve the spatial resolution of the remote sensing image by the image fusion technique to enhan cethe characteristics of the target, and to improve the classificatio n accuracy and dyn amic mon itori