Mapping without the sun

zhang, jixian

Bibliographic data

Monograph

Persistent identifier:: 856578517

Author:: Zhang, Jixian

Title:: Mapping without the sun

Sub title:: techniques and applications of optical and SAR imagery fusion ; Chengdu, China, 25 - 27 September 2007

Scope:: 1 Online-Ressource (III, 352 Seiten)

Year of publication:: 2007

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856578517

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: THE OPTIMIZING METHOD OF FUSING SAR WITH OPTICAL IMAGES FOR INFORMATION EXTRACTION. Feng Xie, Yingying Chen, Yi Lin

Document type:: Monograph

Structure type:: Chapter

24 2. FUSION ALGORITHMS The theory and the algorithms of the image fusion have been studied widely, including those using several different multi scale transforms and those using no transforms (e.g. additive (ADD) fusion which weights the two source images directly). The tests included the most frequently employed MSD fusion approaches: the Laplacian pyramid algorithm (LPT), and the gradient pyramid (GP) and the ratio-of-low-pass pyramid (RoLP) are the same kind; the morphological (MORPH) pyramid algorithm [8]; the discrete wavelet transform (DWT) algorithm and the shift invariant DWT (SiDWT) fusion algorithm. For each basic algorithm configuration, multiple alternatives for the activity level measurements, grouping methods, combining methods, and consistency verification methods from the framework described in [6] were considered. After registering the same region of SAR and optical images as well as possible, based upon the preliminary judging and testing, some promising fusion approaches are proposed. The alternative of the combining method is employed at the lowest frequency band for the pyramid transform-based fusion, and low-low band for DWT fusion. In the process of determining the methods in the test, many algorithms were eliminated because some of them performed quite poorly. For example, consider the contrast pyramid (CONTR) and the ratio of low pass pyramid fusion (RoLP) [9] is the similar and too many algorithm-created spots in the fused images. Therefore, we substitute the CONTR fusion and the RoLP fusion algorithm with LPT algorithm. Many other methods were eliminated for similar reasons (poor performance in a reasonable number of cases) or because they always performed similarly to other approaches. At each sample position, a decision made on how the MSD representations of the source images should be used to construct the MSD representation of the fused image. This decision is based on a quantity called the activity-level measurement. The activity level of an MSD coefficient reflects the local energy in the space spanned by the term in the expansion corresponding to this coefficient. There are three methods to computer the activity level just as [6] expatiate: coefficient-based activity (CBA); window-based activity (WBA); and region-based activity (RBA). When determining the coefficients of the MSD, these coefficients may be associated with each other or not. Determining these coefficients together or not is called no grouping (NG) schemes. If the corresponding coefficients in the same decomposition scale are jointly constrained to take the same decision, we call this a single scale grouping (SG) scheme. So a multi-scale grouping (MG) is that consider the different frequency coefficients together. For example the LAP fusion algorithm, the NG and SG are the same since there is only one frequency band in each decomposition level. Then we must consider how to combine the source MSD coefficients to produce the composite MSD representation. There are at lease two alternatives, the choose-max (CM) scheme and the weighted average (WA) scheme, which they appear most frequently in the documents. In the paper we emphasize on using adaptive multi-objective optimization to search the Pareto optimal weights of the model and compared the results with other methods. J. Kennedy and R. C. Eberhart brought forward particle swarm optimization (PSO) inspired by the choreography of a bird flock in 1995 [10]. Unlike conventional evolutionary algorithms, PSO possesses the following characteristics: 1) Each individual (or particle) is given a random speed and flows in the decision space; 2) Each individual has its own memory; 3) The evolutionary of each individual is composed of the cooperation and competition among these particles. Since the PSO was proposed, it has been of great concern and become a new research field. PSO has shown a high convergence speed in single objective optimization, and it is also particularly suitable for multi-objective optimization [4], [11].In this article, we use so called “Adaptive Multi-objective Optimization” (AMO) to combine, in which not only an adaptive mutation operator is used to avoid earlier convergence, but also a crowding distance operator is used to improve the distribution of nondominated solutions along the Pareto front and maintain the population diversity[3], and an adaptive exponent inertia weight is used to raise the searching capacity. In the paper, the algorithm is definite as follows: first, initialize the population and algorithm parameters, then execution the optimal cycles. 1) Initialize the position of each particle: pop[i], where i=l, '".AT 5 , NP is the particle number, in the circumstance we use 150; the speed of each particle: vel[i]=0; the record of each particle: pbests[i] =pop[i\\Evaluate each of the particles in the POP: fun[i,j],where j= 1, “‘,NF, and NF is the objective number, in here we use 5. Then store the positions that represent nondominated particles in the repository of the REP according to the Pareto optimality. 2) Before the maximum number of cycles is reached, do update the speed of each particle using function as below. vel[ i] = W- vel[ i] + c, • ranc{ • (pbesL{ i] - pop[ i]) +c 2 ■ rand^ ■ (rep[ h] - pop[ i]) where W is the inertia weight [12]; cl and c2 are the learning factors [13], randl and rand2 are random values in the range [0, 1], the inertia weight of Wmax is 1.2, and Wmin is 0.2; the learning factor of cl is 1, and c2 is 1; the maximum cycle number of Gmax is 1QQ,pbests[i\ is the best position that the particle i has had; h is the index of the maximum crowding distance in the repository that implies the particle locates in the sparse region, as aims to maintain the population diversity; pop[i] is the current position of the particle 7. Update the new positions of the particles adding the speed produced from the previous step pop[i] =pop[i] + vel[i], 3) Maintain the particles within the search space in case they go beyond their boundaries. When a decision variable goes beyond its boundaries, the decision variable takes the value of its corresponding boundary, and its velocity is multiplied by -1. 4) Adaptively mutate each of the particles in the POP at a probability of Pm. Evaluate each of the particles in the POP. Then update the contents in the REP, and insert all the current nondominated positions into the repository. 5) Update the records, when the current position of the particle is better than the position contained in its memory, the particle’s position is updated. pbests[i] = pop[i\ 6) Increase the loop counter of g. In the whole algorithm the sum of the weights at each position of two source images is limited to 1. All approaches are run for a maximum of 100 evaluations. 3. OBJECTIVE QUALITY MEASURES For the RS application, the ideal image is always unknown. Without an ideal or reference image, designing objective metrics that describes what the perfect scheme would produce is a very difficult the limited nur literature for ir image, most c researchers ass However, althc if performed c time consumir continually adj There are a ft availability of < In the articl quality metrics draw out throu Deviation (SD' (CE), mutual ii which utilizes t 3.1 A Standai As we know a fused image estimated by SD = where C(i, j) i: sample mean c SD is compose This measurem 3.2 Entropy (I An index to image. Entropy content of an ir where L is the probability dist 3.3 Cross Entr The source ii is defined as (p CE where CE(A, 1 image A(B) am CEO CE(] 3.4 Mutual Ini A higher va contains fairly Define the join image F as I between source

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