KR20160016238A - System and method for quantitative quality assessment of mosaic imagery - Google Patents

Abstract

The present invention relates to a system and a method for quantitative quality assessment of a mosaic image and, more specifically, to a system and a method for quantitative quality assessment of a mosaic image which propose a quantitative quality assessment index for comparing performance of a created mosaic image, and perform comparison and verification with a visual result to suggest an applicability of the proposed quality assessment index. According to an embodiment of the present invention, the system for quantitative quality assessment of a mosaic image comprises an image acquisition unit, a masking image extraction unit, a similarity assessment calculation unit, and an assessment unit.

Description

TECHNICAL FIELD [0001] The present invention relates to a quantitative quality evaluation system and method for a mosaic image,

The present invention relates to a quantitative quality evaluation system and method of a mosaic image, and more particularly, to a quantitative evaluation index capable of comparing the performance of produced mosaic images, And to a quantitative quality evaluation system and method of a mosaic image.

The present invention was derived from research conducted as part of the space core technology development project of the future creation science department and the Korea Research Foundation [assignment number: 1345198516, title: Development of ion effect extraction and correction technique using satellite radar].

The most important technology in image mapping using satellite imagery is a mosaic technique that connects adjacent images together. In the satellite image mosaic technique, it is common that images are taken over a long period of time, since the shooting timing between the left and right adjacent images is not constant. Therefore, it is difficult to connect images having different colors even in the same place. Because the difference in seasons, atmospheres, and climates occurs at the time of shooting, the amount of radiant energy collected by the sensor varies with features (roads, forests, rivers, etc.) that have the same reflectivity, resulting in similar characteristics Ginny is not represented in the same color even in the area.

Therefore, if only simple joining is performed without special color correction between the left and right adjacent images, very different color heterogeneity can be generated based on the joining line, which has a great influence on the quality of the final mosaic image . That is, the most important keyword in the production of a mosaic image is a color correction technique that efficiently minimizes the color difference between adjacent images and obtains a natural image without color distortion between consecutive images.

On the other hand, representative image processing software such as ERDAS, ENVI, PCI, PGsteamer, PixelFactory etc. are developing the latest mosaic module which can minimize the color distortion problem that can occur in the mosaic of the satellite image. Have been put to practical use.

However, in the production of mosaic image using commercial software, different algorithms and processing methods are applied according to the software, resulting in inconsistent performance results. Accordingly, The importance of efficient quality evaluation method for the produced mosaic image for software and algorithm selection is increasing steadily.

However, the quality evaluation of the produced mosaic image depends mostly on the visual inspection, and it is difficult to obtain the objectivity and reliability of the quality evaluation result because it is greatly influenced by subjective thought and visual environment of the individual . Therefore, there is a need for a quantitative evaluation method that can quantify the quality of mosaic images.

On the other hand, as mentioned above, the most important thing in the production of the mosaic image using the satellite image is to minimize the color difference between adjacent images and to produce a natural image without any sense of heterogeneity. One of the important criteria for evaluating the performance of satellite image mosaic images is to measure the similarity of colors between images.

In this regard, some researchers have proposed a quality assessment method that measures the similarity and contrast of the same object based on the seam line. However, this study has the problem that it can not evaluate the color similarity of the entire mosaic image as an evaluation method which is performed only in the adjacent region. Further, the progressed research result shows that the result obtained from some images with limited region and seasonality , It is required to expand the scope of the research using the images having a variety of characteristics, to comprehensively analyze them, and to establish the objective evaluation index more generally applicable.

Korean Patent No. 10-1208621, entitled " Airborne MSS image data processing system and method ", corrects the S-Bend distortion included in the airborne MSS image data, and converts each corrected RAW format data into one image data The first aerial image data is formed by mosaicing the first aerial image data, and the final aerial image data is formed by matching the color of the object to be photographed with the first aerial image data.

Although the prior art has an advantage of improving the reliability and quality of an aerial photographing image by correcting the S-Bend distortion included in the Airborne MSS image data, the prior art has a merit that the aerial photographing image having improved quality using the mosaic technique is simply However, since the quality of the generated image can not be quantitatively evaluated, there is still a problem in that objective and reliability of the quality evaluation result of the generated image can not be secured.

Therefore, a more objective evaluation technique for the quality of the mosaic image is required.

Korean Registered Patent No. 10-1208621 (Registered on November 29, 2012)

SUMMARY OF THE INVENTION It is an object of the present invention to provide a system and method for quantitative quality evaluation of mosaic images.

An object of the present invention is to perform a comprehensive quality evaluation on a mosaic-performed image from a plurality of images.

An object of the present invention is to improve the reliability of a produced mosaic image.

To achieve these and other advantages and in accordance with the purpose of the present invention, as embodied and broadly described herein, a quantitative quality evaluation system for a mosaic image includes an image acquisition unit, a masking image extraction unit, a similarity evaluation index calculation unit, and an evaluation unit.

The image acquisition unit acquires a mosaic image produced using a multi-time satellite image. The masking image extracting unit selects the same characteristic region in each of the neighboring multi-time satellite images constituting the mosaic image, and extracts a masking image for the selected same characteristic region. The similarity evaluation index calculation unit calculates a vector having an average value of the extracted masking images as an element, and calculates a similarity evaluation index using the calculated vector. The evaluation unit quantitatively evaluates the performance of the mosaic image based on the calculated similarity evaluation index.

In addition, the masking image extracting unit may extract the masking image for the same characteristic region from the unjoined portion of the adjacent multi-time satellite images, and the masking image extracting unit may extract vegetation regions, shadow regions , Unpacked road, asphalt, concrete, steel and sea can be selected as the same characteristic region. Also, the image acquiring unit may acquire the mosaic image using the multi-stage mosaic satellite image observed from the KOMPSAT-2 satellite.

Meanwhile, a quantitative quality evaluation method of a mosaic image according to an exemplary embodiment of the present invention includes acquiring a mosaic image produced using a multi-temporal satellite image, Selecting a characteristic region and extracting a masking image for the selected same characteristic region, calculating a vector having an average value of the extracted masking images as an element, and calculating a similarity evaluation index using the calculated vector And quantitatively evaluating the performance of the mosaic image based on the calculated similarity evaluation index.

In addition, the extracting of the masking image may extract the masking image for the same characteristic region from the unjoined portion of the adjacent multi-time-satellite images. In accordance with the purpose of using the mosaic image, , Shadow areas, unpaved roads, asphalt, concrete, steel and sea can be selected as the same characteristic areas. In addition, the step of acquiring the mosaic image may acquire the mosaic image using the multi-view mosaic satellite image observed from the KOMPSAT-2 satellite.

The present invention relates to a quantitative quality evaluation system and method for a mosaic image, and is capable of performing a comprehensive quality evaluation on a mosaiced image.

The present invention has the effect of improving the reliability of the produced mosaic image.

The present invention can be utilized in an organization that requires mosaic of large-capacity satellite data such as a satellite monitoring center, and is applicable to various commercial software, so that it can be used to compare quantitative performance of produced mosaic image.

Since the mosaic technology of the satellite image is an essential tool for analyzing a wide research area, the present invention can be widely applied to an overall utilization field using a satellite image.

The present invention enables a comprehensive quality evaluation of an image in which mosaics are performed with a plurality of images, and it has a great effect of creativity and its utilization value by defining a methodology not previously provided.

The present invention proposes a technical methodology for evaluating the reliability of a secondary product using a high resolution satellite image. This provides a quantitative comparison information on the algorithms used for producing the mosaic image and the result, Can be improved.

The present invention relates to the construction of a geographic information system, a public business field such as a mountainous terrain and a precise change detection of a city center, a natural disaster observation, a location information service using a smart phone, There is an effect that can be utilized throughout.

The present invention can be utilized as a source technology that can be advantageous in comparison with the existing foreign satellite image processing software installed in the satellite image processing software developed in the domestic market. By increasing the demand of the satellite industry in general, it has the effect of expanding the national satellite industry.

The present invention has the effect of increasing the utilization and applicability thereof, in addition to the commercialization of Arirang No. 2/3 / 3A, which is a domestic high resolution optical satellite.

1 is a schematic block diagram of a quantitative quality evaluation system for mosaic images according to an embodiment of the present invention.
2 is a flowchart of a quantitative quality evaluation method of a mosaic image according to an embodiment of the present invention.
3 is a view showing a KOMPSAT-2 satellite image, which is an image data for evaluating the quantitative quality of a mosaic image according to an embodiment of the present invention.
4 is a diagram illustrating a method for selecting a region for evaluating the quality of a mosaic image according to an exemplary embodiment of the present invention.
5 is a diagram illustrating a mosaic image comparison at various conditions according to an embodiment of the present invention.
FIG. 6 is a view showing an image masking a vegetation area in a mosaic image according to an embodiment of the present invention.
FIG. 7 is a view showing masking images for the same characteristic regions selected from the various mosaic images according to an embodiment of the present invention.
8 is a graphical representation of MSI values calculated in four characteristic regions according to an embodiment of the present invention.

The terms and words used in the present specification and claims should not be construed as limited to ordinary or dictionary terms and the inventor may appropriately define the concept of the term in order to best describe its invention It should be construed as meaning and concept consistent with the technical idea of the present invention.

Therefore, the embodiments described in this specification and the configurations shown in the drawings are merely the most preferred embodiments of the present invention and do not represent all the technical ideas of the present invention. Therefore, It is to be understood that equivalents and modifications are possible.

The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are intended to specify that there are stated features, numbers, steps, operations, elements, parts, or combinations thereof, But do not preclude the presence or addition of features, numbers, steps, operations, components, parts, or combinations thereof.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear. In the following description of the embodiments of the present invention, specific values are only examples.

First, prior to the full-scale explanation, the precondition for providing the quantitative quality evaluation system and method of the mosaic image of the present invention will be described.

First, the present invention is an image data for evaluating the quantitative quality of a mosaic image, which can simultaneously observe a mountainous area and an urban area, and is a multi- Time mosaic satellite images can be used. At this time, according to an embodiment of the present invention, a total of seven KOMPSAT-2 satellite images can be used as the multi-stage mosaic satellite image.

At this time, KOMPSAT-2 is a multipurpose practical satellite No. 2 (Arirang Satellite No. 2), which can monitor natural disasters that may occur on the Korean peninsula and analyze the actual use of various resources. Using the data obtained from satellites, Has a characteristic that it can be used as basic data for the balanced development of the Korean Peninsula. In addition, the performance of the electro-optical camera (MSC), which is the most important element of KOMPSAT-2, is distinguishable not only on the ground vehicle but also on the road lane, so that only USA, Russia, France, have.

On the other hand, such satellite images are not used as general mosaic data when the amount of data on the target area is large. However, due to the characteristics of the high resolution satellite image, the acquisition width is small and the acquisition rate of the perfect satellite image without cloud is very low for the same region. Therefore, in the present invention, image data for evaluating the quantitative quality of the mosaic image, We excluded the condition of mosaic image used and used image data of environmental condition which is often seen in reality and business.

Table 1 shows characteristics of a total of seven KOMPSAT-2 satellite images used as a multi-stage mosaic satellite image according to an embodiment of the present invention.

3 is a view showing a KOMPSAT-2 satellite image, which is an image data for evaluating the quantitative quality of a mosaic image according to an embodiment of the present invention.

That is, the image data shown in FIG. 3 represent actual image data corresponding to seven KOMPSAT-2 satellite images shown in Table 1, and even regions having similar characteristics are not represented with the same color.

In the meantime, a procedure for producing a mosaic image used for the quality evaluation will be described below in quantitative evaluation of the quality of the mosaic image.

Currently, there are ERDAS, ENVI, PCI, PGsteamer, PixelFactory, and mosaic algorithms that are different for each software. Table 2 summarizes the mosaic-related functions provided by typical commercial software.

In the present invention, a total of five mosaic images are generated by applying a total of seven KOMPSAT-2 satellite images used as the multi-phase mosaic satellite images to the mosaic algorithm provided as a basic setting in a total of five software shown in Table 2 After selecting the software that showed relatively good performance through visual inspection, the quality evaluation of the mosaic image was performed based on the selected mosaic image.

In the present invention, the mosaic image produced through the software C and E is relatively superior to the mosaic image produced through the software A, B and D, and the mosaic image produced by the two selected software (C, E) The quantitative quality evaluation of the mosaic image was performed.

On the other hand, in the case of mosaic image production using commercial software, the mosaic function provided by general software can be roughly divided into a joint line extraction step and a color adjustment step.

The extraction method of C junction line of software can be selected as Min difference, Min relative difference, Edge matching, Entire area, and generally Min difference method is used. Color adjustment can be divided into normalization and color balance. Normalization is composed of None, Hotspot, Adaptivefilter. Color balance consists of None, Overlap, Histogram matching, Neighborhood.

For E software, the extraction of the bond line proceeds automatically without any special selection. Color adjustment is divided into Adjustment and Equalization. Options include Global Adjustment and No Adjustment. Equalization consists of Local Equalization, Reference image, and No Equalization. For Equalization, you can choose New Equalization or Old Equalization. At this time, the detailed algorithm for each option is not disclosed for security reasons.

In the present invention, for the detailed comparison of the mosaic image quality using the two selected software (C, E), experimental conditions combining the related functions provided by the respective software are constructed. That is, in the case of the bond line extraction step, the C software has a total of four options, and the E software can be configured to proceed automatically without any special selection. At this time, there are four options for the method of extracting the joining line of the C software, but the min difference method is used in the present invention. In the case of the color adjustment step, C and E software can be composed of twelve and eight experimental conditions, respectively, and the details may be summarized in Table 3. That is, Table 3 shows experimental conditions of C and E software.

Hereinafter, a system and method for quantitative quality evaluation of a mosaic image of the present invention will be described in detail based on the precondition described above.

1 is a schematic block diagram of a quantitative quality evaluation system for mosaic images according to an embodiment of the present invention.

1, a quantitative quality evaluation system 100 for a mosaic image according to an exemplary embodiment of the present invention includes an image acquisition unit 110, a masking image extraction unit 120, a similarity evaluation index calculation unit 130, Section 140

Prior to the description, the present invention proposes a similarity evaluation index as an evaluation index for quantitative quality evaluation of a mosaic image, and terms such as MSI (Mosaic Similarity Index), MSI evaluation index, or color evaluation index used in the present invention, Corresponds to the similarity evaluation index.

The image acquisition unit 110 acquires a mosaic image produced using the multi-time satellite image.

The multi-temporal satellite image used in the image acquisition unit 110 is an initial image data for evaluating the quantitative quality of the produced mosaic image. It can observe the mountainous area and the urban area at the same time, , Summer, autumn, winter), which can be a total of seven satellite images observed from KOMPSAT-2 satellites.

That is, the image acquisition unit 110 may acquire the mosaic image using the multi-stage mosaic satellite image observed from the KOMPSAT-2 satellite.

In addition, when acquiring a mosaic image produced using a multi-time satellite image, the image acquisition unit 110 may apply a mosaic algorithm provided by commercial software such as ERDAS, ENVI, PCI, PGsteamer, (I.e., a total of 7 KOMPSAT-2 satellite images according to an embodiment of the present invention), respectively.

The masking image extracting unit 120 selects the same characteristic region in each adjacent multi-time satellite image (i.e., a total of seven KOMPSAT-2 satellite images) constituting the mosaic image acquired by the image acquiring unit 110, And extracts a masking image for the selected characteristic region.

In other words, in the performance evaluation of the mosaic image, one of important criteria is to measure the similarity of color between adjacent images. The masking image extracting unit 120 combines adjacent images in a multi-timing satellite image, Based on the produced mosaic image, a region having the same characteristics is selected in each of the multiple-time satellite images constituting the mosaic image, and a masking image is extracted for the selected same characteristic region. At this time, the masking image extracting unit 120 can select one of the vegetation area, the shadow area, the unpaved road, the asphalt, the concrete, the river and the sea as the same characteristic area according to the purpose of using the mosaic image, The description will be further described with reference to FIG.

4 is a diagram illustrating a method for selecting a region for evaluating the quality of a mosaic image according to an exemplary embodiment of the present invention.

That is, FIG. 4 represents a process for selecting regions having the same characteristics for each image (that is, for each multi-temporal satellite image) for similarity evaluation. The same characteristic regions include vegetation regions, shadow regions, It is possible to select various characteristic areas such as off-road, asphalt, concrete, river, and sea. However, the selection of the characteristic area should be classified according to the purpose of using the mosaic image.

If the user wants to produce the most natural mosaic image by minimizing the color heterogeneity between adjacent images rather than the secondary analysis using the radiation value of the satellite image, The characteristic area can be selected based on the center. For example, in the case of images photographed in spring and autumn, there may be a distinct color difference between the mosaic images, especially if the target area is vegetation. However, if the purpose of the user is to minimize the difference in color and visually connect the most natural way, the target area should be selected as the center where the seasonal influences such as the vegetation area largely act. However, if the user prepares a preliminary pretreatment process for additional analysis such as creation of a theme, it is preferable to select an object having a certain spectral characteristic regardless of the season, such as concrete or asphalt.

In addition, the masking image extracting unit 120 may select the same characteristic region from the unjoined portion between adjacent multi-time-satellite images, and extract the masking image for the selected same characteristic region. That is, the same characteristic region selected from each of the adjacent multi-time satellite images does not include the junction portion between neighboring images (based on the joining line), and does not distinguish overlapping regions from nonoverlapping regions. This is to exclude cases in which the experimental conditions may be different due to the difference in the joining line positions generated for each mosaic algorithm. Thereafter, through the similarity evaluation index calculation unit 130 of the present invention, the value of the similarity index (MSI) proposed by the present invention can be derived for each of the same characteristic regions selected from the multiple time satellite images, If the MSI value for the selected region is smaller than the MSI value for the other region, it means that the color of the characteristic region is relatively more similar among the joint images in the mosaic image. In order to prove this, that is, to prove the performance of the quantitative quality evaluation technique of the mosaic image proposed by the present invention, the present invention has been subjected to various experiments, and the experimental results thereof will be described in detail later.

Meanwhile, the similarity evaluation index calculating unit 130 calculates a vector having an average value of the masking images extracted by the masking image extracting unit 120 as an element, calculates a similarity index (MSI) using the calculated vector, ).

Before describing this, one of the important criteria for evaluating the performance of a mosaic image proposed by the present invention is to measure the similarity of colors between adjacent images. In the present invention, regions having the same characteristics are extracted for each adjacent image (that is, for each adjacent multi-time satellite image) using the characteristic that the regions having the same characteristics should have similar colors in the similarity evaluation, We compared the difference of the performance by expressing the difference of the average value of each region extracted by image by angle. In the present invention, the similarity evaluation method is referred to as a Mosaic Similarity Index (MSI), the MSI is defined as a similarity evaluation index, and the MSI can be defined as Equation (1).

,

,

,

이다.At this time,

,

,

,

to be.

는 각 영상별, 밴드별로 동일한 특성을 지닌 지역들을 마스킹한 영상들의 평균값을 원소로 갖는 벡터를 나타내며,

의 성분은 의 성분 개수와 동일하고, 각각의 성분은 모두 1로 구성되어 있는 벡터를 나타낸다. 그리고 평가하고자 하는 모자이크 영상이 총 j개의 단일 영상으로 접합된 것이라면, 벡터

와

의 성분 개수는 각각 j개로 구성된다.More specifically,

Represents a vector having an average value of images masked with regions having the same characteristics for each video and band as elements,

≪ / RTI & And each of the components represents a vector composed of 1's. Then, if the mosaic image to be evaluated is connected to a total of j number of single images,

Wow

The number of components of each component is j.

의 성분(m_1i, m_2i, m_3i, m_4i,…, m_ji)은 모자이크 영상 내에서 j개의 단일 영상의 공간적 위치에 속한 지역에서 마스킹한 영상들의 평균값을 나타낸다. 예를 들어, 도면 4의 j번째 영상(Scene j)의 경우, 식생지역(vegetation region)으로 마스킹 된 부분의 영상은 두 장이며, 두 장의 마스킹 영상의 i번째 밴드의 평균값이

의 j번째 성분 값 m_ji이 된다.Also,

(M _1i , m _2i , m _3i , m _{4i, ...,} m _ji ) represent the average value of the images masked in the region belonging to the spatial position of j single images in the mosaic image. For example, in the case of the jth image (Scene j) in FIG. 4, the image of the portion masked by the vegetation region is two, and the average value of the i-th band of the two masking images is

It is the j-th component of the value m _ji.

는 두 벡터 간 내적을 의미하고,

는 두 벡터 크기의 곱을 의미한다. 그리고 밴드별로 계산된 arccos 값을 평균하여, 최종적인 MSI 값을 계산할 수 있다. 만약, MSI가 0에 가깝다면, 이는 모자이크 영상 내에서 인접한 영상 간의 색상왜곡이 거의 없음을 의미한다.N _B represents the number of bands of the mosaic image, N _i represents the i-th band of the mosaic image,

Denotes the inner product between two vectors,

Means the product of the two vector magnitudes. Then, the final MSI value can be calculated by averaging arccos values calculated for each band. If the MSI is close to 0, this means that there is little color distortion between adjacent images in the mosaic image.

)를 계산하고, 상기 계산된 벡터를 기반으로 정의된 수학식 1의 유사성 평가지수(MSI; Mosaic Similarity Index)를 산출할 수 있다.Accordingly, the similarity evaluation index calculator 130 may calculate a similarity score using a vector having an average value of the masking images extracted by the masking image extracting unit 120 as an element

), And calculate a similarity index (MSI) of Equation (1) defined on the basis of the calculated vector.

The evaluation unit 140 quantitatively evaluates the performance of the mosaic image based on the similarity evaluation index calculated by the similarity evaluation index calculation unit 130. [ That is, as mentioned above, if the value of the similarity evaluation index (MSI) is close to 0, it means that there is little color distortion between adjacent images in the mosaic image. Therefore, the quality of the mosaic image is evaluated as excellent .

Hereinafter, various experimental examples and results of analysis will be described to prove that the quantitative quality evaluation technique of the mosaic image suggested by the present invention is suitable.

Prior to the full-fledged experiment, we selected 12 experimental conditions in C and 8 in E, by combining the selection items of the color correction method provided by software C and E. A total of 20 mosaic images were produced according to the experimental conditions and the images were compared and evaluated visually and quantitatively.

In the following, only the detailed experimental results of the four mosaic images among the experimental conditions showing the relatively good performance in the two software among the experimental conditions of the two software will be described.

5 is a diagram illustrating a mosaic image comparison at various conditions according to an embodiment of the present invention.

Referring to FIG. 5, FIG. 5 (a) shows a reference image obtained by joining a total of 7 original images without specific color correction, for comparison with a resultant image at a predetermined experimental condition. At this time, the total of 7 original images means seven KOMPSAT-2 satellite images shown in Table 1 and FIG. 5 (b) and 5 (c) show that adaptive filters are commonly applied among the normalization techniques provided by the C software, the overlay is selected by the color balance option, and FIG. 5 (c) Is the result of selecting a histogram. 5 (d) and 5 (e) apply the global adjustment of the software E in common, FIG. 5 (d) apply the old equalization by the local statistics, and FIG. 5 The result is applied. As shown in FIG. 5, a mosaic image produced using seven original images can be acquired through the image acquisition unit 110 of the present invention.

Next, the detailed performance evaluation of each mosaic result was conducted through a visual and quantitative method. In order to evaluate the similarity of color between regions having the same characteristics in the mosaic image, four characteristic regions, which are classified into vegetation region, river basin, unpaved road and shadow region, were selected and visual similarity color similarity . Also, to quantify the visual results quantitatively, the masking image for each characteristic region is extracted and the MSI evaluation index is calculated based on the extracted masking image.

FIG. 6 is a view showing an image masking a vegetation area in a mosaic image according to an embodiment of the present invention.

That is, referring to FIG. 6, the masking image extracting unit 120 according to an embodiment of the present invention expresses masked positions for each junction image for comparison of vegetation regions among the four characteristic regions. In the embodiment of FIG. 6, in the case of the vegetation area, it is found that 2-4 regions are selected for each joint image.

FIG. 7 is a view showing masking images for the same characteristic regions selected from the various mosaic images according to an embodiment of the present invention.

More specifically, Fig. 7 shows an enlarged characteristic region indicated by 1, 2, and 3 in Fig. 6 in the mosaic image (the image relating to Figs. 5 (a) to 5 (e) As indicated, the portion marked in red indicates the area actually masked for quantitative evaluation.

(B-1, b-2, b-3) and (c-1, c-2) (d-1, d-2, d-3) and (e-1, e-2, e-3) represent the results extracted from the mosaic image produced by AdaptFilt Overlap and AdaptFilt Histogram of C software. -3) shows the results in Global Old Local and Global New Local of E software.

(A-1, a-2, a-3) are images without color correction between the joint images. A very large color difference is confirmed between the images, and the result of (b- 1, b-2, b-3) and (c-1, c-2, c-3) are relatively darker in color and higher in saturation than other images. The results (d-1, d-2, d-3) and (e-1, e-2, e-3) (C-1, c-2, c-3) are expressed in a darker color than in (b-1, b-2, b-3) (D-1, d-2, d-3) and (e-1, e-2, e-3) produced by software E were not different from each other. Almost identical results were obtained.

Therefore, although the color between each characteristic region did not show a large difference between the respective mosaic images, it was judged that the overall color tone of the image produced by the software E was relatively smoother and similar.

Table 4 shows the average of the masked images extracted from each adjacent image and the result of the similarity evaluation index (MSI) calculated using the same. The result of the similarity evaluation index (MSI) may be calculated through the similarity evaluation index calculating unit 130 of the present invention.

Table 4 shows the results of the quality assessment performed in the vegetation area. As shown in Table 4, the MSI evaluation indexes for the reference image and the mosaic image produced by the software C and E are 14.89, 4.61, 3.66, 2.42, 2.15, the mosaic image produced by software E was relatively low compared to the mosaic image produced by software C, and the result was 6 times lower than the reference image without color correction.

In the case of the software C, the AdaptFilt Histogram shows an MSI index that is somewhat lower than the AdaptFilt Overlap. This is because the images (c-1, c-2, c-3) -3) is considered to be related to a more similar representation of the tone of the overall color. In addition, the mosaic images produced by software E showed almost the same value, 0.3, which shows the same result as the visual evaluation.

In the following, a visual / quantitative evaluation of river basins, off-road roads and shadows was carried out in the same way, and the result of the evaluation for the relevant area is briefly summarized only for the quantitative evaluation results.

Table 5 shows the results of the quality assessment conducted in the river basin.

In other words, Table 5 shows the results of the MSI performed in the river basin, and the MSI evaluation indices in the respective mosaic images were 17.42, 13.80, 8.41, 6.38, and 6.14, respectively. In the case of the image with color correction, the numerical values calculated for the blue band were relatively low as 6.83, 5.19, 4.21 and 4.11, and in the red band, 24.00, 12.68, 9.00 and 8.64, respectively, Respectively. In addition, the MSI value of the AdaptFilt Overlap in C software was almost two times higher than that of the E software.

Table 6 shows the results of the quality evaluation performed on unpaved roads.

In other words, Table 6 shows the results of MSI calculated on the unpacked road, and the MSI evaluation indices in the respective mosaic images were 11.33, 9.48, 8.06, 4.60, and 4.57. The MSI evaluation index on unpackaged roads showed the highest value in the AdaptFilt Overlap of software C, similar to the results in Table 5, and the results of software E were almost similar and relatively lower than those of the other methods.

Table 7 shows the quality evaluation results performed in shadow areas.

In other words, Table 7 shows the MSI results calculated in the shaded area, and the MSI evaluation indices in each mosaic image were 22.80, 8.00, 2.21, 2.66, 2.98. The results of the AdaptFilt Overlap of Software C were three times higher than those of other methods, and this tendency was remarkable in river basins, off-road roads and shadows except vegetation areas.

FIG. 8 is a diagram illustrating MSI values calculated in four characteristic regions according to an exemplary embodiment of the present invention. Referring to FIG.

That is, FIG. 8 schematically shows MSI values calculated in four selected characteristic areas (vegetation area, river basin, unpacked road, shadow area) for color similarity evaluation of produced mosaic image. In the case of the reference image, since the original images are subjected to simple joining only without performing separate color correction processing, the color heterogeneity of the neighboring images is the largest, and the color evaluation index (i.e., similarity evaluation index (MSI) ), It was expressed as a relatively large value. And the difference between each software was the lowest in the vegetation area and showed the greatest difference in the river basin. In addition, the mosaic image produced by software E has the lowest and almost the same values in most of the characteristic regions, so that the color correction ability between the same characteristic regions is relatively good, which is the same as that of the naked eye reading . The evaluation unit 140 of the present invention can quantitatively evaluate the performance of the produced mosaic image based on the calculated similarity evaluation index (MSI).

In conclusion, the object of the present invention is to propose a quality evaluation technique for the produced mosaic image using the image mosaic technique supported by the latest commercial software. To do this, we applied 7 KOMPSAT-2 satellite images, which clearly reflect seasonal differences, to representative image processing software, and produced mosaic images. In addition, we propose a quantitative evaluation index (MSI) that can compare the objective performance of the produced mosaic images and compare the results with the visual analysis results.

To do this, mosaic images were produced by applying the mosaic modules provided by five commercial software, and software with relatively high performance was selected by visual inspection and analyzed in detail. In addition, the color evaluation index (MSI) proposed in the present invention is applied to the four images produced through the two software programs C and E and the reference image to which no special color correction method is applied. As a result, the MSI value calculated from the reference image showed the highest value for all the characteristic regions, and the mosaic image produced by the software E showed a relatively lower value than the other images. This is consistent with the result of the visual evaluation, which is judged to demonstrate the possibility of utilizing the performance evaluation method proposed in the present invention.

Hereinafter, a method for evaluating quantitative quality of a mosaic image according to an embodiment of the present invention will be briefly described based on the details described above.

2 is a flowchart of a quantitative quality evaluation method of a mosaic image according to an embodiment of the present invention.

Referring to FIG. 2, in a quantitative quality evaluation method of a mosaic image, a mosaic image produced using a multi-time satellite image is acquired in a preferred image acquisition unit 110 (S210).

In this case, the multi-temporal satellite image acquired by the image acquisition unit 110 is an initial image data for evaluating the quantitative quality of the produced mosaic image, and it is possible to simultaneously observe the mountainous area and the urban area, It can be a multi-temporal satellite image that reflects the clear characteristics of the KOMPSAT-2 satellite (summer, autumn, winter), which can be a total of seven satellite images observed from KOMPSAT-2 satellites.

That is, the image acquisition unit 110 may acquire the mosaic image using the multi-stage mosaic satellite image observed from the KOMPSAT-2 satellite.

In addition, when acquiring a mosaic image produced using a multi-time satellite image, the image acquisition unit 110 may apply a mosaic algorithm provided by commercial software such as ERDAS, ENVI, PCI, PGsteamer, (I.e., a total of 7 KOMPSAT-2 satellite images according to an embodiment of the present invention), respectively.

Next, the masking image extracting unit 120 selects the same characteristic region in each of the neighboring multi-time-satellite images constituting the mosaic image acquired in Step S210, and extracts a masking image for the selected same characteristic region (S220).

In other words, in the performance evaluation of the mosaic image, one of important criteria is to measure the similarity of color between adjacent images. The masking image extracting unit 120 combines adjacent images in a multi-timing satellite image, Based on the produced mosaic image, a region having the same characteristics is selected in each of the multiple-time satellite images constituting the mosaic image, and a masking image is extracted for the selected same characteristic region. At this time, the masking image extracting unit 120 can select one of the vegetation area, the shadow area, the unpaved road, the asphalt, the concrete, the river and the sea as the same characteristic area according to the purpose of using the mosaic image, Since the description has been described in detail with reference to FIG. 4, reference is made to this.

Next, the similarity evaluation index calculator 130 calculates a vector having an average value of the masking images extracted in step S220 as an element, and calculates a similarity index (MSI) using the calculated vector (S230).

Before describing this, one of the important criteria for evaluating the performance of a mosaic image proposed by the present invention is to measure the similarity of colors between adjacent images. In the present invention, regions having the same characteristics are extracted for each adjacent image (that is, for each adjacent multi-time satellite image) using the characteristic that the regions having the same characteristics should have similar colors in the similarity evaluation, We compared the difference of the performance by expressing the difference of the average value of each region extracted by image by angle. In the present invention, the similarity evaluation method is referred to as a Mosaic Similarity Index (MSI), and the MSI is defined as a similarity evaluation index. The MSI can be defined as Equation 1 described above. This is described in detail above.

Next, the evaluating unit 140 quantitatively evaluates the performance of the mosaic image based on the similarity evaluation index calculated in step S230 (S240).

That is, the evaluation unit 140 can quantitatively evaluate the performance of the mosaic image based on the similarity evaluation index calculated by the similarity evaluation index calculation unit 130. [ If the value of the similarity index (MSI) is close to zero, it means that there is little color distortion between adjacent images in the mosaic image. Therefore, the quality of the mosaic image can be evaluated as excellent.

In order to demonstrate the feasibility of the quality evaluation method of the mosaic image proposed by the present invention, a variety of experimental examples and analysis results are described, thereby demonstrating the suitability of the technology proposed by the present invention Respectively.

Accordingly, the system and method for quantitative quality assessment of mosaic images according to one embodiment of the present invention can improve the reliability of the produced mosaic image and can be utilized in an institution requiring mosaic of large-capacity satellite data such as a satellite monitoring center , It can be applied to various commercial software and can be used to compare the quantitative performance of the produced mosaic image.

In addition, since the mosaic technology of the satellite image is an essential tool for analyzing a wide research area, the present invention can be widely applied to an overall utilization field using a satellite image as a material. The present invention can be applied to a mosaic- The integrated quality evaluation can be done and the methodology which is not presented before is defined, so that originality and its utilization value are very effective.

In addition, the present invention can be applied to public business fields such as geographic information system construction, mountainous terrain and precise change detection in urban areas, natural disaster observation, and private information services such as location information service using smart phone, It can be used as a source technology that can be used for the whole sector and can be used in satellite image processing software developed in Korea and can gain an advantage in comparative competition with existing foreign satellite image processing software, As the utilization industry increases, the demand of the satellite industry as a whole is increased, which has the effect of expanding the national satellite industry.

The quantitative quality evaluation method of the mosaic image according to an exemplary embodiment of the present invention may be implemented in the form of a program command that can be performed through various computer means and recorded in a computer readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

As described above, the present invention has been described with reference to particular embodiments, such as specific elements, and specific embodiments and drawings. However, it should be understood that the present invention is not limited to the above- And various modifications and changes may be made thereto by those skilled in the art to which the present invention pertains.

Accordingly, the spirit of the present invention should not be construed as being limited to the embodiments described, and all of the equivalents or equivalents of the claims, as well as the following claims, belong to the scope of the present invention .

100: Quantitative Quality Evaluation System of Mosaic Images
110: image acquiring unit 120: masking image extracting unit
130: similarity evaluation index calculating section 140:

Claims (9)

Acquiring a mosaic image produced using a multi-time satellite image;
Selecting the same characteristic region in each of the neighboring multi-time-satellite images constituting the mosaic image and extracting a masking image for the selected same characteristic region;
Calculating a vector having an average value of the extracted masking images as an element, and calculating a similarity evaluation index using the calculated vector; And
Quantitatively evaluating the performance of the mosaic image based on the calculated similarity evaluation index;
A method for quantitatively evaluating the quality of a mosaic image. The method according to claim 1,
The step of extracting the masking image
And extracting the masking image for the same characteristic region from the non-joined portion between adjacent multi-time-satellite images
Quantitative Quality Assessment Method of Mosaic Images. The method according to claim 1,
The step of extracting the masking image
According to the purpose of using the mosaic image, one of the vegetation area, the shadow area, the unpaved road, the asphalt, the concrete, the river and the sea is selected as the same characteristic area
Quantitative Quality Assessment Method of Mosaic Images. The method according to claim 1,
The step of acquiring the mosaic image
And acquires the mosaic image using the multi-stage mosaic satellite image observed from the KOMPSAT-2 satellite
Quantitative Quality Assessment Method of Mosaic Images. A computer-readable recording medium having recorded therein a program for executing the method according to any one of claims 1 to 4. An image acquisition unit for acquiring a mosaic image produced using a multi-time satellite image;
A masking image extracting unit for selecting the same characteristic region in each of the adjacent multi-time-satellite images constituting the mosaic image and extracting a masking image for the selected same characteristic region;
A similarity evaluation index calculation unit for calculating a vector having an average value of the extracted masking images as an element and calculating a similarity evaluation index using the calculated vector; And
An evaluation unit for quantitatively evaluating the performance of the mosaic image based on the calculated similarity evaluation index;
A quantitative quality evaluation system for mosaic images. The method according to claim 6,
The masking image extracting unit
And extracting the masking image for the same characteristic region from the non-joined portion between adjacent multi-time-satellite images
Quantitative Quality Assessment System of Mosaic Images. The method according to claim 6,
The masking image extracting unit
According to the purpose of using the mosaic image, one of the vegetation area, the shadow area, the unpaved road, the asphalt, the concrete, the river and the sea is selected as the same characteristic area
Quantitative Quality Assessment System of Mosaic Images. The method according to claim 6,
The image acquiring unit
And acquires the mosaic image using the multi-stage mosaic satellite image observed from the KOMPSAT-2 satellite
Quantitative Quality Assessment System of Mosaic Images.

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