Application and Evaluation of Object-oriented Technology in High-resolution Remote Sensing Image Classification

Chuan Zhang, Yingjun Zhao, Donghui Zhang and Ningbo Zhao

National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology Beijing Research Institute of Uranium Geology

Beijing, China zhang1232123@tom.com

Abstract—Object-oriented classification of remote sensing image is that the image is divided into a series of image objects based on objects’ spectrum, shape and texture features, adopting the technology of fuzzy classification to achieve classification and information extraction. In this paper, SPOT5 image of a hypaethral mine as an study area, object-oriented classification was used to study the mine information extraction and classification. After completing multi-scale image segmentation and establishment of classification rules, object-oriented classification of experimental area image was accomplished. The results show that the classification accuracy based on object-oriented technology of image classification is higher, by compared with the results based on traditional technology of image classification. The study in this paper demonstrates the superiority of object-oriented technology of image classification and it’s particularly suitable for information extraction of high-resolution remote sensing data.

Keywords-object-oriented; high-resolution; remote sensing image; classification

I. INTRODUCTION It is difficult to extract useful information efficiently

from the high-resolution remote sensing image in the field of image processing. Compared with the low-resolution remote sensing image, the characteristics of high-resolution remote sensing image are more clear target outline, more complex texture and more rich spatial detail information. Therefore, the information obtained by traditional based-pixel image processing is limited and this process is inefficient. For this choke point of high-resolution remote sensing image processing, scholars have proposed technology of object-oriented remote sensing information extration(Baaz M,1999). This technology has achieved good results in application of some areas (Hofmann,2001;Bauer,2001;Sande,2001;Willhauk,2002) . Image object as the unit of analysis, this technology provides a new way of thinking for the remote sensing image classification, taking advantage of geometry, texture, and other features such as neighboring relations. In this paper, the basic idea of object-oriented technology is elaborated, and object-oriented classification is carried out in a mine area. Reliability and superiority of object-oriented classification in high-resolution remote sensing image classification are demonstrated by comparison of the results between object-oriented classification and traditional classification.

II. PRINCIPLES OF OBJECT-ORIENTED CLASSIFICATION The principle of object-oriented classification of

remote sensing image is that the image is divided into a series of image objects based on objects’spectrum, the shape and texture characteristics, adopting fuzzy classification to achieve classification information extraction. Therefore, the key technology of object-oriented classification are multi-scale image segmentation and fuzzy classification model.

A. Multi-scale Image Segmentation Image segmentation is that the whole image is divided

into a number of non-empty region not overlaping each other based on pixel color and shape. Multi-scale image segmentation means that different scales can be used in the process of segmentation to generate the objects and sizes of the objects depend on the selected scales before segmentation(Baatz M,2000).

The spectral factor and the shape factor which includes smoothness heterogeneity and tightness heterogeneity are two factors which affect the size of heterogeneity and they need to be confirmed before segmentation. Only ensure that spectral heterogeneity and smoothness heterogeneity and tightness heterogeneity are least simultaneously, average heterogeneity of all objects in the image is minimum.

Spectral heterogeneity is obtained by calculating the sum of the standard deviation of spectrum value from various data layers based on specific weight. The formula is color c c

ch w σ= ⋅∑ , where cw is the weigh of layer C

and cσ is the standard deviation of spectrum value from layer C. The formula of tightness heterogeneity is

/tightnessh l n= and smoothness heterogeneity is

/smoothh l b= , where l is side of target polygon and n is the number of pixels in the polygon and b is the shortest side of enclosing rectangle of the polygon.

General steps of multi-scale image segmentation is: first, setting parameters of segmentation including the weight of each layer, scale threshold to determine whether to split or merge and the weights of the spectral factor and the shape factor. Then, any pixel in the image as the smallest polygon, heterogeneities are computed in the first segmentation. After the first segmentation, heterogeneities are computed in the second segmentation based on generated polygon objects above. In the course of each

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segmentation, differences between the heterogeneity and set threshold are estimated. If the heterogeneity is less than the threshold, the next segmentation would continues, or stopping. Finally, a fixed-scale object layer is obtained, flowchart of multi-scale image segmentation shown in Figure 1.

Figure 1. Flowchart of multi-scale image segmentation

B. Fuzzy Classification Model Based on Membership Degree Function Traditional classifier, such as minimum distance

method, which gives attribute value of pixel class 0 or 1 expressing whether the pixel belongs to certain class. Howover, the fuzzy classification method adope the possibility to describe the extent of membership degree of the object class. Membership degree value is between 0 and 1 at the series of values, where 1 means fully belong to the class and 0 means completely not belong to the class. It mathematically expressed as a fuzzy set where supposing domain as U and the range of U is [0,1].

[ ]: 0,1A Uμ → , ( )Au uμ→ .

A is a fuzzy set of U, and Aμ is called the fuzzy

membership function of A, and ( )A uμ is called the membership degree of u to A. The theory of fuzzy set means that a value between 0 and 1 called membership degree is used to represent the extent of certain feature belonging to certain set. Membership function can mapped an element to an appropriate membership degree.

Membership degree function is the key of fuzzy classification model. Membership degree function can be any form of curve, taking which form determined by the specific purpose of classification. Determining membership degree function should be objective in nature, but allowing subjective skills with some flexibility. In the process of application, the membership degree function can be gradually revised and improved. The types of familiar membership degree functions are Gaussian (parabola), triangular (double line), ladder (Tri) and Don't Care-type (a horizontal line).

III. APPLICATION OF OBJECT-ORIENTED CLASSIFICATION IN MINE INFORMATION

EXTRACTION

A. Image Processing before Classification The experimental area is a hypaethral iron ore mining

area, and the area is about 600 hectares. There is a large limestone mining area which is exploiting in the region, and the area is about 200 hectares.

The remote sensing data of experimental area is SPOT5 data, including the 10-meter panchromatic and 2.5 m multi-spectral data. First, a series of processing are implemented, including Orthorectification, registration, cutting, etc., and then PANSHARP fusion method is adoped for image fusion between multi-spectral and panchromatic data. Figure 2 is the comparison between panchromatic image before fusion and color composite image after fusion.

Figure 2. Comparison between before and after fusion

B. Image Segmentation of Experimental Area Image segmentation needs according to the specific

categories of classification. For the experimental area, there are seven kinds of mining feature type: surface mining, mining field, transit sites, dump, tailings, mine construction, and closure mine. Moreover, there are some kinds of basic feature type: vegetation, roads, water, urban and unclassified. The total is 12 categories.

Currently, the choice of best parameter in remote sensing image segmentation is an important research topic. In this paper, area Ratio mean method is used to determine the best segmentation scale for each category. Finally, three segmentation scales 149, 75, 38 are determined by using of area ratio mean method.

TABLE I. LAYER CATEGORIES AND SEGMENTATION PARAMETERS

Layers Scale Weight of Spectral Weight of Shape

color Shape smoothness tightness First 38 0.6 0.4 0.4 0.6

Second 75 0.7 0.3 0.3 0.7 Third 149 0.8 0.2 0.2 0.8 First Small Area Water, Roads, Mine Water

Second Surface Mining, Mining Field, Transit Sites, Dump, Tailings, Closure Mine, Mine Construction, Vegetation, Water, Urban, Unclassified

Third Large Area Vegetation, Water, Surface Mining, Stope, Transit Sites, Closure Mine

Three 3 different object layers are generated by multi-scale segmentation corresponding to three different scales. The first layer is small-scale object, and the generation is based on pixel. The second layer is generated on the basis of combined objects in the first layer. The third layer is generated on combined objects in the second layer.

Because NDVI is conducive to vegetation analysis, and DEM benefit analysis related to the elevation mountain, and the image of sharpening features enhanced the edge features, the NDVI of fusion image and DEM of experimental area and panchromatic image after Robert sharpening together with the original four-band images loaded with a total of seven layers are analyzed, supposing the weigh of each layer as 1. The layer categories and segmentation parameters of multi-scale segmentation shown in TABLEⅠ.

C. Feature Selection of Object and Achievement of Classification Three different scale object layers are obtained after

image segmentation. The establishment of classification rules is achieved by selecting respectively typical features of various types in object layers. The selected features include mean, standard deviation, texture, density, aspect ratio, area, length and so on.

Figure 3. Flowchart of object-oriented classification

Vegetation is the best characterized by vegetation index, in addition, the reflection of vegetation is strong in the Green band. Therefore, mean of NDVI and mean of green band are chosed as two features. Water reflectance in the near infrared band is very small. In order to distinguish between water and shadow, the standard deviation of the red band can be considered, in addition, there are usually vegetation in shadow areas, so mean of NDVI, mean of NIR band and the Standard deviation of red band are

chosed as three features. The road is linear and its image brightness is high, so two features are chosed, aspect ratio and mean of red band. Texture feature is chosed for other classes.

After features selection of various of objects and establishment of the classification rules, object-oriented classification can be implemented according to the rule function. The process of object-oriented classification for experimental area image shown in Figure 3.

IV. COMPARISON OF THE RESULTS BETWEEN OBJECT-ORIENTED AND TRADITIONAL

CLASSIFICATION In order to evaluate the effect of object-oriented

classification, traditional supervised classification method, maximum likelihood classification is used to classify the experimental area image. Figure 4 is the comparison of the results between two classification methods in experimental area.

(a) (b)

Figure 4. The results of two methods of classification. (a) is based on object-oriented classification; (b) is based on maximum likelihood

classification

Analyzing the results from the visual, the results based on object-oriented classification are obviously better than the results based on maximum likelihood classification. Field interpretation map as the benchmark of evaluation, confusion matrix and Kappa coefficient are used to evaluate the accuracy of the results of two classification. The comparison of Kappa coefficient between two classifications is shown in TABLEⅡ, and the comparison of accuracy between two classifications is shown TABLEⅢ.

TABLE II. THE COMPARISON OF KAPPA COEFFICIENT BETWEEN TWO CLASSIFICATIONS

Class Kappa coefficient

object-oriented classification

maximum likelihood classification

Roads 0.768 0.094

Tailings 0.815 0.551

Dump 0.973 0.889

Mining Field 0.765 0.440

Surface Mining 0.805 0.465

Transit Sites 0.673 0.524

Mine Construction 0.462 0.315

Closure Mine 0.768 0.407

Total Kappa coefficient 0.8643 0.5393

TABLE III. THE COMPARISON OF ACCURACY BETWEEN TWO CLASSIFICATIONS

Class Object-oriented classification

Maximum likelihood classification

Producer accuracy

User accuracy

Producer accuracy

User accuracy

Roads 69.97% 76.87% 43.01% 10.09%

Tailings 91.17% 82.83% 75.56% 59.42%

Dump 86.80% 98.44% 64.26% 95.39%

Mining Field 68.64% 77.56% 64.82% 47.36%

Surface Mining 89.14% 81.93% 65.16% 51.89%

Transit Sites 67.02% 69.09% 70.26% 56.05%

Mine Construction 86.70% 47.31% 71.43% 33.45%

Closure Mine 98.94% 71.35% 77.08% 43.41%

Total accuracy 89.6953% 66.5342%

From the results shown in TABLEⅡand Ⅲ, the total classification accuracy of object-oriented classification is 23% higher than the maximum likelihood classification. Simultaneously, the total Kappa coefficient of object-oriented classification is 0.32 higher than the maximum likelihood classification. Obviously, the results of object-oriented classification are superior.

V. CONCLUSIONS Object-oriented classification has three important

characteristics. First, object layers of different scales are obtained by means of image segmentation based on different scales, and features are extracted in the object layer of the most appropriate scale; Second, a variety of typical features of the object are chosed to extract object information; Third, membership degree function is used to establish the model of classification. The advantages of object-oriented classification relative to traditional pixel-based classification are that overcoming the disadvantage of traditional pixel-based classification which only utilizes spectral features and no longer processing on the same scale for all classes. Therefore, the technology of object-oriented classification can fully make use of various image information to make the results of classification more

reasonable, especially suitable for high-resolution remote sensing image.

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