Urban Land Cover Classification Based on WorldView-2 Image Data

Zhiyong Chen School of Math, Physics and Software Engineering

Lanzhou Jiaotong University Gansu, China

e-mail:chenzhiyong1117@126.com

Xiaogang Ning, Jixian Zhang Institue of Photogrammety and Remote Sensing

Chinese Academy of Surverying&Mapping Beijing, China

ningxg@casm.ac.cn

Abstract—Cities hahave a complex construction and easily affected by human activities, so they needsto be surveyed and analyzed timely. WorldView-2 high resolution remote sensing image makes it possible to study the urban land cover classification by its abundant space geometric features and spectral information. This paper would have aimed at given urban land cover types to choose suitable segmentation scales and classification features through object oriented multi-scale segmentation and classification method based on WorldView-2 image data, and extracted the urban land cover types progressively according to reasonable order. Then it raised the NDWI and NDVI which appropriated to extract water and vegetation on WorldView-2 image, and grouped the objects’ features after segmentation to extract the roads and buildings hierarchically. The results of accuracy assessment indicated that using this method to study the urban land cover classification based on WorldView-2 image received an ideal effect.

Keywords-WorldView-2 high resolution remote sensing image; Object-oriented; multi-scale segmentation

I. INTRODUCTION Cities have a complex construction that locates in a

convenient transportation surrounding and covers some acreage multitude and buildings. It is a complex society has intensive economic activity and different ways of life, it is also the epitome of human activities and never stop changing, so it needs timely monitoring and analysis [1][2]. Land cover is a concept that comes into being with remote sensing application. Urban land cover classification monitors and analyzes the situation of city land cover by using technical means. With the development of high resolution remote sensing technology, high resolution remote sensing image (QuickBird, IKONOS, SPOT5, WorldView-2) is used more and more wide because of its characteristic like smoother information performance, rich spatial information and so on [3]. However, the traditional pixel-based classification method can’t make the best use of the relationship between pixel and pixels around it, which makes the classification results become incoherent, caused “Salt & pepper phenomenon”. It can’t distinguish surface features which have different object with the same spectra characteristics, and the classification accuracy is not perfect [4].

Considering the shortage of pixel-based classification technology and visual interpretation classification methods, the object-based classification technology is produced. The object-based classification technology takes various factors into consideration, like spectrum, structure, texture and so on. It greatly improves the classification accuracy of high resolution remote sensing image such as QuickBird, IKONOS, SPOT5, WorldView-2, and gets a better effect. In this method, image analysis and manage focus on object instead of pixel. Object-based classification can not only use spectral information of land types, but also use images’ spatial position, shape characteristic, texture parameter and the relationship between contexts, which effectively avoid the “Salt & pepper phenomenon” and greatly improve the accuracy of classification [5].

In recent years, researches all over the world discuss method to obtain urban land cover information by using object-based classification based on high resolution remote sensing image. They study the land cover current situation and changing problem from different scales and different point of view, which get greatly improve and perfect result [3]. In contrast to per-pixel classification, object-based classification classifies land use by pre-determined field boundaries, with the assumption that each field belongs to a single, homogeneous class [6]. Urban lands cover classification method using multi-scale and multi-variable image segmentation method. The innovation of this method lies in selection of proper scale parameter resulting from proper image data and certain classification order. Normalized Digital Surface Model (nDSM) is constructed in the process of building extraction; a Case Study in Kuala Lumpur City Center, Malaysia, this approach overcomes the difficult problem that is difficult to distinguish road building, which makes the studying about land cover obtaining an ideal classification result [1]. There are two object-oriented land cover classification schemes based on high spatial resolution imagery in Beijing urban areas. Results indicate that optimized classification scheme produces a more enhanced accuracy than the generalized one [3][7]. This paper will extract urban land information from the aspect of features that different land cover types have (spectrum, texture and shape) based on WorldView-2 image data and multi-scale segmentation.

II. STUDY AREA AND DATA ACQUISITION This paper selects 1469*2047 pixel area as the study area

from Zhengzhou’s WorldView-2 high resolution remote sensing image in Dec. 27, 2009 (Figure 1). According to The People's Republic of China national LAND USE CLASSIFICATION, LAND COVER CLASSIFICATION REFERENCE GUIDE and combining the characteristics of Zhengzhou’s land cover, we confirm that the major information this paper needs to provide are vegetation, water, buildings, roads and bare land.

Figure 1. The WorldView-2 image of Zhengzhou city.

WorldView-2 satellite is successful launched by American Digital Global Company in Oct. 9, 2009. It can provide 0.46m panchromatic band and 1.8m multispectral bands, which have eight bands. WorldView-2’s main parameters can be found in Table Ⅰ.

TABLE I. WORLDVIEW-2 SATELLITE MAIN PARAMETERS

Imaging style Push-broom scanning image

Orbit altitude 770km

Sensor panchromatic multispectral

Resolution 0.46m 1.84m

Wavelength 450—800nm

400-450nm Coastal

450-510nm Blue

510-580nm Green

585-625nm Yellow

630-690nm Red

705-745nm RedEdge

770-895nm Near-IR 1

860-1040nm Near-IR 2 Compared with IKONOS and QuickBird image,

WorldView-2’s spatial resolution is higher, there are 8 bands, Coastal, Blue, Green, Yellow, Red, RedEdge, Near-IR 1, Near-IR 2, bit depth is 11 bit, it has enough Radiation resolution to deal with texture feature in high brightness shadow, and spectrum information more detailed.

III. METHODOLOGY According to the character of WorldView-2 data and the

target of classification, this paper divided into four stages of urban land cover classification, image preprocessing, multi-scale segmentation, urban land cover types extraction and accuracy assessment.

A. Image preprocessing Merging the resolution on image of WorldView-2,

merged image not only has the panchromatic high spatial resolution characteristics, but also has the spectral characteristics of the multi-spectral band [8][9]. Analysis the merged image, we can get the categories of urban covered land spectral curve in the eight bands of WorldView-2 image (Figure 2).

Figure 2. Land cover types spectrum curve.

B. Multi-scale segmentation

TABLE II. IMAGE SEGMENTATION PARAMETERS SETTINGS

Land cover types

Segmentation parameters

Scale

Homogeneous standard

Color factor

Shape factor

Shape factor

Smoothness Compactness

Vegetation 25 0.9 0.1 0.5 0.5

Water 200 0.2 0.8 0.8 0.2

Road 120 0.2 0.8 0.9 0.1

Building 100 0.4 0.6 0.2 0.8

Space land 100 0.9 0.1 0.2 0.8

Object-based classification makes homogeneous pixel

form different size objects and classifies those objects. Image segmentation is that identifying and classifying image object by taking advantage of spectral information and spatial information (size, shape, the neighboring pixel of the feature vector pixel, etc.) For this method, successful image segmentation is a necessary prerequisite. The segmentation

results will directly affect the accuracy of the classification results.

In this paper, according to the characteristics of a variety of urban land, setting the different segmentation scale parameter, through repeated experiments for the segmentation parameters (Table Ⅱ), extracting the land cover information by segmented object spectral information, texture information, shape information.

C. Urban land types extraction 1) Vegetation extraction

Under the feature of spectrum curve in figure 2, we can obviously distinguish vegetation and other surface features in some bands. There is strong absorption ability in the red band and strong reflection in the near-infrared band, and we can extract the vegetation by NDVI. The WorldView-2 image data has two near-infrared bands (NIR 1 and NIR 2) in band 7 and band 8. Through experimentations, it draws a conclusion that the value of NDVI calculated by NIR 1 and red band is better than which calculated only by red band, so

The vegetation information is more precise and clear[10]. The formula (1) is:

dRe1NIRdRe1NIR

NDVI+−=

(1)

We classify the divided objects by fuzzy membership function classification, and extract the buildings with blue roof by the vegetation that extracted by NDVI. The analysis finds that there is obviously difference between vegetation and buildings with blue roof in blue band, so we can set a threshold value in blue band to shield the effect from buildings to vegetation [11]. That is to say we get the vegetation information in this area based on NDVI > 0.21 && Blue < 450 (Figure 3).

Figure 3. The image of Vegetation.

1) Water extraction Water is mostly absorbed in near-infrared band, almost

no reflection. Meanwhile, other surface features are mostly reflected in near-infrared band, especially in green band. Based on relevant theories of NDWI and WorldView-2 image data, through some experiments, we know that NIR 2 has the lowest brightness in water and highest brightness in

green band. Since, we choose NDWI to extract the water information [12]. The formula (2) is:

2NIRGreen2NIRGreen

NDWI+−= (2)

We use mask in the water information, and set the threshold value of NDWI in no vegetated area to get the water information in city. The effect of water extracting is good when NDWI > 0.47 (Figure 4).

Figure 4. The image of Water.

2) Road extraction Road is extended and stripped, and it has little change on

surface gray level and texture. The curve of road is limited and do not have dramatic changes, the road width remains steady. Since, it is difficult to extract the road information through the spectral information. We can divide shape features of objects to process the extraction of road. The ratio of length to width is identical to the ratio of the eigenvalues of the covariance matrix, and it can also be approximated using the Minimum Bounding Rectangle. The object feature of Density could get thin and long road, which like a filament [13].

Figure 5. The image of Road.

The formula (3)and (4) are:

WLR = (3)

L: length of the Minimum Bounding Rectangle of objects, W: width of Minimum Bounding Rectangle of objects;

YX VVP

++=

1Density (4)

P : the objects, YX VV + : the diameter value of ellipse nearest to the object.

After masking Vegetation and Water information, we can get the type of road by using fuzzy membership classification approach, there are three conditions must be needed: R > 7.8, W >400, Density < 0.8 (Figure 5).

3) Building extraction The images of city buildings mainly display the

following three kinds of cases: The internal gray level of some buildings’ roofs are equally distributed and different from surrounding background, so they can be completely segmented; some buildings that nearby roads have the similar spectral features with roads and overlay with roads after segmentation, so it is difficult to extract them; some buildings will miss the shape after segmentation because of being blocked by trees or other buildings, and we can't use the same geometrical model to describe its shapes. This paper highlights the target object through calculating the difference of brightness value between segmentation objects and nearby objects (Distance=0). Furthermore, the existence of shadow of high building in city and its obvious edge information highlight the difference of buildings and nearby objects [14]. The formula (5) is:

∑∈

−=VNU

KKUK )U(C)V(CWW1

)V(Δ (5)

U, V: nearby objects, K: band layer, NV: the set of nearest objects, )(C VK , )U(CK : the mean brightness value of V and U in band K, W: the summation of band weight in set NV, WU: the length of common edge of object U and object V.

Through this process we get the urban buildings information (Figure 6).

Figure 6. The image of Building.

4) Bare land extraction Space or bare land has no regular shape and it will

immensely obstruct the extraction of roads and buildings.

Through trial and error, this paper selected band 5(R),band 3(G),band(B) from WorldView-2 image data for HIS color transform, selected Hue (H) for calculation and set the corresponding threshold, so we can extract the bare land information. The formula (6) is:

°−−×°

=360

MINMAXBG

60H

if MAX = R

°

°+−−×°

=360

120MINMAXRB

60H

if MAX = G

°

°+−−×°

=360

240MINMAXGR

60H

if MAX = B (6)

MAX: the maximum of the brightness average value in band R, band G and band B, MIN: the minimum of the brightness average value in band R, band G and band B.

Before the extraction of buildings, we firstly extract the city Bare land information with two conditions: H > 0.8, H < 0.14. Then we can extract the buildings information based on masking features which were extracted with the condition: Mean Diff. to Neighbor > 10. At last, we classify the rest of land features as bare land, such as the gap between the buildings, the school playground (Figure 7).

Figure 7. The image of Bare land.

Figure 8. The image of classification results.

D. Accuracy assessment In order to get the result of classification, the land cover

classes of sample points were also investigated and used as references to compare against the class assignment by the above methods. Sample points in space and on the category even distribution, each sample point accurately belong to the true category. Confusion matrices were then generated.

TABLE III. ACCURACY ASSESSMENT REPORT FOR OBJECT-BASED CLASSIFICATION

Samples Reference

Vegetation Water Road Building Bare land

Vegetation 2203 0 5 0 414

Water 274 6632 3 0 0

Road 0 0 6381 191 373

Building 2 0 249 2249 174

Bare land 142 339 2764 326 13519 Users’

accuracy 84.1% 95.12% 67.55% 81.27% 70.49%

Producers’ accuracy 70.52% 95.97% 91.85% 84.07% 79.10%

Total accuracy 84.3144%

Kappa coefficient 0.7807

IV. CONCLUSION The results of this research show that object-based

classification using WorldView-2 high resolution satellite remote sensing data can be an effective tool for improving efficiency in the classification of urban land cover. In this document, we described the mechanism by which image objects were segmented from images and classified into different land cover classes using WorldView-2 image and through the application of the object-based approach. Accuracy evaluation result shows that this method can get an ideal effect of classification.

There are three innovative points in this research: According to the certain order, we classified every type based on the characteristic of WorldView-2 image data and urban land cover types; Trough the experiment, we get the combination of Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) to fit the extraction of vegetation, they had been proved to have a good effect; Using the image objects features, such as the length-to-width ratio, Density and The Mean Diff. to Neighbors feature, this paper put forward their own classification approach. In addition, there are two shortages in this research: The choice of segmentation scale needs repeated experiments; the classification method and corresponding classification parameter are low in flexibility and portability; it is difficult to solve the influence of the shadow of buildings and crown in classification.

ACKNOWLEDGMENT

This work was supported by Special Fund for Land Resources Research in the Public. This work was supported by Special Fund for Land Resources Research in the PublicInterest (201211050). The author would thank Associate Professor Ning Xiaogang of Chinese Academy of Surveying and Mapping, Professor Zhang Jixian of Chinese Academy of Surverying and Mapping, and Professor Yan Hanwen of Lanzhou Jiaotong University for their invaluable help in the research and providing such a good learning environment and opportunity.

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