IEG 환경지질연구정보센터

A STUDY OF IMPROVEMENT FOR ROAD EXTRACTION USING SPOT – 5 IMAGERY

Dinh-Tai Nguyen

, Sung-Hoo Hong

, and Choen Kim

1

Dept. of Applied Information Technology, Kookmin University, Seoul 136-702, Korea

2

Dept. of Forest Resources, Kookmin University

1

E-mail: [email protected],

Tel.: +82-2-910-5080

ABSTRACT: Road information is essential for cartography, traffic management, and planning of urban and industrial areas. In the past, many approaches have been considered, however the existing road extraction methods still need much post editing. The objective of this paper is to improve road extraction based on Spectral Angle Mapper (SAM) classification by using SPOT-5 imagery. SAM directly compares the target spectrum roads with each pixel’s spectrum to determine the similarity of their shape. This paper compared and proved accuracy of extracted road from enhanced imagery by SAM method is more detail than extracted road from merged multi imagery.

KEYWORDS: Road extraction, Spectral angle mapper I. INTRODUCTION

Recently, many high resolution images have been collected from new satellite generation, so the establishing of more detail mapping satisfy the development of economy and social is important now days. Before, the extraction of small object is difficult by using low resolution imagery as Landsat have resolution 30 m but at present, that work is more much easy by using high resolution imagery as SPOT-5 have resolution 2,5 m. In the past, a lot of methods have been proposed for road feature extraction from high resolution imagery. Koutaki and Uchimura (2004) proposed automatic road extraction based on cross detection in suburb. Based on the characteristic and information of the road intersections, shape model for intersection has been created and classified the intersection to three types:

the crossroad represent the intersection of two road portions, the T-intersection consist of one straight road and connected branch and the Three-forked road has three road segment, each branch has different direction.

The aim of this paper is to study a method to improve the accuracy of road extraction from high- resolution satellite imagery. A Spot-5 satellite panchromatic image with the resolution 2.5m and a Spot-5 satellite multispectral image with the resolution 10m have used in this study.

II. METHODOLOGY

In this paper, the improvement method for the

extraction of the road using Spot-5 imagery is

proposed. In which road objects have been enhanced

and also extracted roads are converted into road vector. The processing steps are shown in Figure 1.

Figure1. The processing steps for road extraction

Fusion

To enhance the spatial resolution of multi-spectral image Spot-5, a higher spatial resolution (2.5m) panchromatic image Spot-5 is merged with a plurality of lower spatial resolution (10m) spectral band images Spot-5 by using Gram- Schmidt spectral Sharpening tool in ENVI version 4.4 software. The received imagery is a multi-spectral image with resolution 2.5m will be provide more information and more detail about objects.

Figure2. Spot 5 Merged imagery 2.5m

Enhance by SAM method

The SAM is a classification method that permits rapid mapping by calculating the spectral similarity between the image spectrums to reference reflectance spectra (Hunter and Power, 2002). The reference spectra can either be taken from laboratory or field measurements or extracted directly from the image.

SAM measures the spectral similarity by calculating the angle between the two spectra, treating them as vectors in n-dimensional space (Rowan and Mars, 2003). Small angles between the two spectrums indicate high similarity and high angles indicate low similarity. This method is not affected by solar illumination factors, because the angle between the two vectors is independent of the vectors length (Crosta et al., 1998). It takes the arccosine of the dot product between the test spectrums "t" to a reference spectrum "r" with the following equation (Carvalho and Meneses, 2000).

Where nb = the number of bands t

= test spectrum

r

= reference spectrum

The SAM directly compares the target spectrum

roads with each pixel’s spectrum to determine the

similarity of their shape. The result of using SAM

method has been created the imagery with the

enhanced road objects than others. The spectral

value of the road objects have been decreased

distance and advanced gradually from 220 to 255 in

the black-white data.

Figure3. The enhanced imagery by SAM method Road extraction

This step divides the image into segment corresponding to real-world objects. Merging is an optional step used for solving over-segmentation problems. Now day, the segmentation of objects based on spatial, spectral, texture, band ratio is popular in commercial remote sensing software. It helps researchers on classification of objects to establish thematic mapping so much. In this paper we also used feature extraction tools in ENVI 4.4 software to extract the road from the image have enhanced by SAM method and the image after fusion of panchromatic and multi-spectral image.

Figure4. The extracted road from enhancement image by SAM method

Figure5. The extracted road from the merged image Road vector

After the road extraction step, the received road objects information is raster data. Road raster have been converted into road vector using ENVI version 4.4. There are normally some errors on the road data as broken point or line (see Figure6). The converted road vectors which were compared with the existing official road vector and find their difference have been corrected by the hand-made digitizing. Thus the correct road vectors can be acquired.

Figure6. The extracted road vector

Figure7. The corrected road vector III. RESULTS

After the comparison of the extracted road results from two images: enhanced image and merged image (see figure4 and figure5) we recognized extracted road objects from merged image were not continues and mixed some other features. While the enhanced image has been extracted road network is more exactly and no noise.

Finally, the result of this paper is the corrected road vector has shown in figure7.

IV. CONCLUSTION

Despite the result of road extraction based on SAM method is quite rapid and efficient. But the accuracy of road extraction always depended on scale of map.

In the small scale of map, some small road but necessary have been missed out. In addition, there are a lot of features and shadow will be reduced the accuracy of road extraction in the complex city.

REFERENCES

Crosta, A.P., C. Sabine. and J.V. Taranik, 1998.

Hydrothermal Alteration Mapping at Bodie, California, Using AVIRIS Hyperspectral Data.

Remote Sensing of Environment, Vol. 65, pp. 309- 319.

De Carvalho, O.A. and P.R. Meneses, 2000. Spectral Correlation Mapper (SCM); An improvement on the Spectral Angle Mapper (SAM). Summaries of the 9

JPL Airborne Earth Science Workshop, JPL Publication 00-18, pp. 9

Hunter, E.L. and C.H. Power, , 2002. An assessment of two classification methods for mapping Thames Estuary intertidal habitats using CASI data.

International Journal of Remote Sensing, Vol. 23, pp.

2989-3008

Koutaki, G. and K. Uchimara, 2004. Automatic road extraction based on cross detection in suburb.

Proceedings of the SPIE, Vol. 5299, pp. 337-344

Rowan, L.C. and J.C. Mars, 2003. Lithologic

mapping in the Mountain Pass, California area using

Advanced Spaceborne Thermal Emission and

Reflection Radiometer (ASTER) data. Remote

Sensing of Environment, Vol. 84, pp. 350-366