Application Of Generalized Constrained Energy ...

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of most important features in digital maps is traffic patterns and road networks. ... spectral signatures vary with pixel by pixel due to natural background.

Application of Generalized Constrained Energy Minimization Approach to Urban Road Detection Chinsu Lin Chuin-Mu Wang2 Chein-I Chang3 ]Environmental Monitoring and Planning Laboratory, Department of Forestry, National Chiayi University. 300 University Road, Chiayi (600), Taiwan, Republic of China +886 5 2717476 (t) / +886 5 271 7467 (Q / [email protected] 2Deparment of Electrical Engineering, National Cheng Kung University. Tainan (700), Taiwan, Republic of China 3Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County. 21228-5398 MD, USA + I 410 455 3502 (t) / +1 410 455 3969 (Q / [email protected] Classification of thematic layers have been used to update digital database, particularly digital maps which can be accessed on internet by a fast growing number of users. One of most important features in digital maps is traffic patterns and road networks. Unfortunately, due to low spectral and spatial resolutions of satellite imagery, many roads cannot be extracted at pixel level since the width of most urban and rural roads is less than the pixel size. In addition, road spectral signatures vary with pixel by pixel due to natural background. In this paper, an approach combining a subpixel detection method, called generalized constrained energy minimization with a principal component analysis-based fusion technique is proposed for urban road extraction. Experimental results show that the proposed method can effectively detect roads in Landsat TM and SPOT panchromatic images. Keywordr: Generalized constrained energy minimization, data dimensionality expansion, road detection, remote sensing. INTRODUCTION

Remote sensing techniques have been widely used to survey and monitor land covers and natural resources in past decades. GIS applications also promote the utilization of digital data for automation and intemet accesses. Digital maps are one of most fiequently used remotely sensed data. Extraction of thematic information fiom satellite images is a first initial step towards this need. The distribution of traffic networks is generally an important guide of the development of local and regional urbanization and industrialization. Many citizen activities and information products are developed based on road maps. Urban planning and natural resources management are also closely related to the road development. Therefore, in this paper the problem of the road detection fiom satellite imagery is considered. It is a great challenge to survey city land-cover using satellite imagery with low spatial and spectral resolution, particularly for linear objects, such as roads whose spectral information usually mixed with other objects such as buildings or parking lots or trees. In addition, the objects along roadsides may also affect digital numbers of road pixels. The ambient effect further increases difficulty of road extraction fiom satellite imagery. SPOT panchromatic data have a higher spatial resolution but a poor

0-7803-6359-0/001$10.00 0 2000 IEEE

spectral resolution compared to L.andsat TM data. A higher spatial resolution may purify objeci contents in a pixel, thus it offers more visual discrimination in display. But a low spectral resolution may not be able to differentiate one object fiom another. Although TM data provide more spectral information of a pixel than SPOT panchromatic data, such information is sometimes redundant and confused due to a large area coverage by a single pixel. In general, a 30m-wide road is not popular in cities. By contrast, the width of most city roads is usually smaller than a pixel size in TM data. As a result, a road pixel is generally invisible in the image. Its existence or presence must rely on spectral subpixel detection. TM data and SPOT data have been widely used in land cover and monitoring but they have intrinsic limitation due to low spatial or spectral resolution compared to hyperspectral imagery. However, if the 1Om SPOT panchromatic band can be used in conjunction with better spectral resolution TM data, the extraction of city roads may become possible. Many efforts have been devoted to data fusion to enhance the spectral and spatial information provided by different types of satellite images. In this paper., Landsat TM and SPOT panchromatic data were used to study urban road extraction. The pixel size of TM and panchromatic SPOT image was resampled as 25mx25m and 6.25mx6.25m respectively. Then the two types of images were fused by the principal component analysis (PCA). The resultant spatial merged TM image was then used for road detection. METHOD A complete knowledge of object signatures in an image scene is generally required for image classification or object detection. In many practical applications it is difficult to obtain. In order to realty this requirement, an approach, called Constrained Energy Minimization (CEM) was developed in [ I ] for hyperspectral images. It has been shown that CEM was very effective in subpixel target detection. Recently, CEM was further extended to multispectral imagery, referred to as Generalized constrained energy minimization (GCEM) where a dimensionality expansion (DE) was introduced to expand multspectral image data [:!I. Since CEM has shown its success in subpixel detection, it can be used to extract roads at subpixel level. Our proposed method first uses PCA to fuse the Landsat TM and SPOT panchromatic data to


TM data with better spectral hut rough spatial

Constrained Energy Minimization (CEM) CEM technique was developed for object detection [4-51 and was also successfUlly applied to AVIRIS hyperspectral data to map the distribution of mine tailing [6] and to SPOT multispectral data to detect land-cover [l]. This technique applied here was identical to that in [I]

Pan. SPOT with better spatial hut poor spectral resolution


Target Detection with Constrained Energy Minimization approach

Road signature identification

Fig.1. Procedure of road detection by General Constrained Energy Minimization approach. produce a better spatial resolution TM image. It then applies DE to expand the fused TM data. Finally the CEM is used to detect roads. A block diagram of the proposed method is depicted in Fig. 1 where each of processes is described as follows. Spatial Resolution Fusion Landsat TM data have six bands with a spatial resolution of 30m and one thermal band with 12Om spatial resolution. The fusion method used here is the forward-reverse PCA [3]. It assumes that PC-1 contains only overall scene luminance, all interband variations are contained in other PCs, and scene luminance in the short ware infiared bands is identical to visible scene luminance. A TM image is first forwardtransformed into principal components (PCs). Then, the first PC is removed and its numerical range is determined. It is followed by a process that the SPOT panchromatic image is remapped so that its histogram shape is remained constant, but still in the same numerical range as that of the first PC. Finally, the remapped SPOT panchromatic band is substituted for the first PC. The resultant image is reversetransformed by PCA. Data Dimensionality Expansion (DE) Ren and Chang [2] first introduce the dimensionality expansion idea to develop Generalized Orthogonal Subspace Projection (GOSP) for deriving sufficient multibands to classify more endmembers. DE uses the original multispectral bands as the first-order images and then derives the second-order correlated images of original ban4 and nonlinear correlated images including the square-rooted and the logarithmic transformed as the extra band. The original bands of the image are then augmented to (I2 +51)/2 bands image. Since all of the derived bands produced nonlinearly fkom the original bands, therefore they should present additional useful information and hence could improve the performance in object detection.

Figure 2A is the TM image with 7 bands whose pixel size is 25-meter in which road is not visually interpretable because the width of most roads in the city are narrow and whose signature is merged with their neighboring objects. Such spectral information confusion induced by the pixel size was eliminated by smaller IFOV of the sensor and hence the linear pattern of road is obviously clear in spatially 6.25meter panchromatic SPOT data, which is showed in figure 2B. Figure 2C showed the spatially 6.25-meter TM image which is derived fiom the spatial resolution merging of TM and SPOT images from which the better spatial and spectral resolution of panchromatic SPOT and TM data are all retained and the linear road pattern is thus visually clear. Figure 3 shows the constrained energy minimization image of the spatial merged TM image. To examine how the spectral resolution would influence the road detection, a subset of the image located at the upper left portion was used where the ground truth of the road, a segment of fieeway, was digitized fiom the panehromatic SPOT data. The ground truth of that road is shown in figure 4a and whose corresponding image of both SPOT and spatial merged TM GCEM image is also shown in figure 4b and 4c. In this paper, we apply the receiver-operating characteristic (ROC) curve to assess the efficiency of road detection of the images with different spectral resolution.

Fig. 2 Spatial resolution fusion TM image which has spatially pixel size of 6.25-meter and was derived fiom panchromatic SPOT and Landsat TM images. The inserted yellow-blocked images were stands for the centered white-blocked portion of the original 25-meter a pixel TM image, and pan. SPOT image (6.25-meter a pixel) and the spatial fusion TM image.

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Figure 5 shows the ROC curve of the origtnal panchromatic SPOT image and that of the GCEM technique processed spatial fusion TM image. The horkontal and vertical axis of figure 5 depicted by the false-positive fraction and huepositive fraction, and the area unaler the ROC curve of the SPOT image and that of the GCEM TM image is 0.6272 and 0.9168 respectively. The result iindicates that the GCEM technique is suitable for road detection and imagery with better spatial and spectral resolution is also needed for road detection. DISCUSSION

Fig. 3. Constrained Energy Minimization image of the fusion TM image in figure 2.

Fig. 4. The ground truth (a: left) of assessing site and corresponding SPOT (b: middle) and spatial merged TM CEM image (c: right).

Fig. 5 . The receiver-operating characteristic curve of the SPOT and spatial merged TM GCEM image. The solid line and dash line represents the ROC curve for the TM and SPOT image respectively.

There is an enhanced edge efiect around the road and building in GCEM image in figure 3 . It was supposed that the demand road signature is close to the ones of road and its surrounding objects. This condition indicates that the spectral signature of road is very complex and the GCEM technique is also so sensitive to the sample signature so that the edge effect existed in the GCEM TM image. For taking account the complicated or large variation of road signature and sensitivity of GCEM technique, one improved method that punfylng or homogenizing the road signatures and depressing the others’ signature maybe needed and will be the following research of this study. Another efforts will also need to spend for extracting thematic layer from the image by combining the GCEM technique and the other image extraction technique. REFERENCE [l] C.-I Chang, J.-M. Liu, B.-C. Chueu, C.-M. Wang C. S. Lo, P.-C. Chung H. Ren, C.-W. Yang, D.-J. Ma, ”A generalized constrained energy minimization approach to subpixel target detection for multispectral imagery,” Optical Engineering, vol. 39, no. 5, pp. 1-7, May 2000. [2] H. Ren and C.-I Chang, “A generalized orthogonal subspace projection approach to unsupercised multispectral image classification,” IEEE TGARS (to appear). [3] R.A. Schowengerdt, “Reconstruction of multispatial, multispectral image data using spatial frequency contents,” Photogrammetric Engineering & Remote Sensing, vol. 46,no. 10, pp.1325-1334, Oct. 1980. [4]J.C. Harsanyi, “Detection and Classification of Subpixel Spectral Signatures in Hyperspxtral Image Sequences,” Ph.D. dissertation, Detp. Of Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 1993. [5] C.-I Chang, “Further results on relationship between spectral unmixing and subspace projection,” IEEE Trans. Geosci. Remote Sens., vol. 36, no. 3, pp. 1030-1032, March, 1998. [6] B.D. Van Veen, and K.M. Buckley, “Beamforming: a versatile approach to spatial filtering,” IEEE ASSP Magazine, 4-24, April 1988.


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