Full text: Mapping without the sun

Sun Mu-han, Zhou Yin-qing, Xu Hua-ping 
Dept, of Electronic Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China 
muhansun@yahoo.com.cn, huaping_xu@sohu.com 
KEY WORDS: data fusion, digital image processing, target detection, image segmentation, edge detection. 
Detection is the first step in most ATR (Automatic Target Recognition) systems to find out ROI (Region of Interest) with the 
potential targets. The concept and technique of MSDF (Multi-Sensor Data Fusion) exploits the potential of ATR systems mostly 
based on digital images to a great extent. SAR (Synthetic Aperture Radar) can work in all-weather, day and night, and is capable of 
penetration, due to microwave or millimeter wave scattering. All these imaging advantages facilitate the tasks of reconnaissance, 
detection and recognition. It is a guarantee of providing image segmentation results with region integrality, especially for the ground 
targets with weak backscattering. However, the existence of speckle noise caused by its unique imaging mechanism has a bad 
influence on the accuracy of target edge location in SAR images. While remote sensing images obtained by optical sensors can make 
up this limitation. This paper presents a method of automatic detection for line-shape target based on SAR and optical images. To 
improve the universality of the proposed method, the remote sensing images are all processed by traditional image processing 
techniques. The method makes use of the preponderant characters of the line-shape target in SAR images and optical images, i.e. the 
region segmentation result of SAR images and the accurate edge information of optical images, to realize target detection and 
location. Experiments are carried out on both the SAR image and optical image of the same region which have been registered, and 
the detection result demonstrates the validity of the proposed method. 
The processing flow of a typical ATR (Automatic Target 
Recognition) system is composed of target detection, target 
discrimination and target classification and recognition. Target 
detection is applied to the whole image pixel by pixel to find 
out ROI (Region of Interest) with the potential targets. The 
computation burden is really huge, so the algorithm for target 
detection should not be too complicated. Manmade targets in 
remote sensing images can be sorted into three types, i.e. point 
targets, line-type targets and extended targets. To realize 
automatic target recognition, different characters (spatial and 
frequential features, edge, texture etc.) should be utilized 
according to the target type. In this paper, the line-type targets, 
such as highways, bridges, and airport runways, are to be 
detected. For this type of targets, their geometric features are 
often used for detection, and edge information is the best 
expression of this feature in the image. 
The result of target detection of the line-type targets is the edge 
pixels of the targets. Following detection, target discrimination 
will extract the meaningful pixels and eliminate the fake and 
false pixels which have nothing to with the target of the interest. 
Target discrimination involves project representation and 
description, and the schemes are advanced and complex. The 
fewer false alarms exist in the detection result, the less 
computational burden there is for discrimination, and the higher 
efficiency of the whole ATR system is. 
In the field of remote sensing image processing and application, 
taking use of only one sensor of various types to detect target 
has been explored a lot and a great amount of technologies have 
been developed and matured. For example, the line-type target 
detection of optical images mainly adopts edge detection 
technology. There is a lot of speckle noise which is 
multiplicative in SAR images, due to its unique imaging 
mechanism. This multiplicative noise challenges SAR image 
processing. And SAR image processing usually makes use of 
two schemes. One scheme is to denoise the SAR image first and 
then apply traditional image processing for optical images to 
SAR images; the other is to utilize the image gray-level 
information as well as the statistical models for describing SAR 
images. At present, the methods of integrating multiple remote 
sensing images to realize target detection can be classified into 
three categories according to the information fusion level, i.e. 
pixel level, characteristic level and decision level (E.Lallier, 
2000; Min-Sil Yang, 2003; Li Ming, 2004). Because radar 
images are quite different from optical images in many aspects, 
such as image features and target characteristics, most image 
fusion schemes are based either on optical images or on radar 
images. Even some methods uses these two kinds of images, the 
fusion is realized on decision level. 
This paper proposed a method for line-type detection 
combining the edge information in optical images and the 
region boundaries in segmented SAR images, based on the 
analysis of both image characteristics and how the target 
express itself differently in two images. This method is the 
image fusion on characteristic level in nature. And the 
characteristic used in detection is the edge pixels of target. First, 
the mature edge detection technology is used to extract as much 
edge information as possible from the registered optical image; 
then the region boundaries of ROI in the SAR image after 
segmentation are regarded as reference in order to eliminate a 
great lot of irrelevant edge pixels to the target being detected in 
the optical image. The decrease of fake and false edge pixels to 
a great extent can dramatically ease the burden for 
discrimination process, because the objects to be described and 
represented have been reduced. Therefore, the efficiency of the 
ATR system becomes higher. 
The paper is organized as follows. An optical image and a SAR 
image of the same region which have been registered are 
presented in Section 2. Image characteristics and line-type 
target features are being analyzed in this section, based on the 
given images. The general processing flow is presented in

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