Automation of Road Extraction from Space and Aerial Images
ArminGruen, Haihong Li
Institute of Gerodesy and photogrammetry
Swiss Federal Institute of Technology
ETH_Hoenggerberg
CH-8093 Zurich
Switzerland
Abstract
This paper deals with automation of monoscopic extraction of liner features, in particular roads ,from space or aerial images. The objective is to devise a digital photogrammetry strategy for the automation of object extraction and precise measurement during GIS data acquisition in order to facilitate and speed-up the GIC generation and updating processes .the proposed techniques can be either used in a monoplotting mode (combining one image with the underling DTM)or in multi-image mode. We will focus here on the first option.
We present some of the results which we obtained with various model-based features extraction techniques, e.g. dynamic programming , and LSB-snakes(Least Squares B-spline snakes).In these model-driven feature extraction schemes a road is represented by a generic model with various photometric and geometric properties .We will briefly report about the mathematical approaches from optimization and estimation that have been used and address also the implementation and application aspects.
1. Introduction
Of all parts in the process of GIS data generation from aerial photographs and satellite images ,the actual mapping phase is one of the most time consuming and expensive. Research is therefore increasingly focusing on the development of efficient methods to extract topographical features like houses and roads from digital images. As fully automatic methods from mapping are still out of reach ,semi-automatic methods for feature extraction that interact with a human operator are considered to be a good compromise , compromise ,combining the mensuration speed and accuracy of a computer algorithm with the interpretation skill of the operator
This paper deals with road extraction, a semi-automatic monoplotting approach to extract road network from digital images for GIS data capture ,where the identification takes is performed manually on a single image, while special automatic digital module performs the high precision road tracking . more specifically , a human operator is used to identify a road from belongs to and provides some very few seed points coarsely distributed. It is done through activation of a mouse in a convenient interaction graphics -images used interface . Subsequently , with these seed points as approximation of the position and shape ,the road will be extracted .these techniques can be either used in a monoplotting mode (combining one image with the underlying DTM)on in a multi -image mode
The next section provides an overview of our sequence of algorithms as it runs in monoplotting mode .section 3outlines the mathematical representation and implementation of our model driven road extraction algorithms . Section 4 presents some experimental result. Finally ,some Conclusion will be given in section 5
2. The Concept of Semi -Automatic Road Extraction
A semi-automatic scheme for the extraction of road network for digital images is shown in Figure 1. Its central procedure is a model driver feature extraction algorithm based on either dynamic programming or LSB-snakes (least squares B-spline snakes).
Fig 1 : A semi-automatic road extraction scheme
In the current implement , image preprocessing and sharpening are performed by two separate programs. The wall is used for preprocessing to enhance the image and facilitate the subsequent road extraction process by locally forcing the gray value mean and especially contrast (dynamic range )to fit certain target values (baltsavias,1991.)after the wallis filtering , wavelet transform is applied to the image is displayed in a convenient interactive graphics-image user interface. The road identification and classification are performed by the human operator . After some very few seed points have been given by the operator, through activation of a mouse on the displayed image , the road will be extracted automatically and precisely by a model driven feature extraction algorithm whose details will be given in the next sections . the algorithm derives either image coordinates (x,y) or orthoimage coordinates (x,y)or space coordinates(x,y,z)of the roads ,whereby the z- component comes from real -time interpolation within an underlying DTM.if an orthophoto is used this is simply done by a bi-linear interpolation . otherwise, a camera model or projection equation is applied , as illustrated in Figure2 , in an iteration procedure for the computation of (x, y ) coordinates and interpolation of the Z-component
Fig.2 : 3-D interpolation within the DTM
