FPGA IMPLEMENTATION OF ROAD NETWORK EXTRACTION USING MORPHOLOGICAL OPERATOR

Image Analysis and Stereology

Selvathi Dharmar¹  Sujatha Chinnathevar² 

[1] Mepco schlenk Engineering College, Sivakasi,Tamil Nadu, India;SSM Institute of Engineering and Technology, Dindigul, Tamil Nadu, India;

关键词: connected component;    FPGA;    morphological operation;    performance measures;    road network extraction;    satellite image;   

DOI  :  10.5566/ias.1493

来源: DOAJ

【 摘 要 】

In the remote sensing analysis, automatic extraction of road network from satellite or aerial images is the most needed approach for efficient road database creation, refinement, and updating. Mathematical morphology is a tool for extracting the features of an image that are useful in the representation and description of region shape. Morphological operator plays a significant role in the extraction of road network from satellite images. Most of the image processing algorithms need to handle large amounts of data, high repeatability, and general software is relatively slow to implement, so the system cannot achieve real-time requirements. In this paper, field programmable gate array (FPGA) architecture designed for automatic extraction of road centerline using morphological operator is proposed. Based on simulation and implementation, results are discussed in terms of register transfer level (RTL) design, FPGA editor and resource estimation. For synthesis and implementation of the above architecture, Spartan 3 XC3S400TQ144-4 device is used. The hardware implementation results are compared with software implementation results. The performance of proposed method is evaluated by comparing the results with ground truth road map as reference data and performance measures such as completeness, correctness and quality are calculated. In the software imple-mentation, the average value of completeness, correctness, and quality of various images are 90%, 96%, and 87% respectively. In the hardware implementation, the average value of completeness, correctness, and quality of various images are 87%, 94%, and 85% respectively. These measures prove that the proposed work yields road network very closer to reference road map.

【 授权许可】

Unknown