Abstract
The emergence of high-resolution satellite imagery is attracting new applications which can take advantage of remotely sensed data for mapping, inventory, and change detection. Automated collection of roadway inventory features is one such application. To this end, it is important to investigate the performance of conventional feature extraction techniques when applied to high-resolution images and to develop new techniques for extraction of roadway features using one-meter, or higher, resolution imagery. In this paper, classification- based and edge detection-based techniques potential for automated extraction of roadway features from high-resolution satellite imagery are described, tested, and evaluated. Of possible techniques, the applicability of conventional classification algorithms, the Thin and Robust Zero-Crossing edge detector based on the Laplacian of Gaussian operator, and seeded region growing segmentation is investigated. The advantages and disadvantages of each technique for extracting roadway features are discussed. These techniques are applied to one-meter resolution images (currently simulated using one-meter aerial photos) and the experimental results are presented.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 7-18 |
| Number of pages | 12 |
| Journal | Geocarto International |
| Volume | 14 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1999 |
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ASJC Scopus subject areas
- Geography, Planning and Development
- Water Science and Technology
Cite this
Techniques for automated extraction of roadway inventory features from high-resolution satellite imagery. / Karimi, Hassan A.; Dai, Xiaolong; Khorram, Siamak; Khattak, Aemal J.; Hummer, Joseph E.
In: Geocarto International, Vol. 14, No. 2, 06.1999, p. 7-18.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Techniques for automated extraction of roadway inventory features from high-resolution satellite imagery
AU - Karimi, Hassan A.
AU - Dai, Xiaolong
AU - Khorram, Siamak
AU - Khattak, Aemal J.
AU - Hummer, Joseph E.
PY - 1999/6
Y1 - 1999/6
N2 - The emergence of high-resolution satellite imagery is attracting new applications which can take advantage of remotely sensed data for mapping, inventory, and change detection. Automated collection of roadway inventory features is one such application. To this end, it is important to investigate the performance of conventional feature extraction techniques when applied to high-resolution images and to develop new techniques for extraction of roadway features using one-meter, or higher, resolution imagery. In this paper, classification- based and edge detection-based techniques potential for automated extraction of roadway features from high-resolution satellite imagery are described, tested, and evaluated. Of possible techniques, the applicability of conventional classification algorithms, the Thin and Robust Zero-Crossing edge detector based on the Laplacian of Gaussian operator, and seeded region growing segmentation is investigated. The advantages and disadvantages of each technique for extracting roadway features are discussed. These techniques are applied to one-meter resolution images (currently simulated using one-meter aerial photos) and the experimental results are presented.
AB - The emergence of high-resolution satellite imagery is attracting new applications which can take advantage of remotely sensed data for mapping, inventory, and change detection. Automated collection of roadway inventory features is one such application. To this end, it is important to investigate the performance of conventional feature extraction techniques when applied to high-resolution images and to develop new techniques for extraction of roadway features using one-meter, or higher, resolution imagery. In this paper, classification- based and edge detection-based techniques potential for automated extraction of roadway features from high-resolution satellite imagery are described, tested, and evaluated. Of possible techniques, the applicability of conventional classification algorithms, the Thin and Robust Zero-Crossing edge detector based on the Laplacian of Gaussian operator, and seeded region growing segmentation is investigated. The advantages and disadvantages of each technique for extracting roadway features are discussed. These techniques are applied to one-meter resolution images (currently simulated using one-meter aerial photos) and the experimental results are presented.
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U2 - 10.1080/10106049908542099
DO - 10.1080/10106049908542099
M3 - Article
AN - SCOPUS:85024580660
VL - 14
SP - 7
EP - 18
JO - Geocarto International
JF - Geocarto International
SN - 1010-6049
IS - 2
ER -