This paper presents a methodology of road feature extraction from the different resolutions of RemoteSensing images of Landsat-8 Operational Lander Image (OLI) and ResourceSat-2 of Linear Imaging SelfSensor-3 (LISS-3) and LISS-4 sensors with the spatial resolutions of 15 m, 24 m, and 5 m. In themethodology of road extraction, an index is proposed based on the spectral profile of Roads, also involvingMorphological transform (Top-Hat or Bot-Hat) and Markov Random Fields (MRF). In the proposed index,Short Wave Infrared (SWIR) band has a significant role in the detection of roads from sensors, and it isnamed Normalized Difference Road Index (NDRI). To enhancement of features from the index, Bot-Hattransforms used. To segment the road features from this image, MRF used. The methodology is performedon the OLI, LISS-3 and LISS-4 images, and presented with results
Road Index, SWIR, NDRI, Bot-Hat transform, Markov Random Fields, LISS-3, OLI
1. Anjaneyulu, R. V. G., S. Suresh, M. K. Gali, C. V. Rao, and B. G. Krishna, Swir band synthesis at spatial resolution of liss-4 mx data using liss-3, in Journal of the Indian National Cartographic Association(INCA), vol. 38, pp. 9- 14, INCA, 2019
2. Bhatti, S. S., and N. K. Tripathi, Built-up area extraction using landsat 8 oli imagery, GIScience & Remote Sensing, 51(4), 445-467, doi:10.1080/15481603.2014.939539, 2014
3. Costa, S., V. Santos, D. Melo, and P. Santos, Evaluation of landsat 8 and sentinel-2a data on the correlation between geological mapping and ndvi, in 2017 First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE), pp. 1-4, doi:10.1109/GRSSCHILE.2017.7996006, 2017
4. Dempster, A. P., N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1-38, 1977
5. Dubes, R. C., A. K. Jain, S. G. Nadabar, and C. C. Chen, Mrf model-based algorithms for image segmentation, in [1990] Proceedings. 10th International Conference on Pattern Recognition, vol. 1, pp. 808-814, doi:10.1109/ICPR.1990.118221, 1990
6. Garg, A., D. Pal, H. Singh, and D. C. Pandey, A comparative study of ndbi, ndisi and ndii for extraction of urban impervious surface of dehradun [uttarakhand, india] using landsat 8 imagery, in 2016 International Conference on Emerging Trends in Communication Technologies (ETCT), pp. 1-5, doi:10.1109/ETCT.2016.7882963, 2016
7. Herald, M., and D. A. Roberts, Spectral characteristics of asphalt road aging and deterioration: implications for remote-sensing applications., Applied optics, 44(20), 4327-4334, doi:10.1364/AO.44.004327, 2005
8. Krishnamachari, S., and R. Chellappa, Multiresolution gauss-markov random field models for texture segmentation, IEEE Transactions on Image Processing, 6(2), 251- 267, doi:10.1109/83.551696, 1997
9. Maselli, F., M. Chiesi, and M. Pieri, A new method to enhance the spatial features of multitemporal ndvi image series, IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4967-4979, doi:10.1109/TGRS.2019.2894850, 2019
10. Parveen, R., S. Kulkarni, and V. D. Mytri, Vegetation extraction based on spectral deviations using an irs-1c liss iii image, in 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 158-162, doi:10.1109/WiSPNET.2016.7566111, 2016
11. Reddy, S. L. K., C. V. Rao, P. R. Kumar, R. V. G. Anjaneyulu, and V. M. Bothale, Automatic road feature extraction using mrf from landsat-8 oli images, in 2019 IEEE Recent Advances in Geoscience and Remote Sensing : Technologies, Standards and Applications (TENGARSS), pp. 15-20, doi:10.1109/TENGARSS48957.2019.8976046, 2019
12. Shahi, K., H. Z. Shafri, E. Taherzadeh, S. Mansor, and R. Muniandy, A novel spectral index to automatically extract road networks from worldview2 satellite imagery, The Egyptian Journal of Remote Sensing and Space Science, 18(1), 27-33, doi:https://doi.org/10.1016/j.ejrs.2014.12.003, 2015
13. Singh, P. P., and R. D. Garg, Study of spectral reflectance characteristics of asphalt road surface using geomatics techniques, in 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 516-520, doi:10.1109/ICACCI.2013.6637225, 2013
14. Soille, P., and M. Pesaresi, Advances in mathematical morphology applied to geoscience and remote sensing, IEEE Transactions on Geoscience and Remote Sensing, 40(9), 2042-2055, doi:10.1109/TGRS.2002.804618, 2002
15. Venkateswarlu, E., T. Sivannarayana, and K. V. Ratna Kumar, A comparative analysis of resourcesat-2 liss3 and landast-8 oli imagery, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-8, 987-989, doi:10.5194/isprsarchives-XL-8-987-2014, 2014
16. Verma, A. K., P. K. Garg, K. S. Hari Prasad, and V. K. Dadhwal, Classification of liss iv imagery using decision tree methods , ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8, 1061-1066, doi:10.5194/isprsarchives-XLI-B8-1061-2016, 2016
17. Xu, H., Modification of normalised difference water index (ndwi) to enhance open water features in remotely sensed imagery, International Journal of Remote Sensing, 27(14), 3025-3033, doi:10.1080/01431160600589179, 2007
18. Zha, Y., J. Gao, and S. Ni, Use of normalized difference built-up index in automatically mapping urban areas from tm imagery, International Journal of Remote Sensing, 24(3), 583-594, doi:10.1080/01431160304987, 2003