Russian Journal of Earth Sciences Russian Journal of Earth Sciences Russian Journal of Earth Sciences 1681-1208 46574 10.2205/2021ES000772 ОРИГИНАЛЬНЫЕ СТАТЬИ ORIGINAL ARTICLES ОРИГИНАЛЬНЫЕ СТАТЬИ Road Feature Extraction from LANDSAT-8 and ResourceSat-2 Images Road Feature Extraction from LANDSAT-8 and ResourceSat-2 Images Lenin Kumar Reddy S Lenin Kumar Reddy S leninkumar438@gmail.com Rao C V Rao C V Rajesh Kumar P Rajesh Kumar P Technology and Innovation, MSA, National Remote Sensing Centre (NRSC) ru Technology and Innovation, MSA, National Remote Sensing Centre (NRSC) ru Technology and Innovation, MSA, National Remote Sensing Centre (NRSC) ru Technology and Innovation, MSA, National Remote Sensing Centre (NRSC) ru Andhra University College of Engineering ru Andhra University College of Engineering ru 21 3 29 10 2021 https://rjes.ru/en/nauka/article/46574/view

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

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 Road Index SWIR NDRI Bot-Hat transform Markov Random Fields LISS-3 OLI This research is funded and Resourcesat-2 data provided by the National Remote Sensing Centre (NRSC)/ISRO under the project (0606322EE303) titled “Development of geophysical and special processing algorithms from OCM and ResourceSat sensor data”. We wish to express our sincere thanks to the Director, NRSC and R. V. G. Anjaneyulu, Head, SPD for their encouragement and guidance to publish the paper.

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