A Top-Down Application of Multi-Resolution Markov Random Fields with Bilateral Information in Semantic Segmentation of Remote Sensing Images

2018 ◽  
Author(s):  
Hongtai Yao ◽  
Min Zhang ◽  
Bingxue Wang
Keyword(s):  
Remote Sensing ◽  
Random Fields ◽  
Markov Random Fields ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Top Down ◽  
Markov Random

scholarly journals Road Feature Extraction from LANDSAT-8 and ResourceSat-2 Images

2021 ◽  
Vol 21 (3) ◽  
pp. 1-9
Author(s):  
Sama Lenin Kumar Reddy ◽  
◽  
C. V. Rao ◽  
P. Rajesh Kumar ◽  
◽  
...  
Keyword(s):  
Feature Extraction ◽  
Random Fields ◽  
Markov Random Fields ◽  
Short Wave ◽  
Landsat 8 ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Markov Random ◽  
Swir Band

This paper presents a methodology of road feature extraction from the different resolutions of Remote Sensing images of Landsat-8 Operational Lander Image (OLI) and ResourceSat-2 of Linear Imaging Self Sensor-3 (LISS-3) and LISS-4 sensors with the spatial resolutions of 15 m, 24 m, and 5 m. In the methodology of road extraction, an index is proposed based on the spectral profile of Roads, also involving Morphological 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 is named Normalized Difference Road Index (NDRI). To enhancement of features from the index, Bot-Hat transforms used. To segment the road features from this image, MRF used. The methodology is performed on the OLI, LISS-3 and LISS-4 images, and presented with results.

Sign in / Sign up

Export Citation Format

Share Document