scholarly journals An index based road feature extraction from LANDSAT-8 OLI images

2021 ◽  
Vol 11 (2) ◽  
pp. 1319
Author(s):  
Sama Lenin Kumar Reddy ◽  
C. V. Rao ◽  
P. Rajesh Kumar ◽  
R. V. G. Anjaneyulu ◽  
B. Gopala Krishna
Keyword(s):  
Feature Extraction ◽  
Traffic Management ◽  
Markov Random Fields ◽  
Landsat 8 ◽  
Connected Component ◽  
Band Combination ◽  
Markov Random ◽  
Arduous Task ◽  
Segmentation Methods ◽  
Model Segmentation

Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (SSF), is proposed. Then, an iterative adaptive threshold (IAT) based online search with variational min-max and Markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RI) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.

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.

scholarly journals Target Detection of Sar Image using Modified Markov Random Fields Ayed Model Segmentation Along with Google Net Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 11123-11128
Keyword(s):  
Target Detection ◽  
Random Fields ◽  
Markov Random Fields ◽  
Region Of Interest ◽  
Visual Signals ◽  
Modern World ◽  
Similar Data ◽  
Sar Images ◽  
Markov Random ◽  
Model Segmentation

In the modern world the mechanism of target detection in the SAR images have huge assistance for humans to deal with complex visual signals of satellite images effectively. However, the ultimate aim of the paper was to segment the region of interest precisely from despeckling SAR images. This paper proposes a novel modified Markov random fields ayed model segmentation along with Google NET classification target detection. In the initial stage, the image gets despeckled for removing the unwanted noise. The boundaries of the images were calculated for checking the discontinuity using the canny edge detector. Then in the data reduction step by grouping the similar data items. Then the target region was segmented using the modified Markov random fields ayed model methods then the segmented output can undergo the classification process by using the Google NET CNN architecture. The proposed technique was capable of getting better results under risky conditions . Thus, the results validate the target detection of detection rate in different complexity over the existing methodology

Sampling from Gaussian Markov random fields conditioned on linear constraints

2008 ◽  
Vol 48 ◽  
pp. 1041 ◽  
Author(s):  
Daniel Peter Simpson ◽  
Ian W. Turner ◽  
A. N. Pettitt
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Linear Constraints ◽  
Gaussian Markov Random Fields ◽  
Markov Random
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