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 Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields

2021 ◽  
Vol 13 (3) ◽  
pp. 465
Author(s):  
Shuyang Wang ◽  
Xiaodong Mu ◽  
Dongfang Yang ◽  
Hao He ◽  
Peng Zhao
Keyword(s):  
Remote Sensing ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Convolutional Network ◽  
The Road ◽  
Disaster Assessment

Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging task due to complex circumstances and factors such as occlusion. To improve the accuracy and connectivity of road extraction, we propose an inner convolution integrated encoder-decoder network with the post-processing of directional conditional random fields. Firstly, we design an inner convolutional network which can propagate information slice-by-slice within feature maps, thus enhancing the learning of road topology and linear features. Additionally, we present the directional conditional random fields to improve the quality of the extracted road by adding the direction of roads to the energy function of the conditional random fields. The experimental results on the Massachusetts road dataset show that the proposed approach achieves high-quality segmentation results, with the F1-score of 84.6%, which outperforms other comparable “state-of-the-art” approaches. The visualization results prove that the proposed approach is able to effectively extract roads from remote sensing images and can solve the road connectivity problem produced by occlusions to some extent.

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.

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