Modified PLVP with Optimised Deep Learning for Morphological based Road Extraction

Kolhe, Abhay; Bhise, Archana

doi:10.1080/19479832.2020.1864785

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…Research scholars have extracted information from the spectral features, shape features, and spatial relationships of HRSIs before classifying and identifying roads [5][6][7]. Currently, much road extraction work has been performed on urban road datasets [8][9][10][11][12][13][14][15][16][17], but there is a lack of extraction work on mountain roads. On the one hand, there is a lack of road datasets in the complex environment of mountainous areas, and on the other hand, there is a lack of effective models for road extraction in mountainous areas.…”

Section: Introductionmentioning

confidence: 99%

Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark

Zhang

Wang

et al. 2022

Remote Sensing

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Mountain roads are of great significance to traffic navigation and military road planning. Extracting mountain roads based on high-resolution remote sensing images (HRSIs) is a hot spot in current road extraction research. However, massive terrain objects, blurred road edges, and sand coverage in complex environments make it challenging to extract mountain roads from HRSIs. Complex environments result in weak research results on targeted extraction models and a lack of corresponding datasets. To solve the above problems, first, we propose a new dataset: Road Datasets in Complex Mountain Environments (RDCME). RDCME comes from the QuickBird satellite, which is at an elevation between 1264 m and 1502 m with a resolution of 0.61 m; it contains 775 image samples, including red, green, and blue channels. Then, we propose the Light Roadformer model, which uses a transformer module and self-attention module to focus on extracting more accurate road edge information. A post-process module is further used to remove incorrectly predicted road segments. Compared with previous related models, the Light Roadformer proposed in this study has higher accuracy. Light Roadformer achieved the highest IoU of 89.5% for roads on the validation set and 88.8% for roads on the test set. The test on RDCME using Light Roadformer shows that the results of this study have broad application prospects in the extraction of mountain roads with similar backgrounds.

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