Sobel Heuristic Kernel for Aerial Semantic Segmentation
Tao Hu, Yao Wang, Yisong Chen, Peng Lu, Heng Wang, Guoping Wang
IEEE International Conference on Image Processing (ICIP), pp. 3074-3078, 2018.
Abstract
Misclassification in semantic segmentation mostly occurs in the pixels around the semantic contour. In this work, we address the task of aerial image segmentation by borrowing the kernel prior from classical edge detecting operator. We propose a module called Sobel Heuristic Kernel(SHK). Our work makes several main contributions and experimentally shows good performance. To the best of our knowledge, we are the first to combine traditional edge detection method and deep learning method in semantic segmentation. Our SHK module reaches state of the art in the Inria Aerial Image Labeling dataset.Links
doi: 10.1109/ICIP.2018.8451170
Paper: hu18_icip.pdf
BibTeX
@inproceedings{hu18_icip,
title = {Sobel Heuristic Kernel for Aerial Semantic Segmentation},
author = {Hu, Tao and Wang, Yao and Chen, Yisong and Lu, Peng and Wang, Heng and Wang, Guoping},
year = {2018},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2018.8451170},
pages = {3074-3078}
}
