Weakly Supervised Semantic Segmentation For Remote Sensing Hyperspectral Imaging

Eloi Moliner; Luis Salgueiro Romero; Verónica Vilaplana

ICASSP 2020accepted0 citations

Weakly Supervised Semantic Segmentation For Remote Sensing Hyperspectral Imaging

Eloi Moliner, Luis Salgueiro Romero, Verónica Vilaplana

Abstract

This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing system which consists on training a semantic segmentation network from a set of seeds generated by a Support Vector Machine. A region growing algorithm module is applied to the seeds to progressively increase the pixel-level supervision. The proposed method performs better than an SVM, which is one of the most popular segmentation tools in remote sensing image applications.

BibTeX

@inproceedings{icassp2020_weaklysupervised,
  title = {Weakly Supervised Semantic Segmentation For Remote Sensing Hyperspectral Imaging},
  author = {Eloi Moliner and Luis Salgueiro Romero and Verónica Vilaplana},
  booktitle = {ICASSP 2020},
  year = {2020}
}