. Weakly Supervised Semantic Segmentation for Remote Sensing Hyperspectral Imaging. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020). 2020.
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.
