Moliner E, Salgueiro L, Vilaplana V. Weakly Supervised Semantic Segmentation for Remote Sensing Hyperspectral Imaging. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020). In Press.

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.