2026-04-13T01:49:11Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1924822026-02-02T09:21:00Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Moliner, Eloi author Salgueiro Romero, Luis Fernando author Vilaplana Besler, Verónica author 2020 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. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció Remote sensing Weakly-supervised segmentation Remote sensing Hyperspectral image Teledetecció Weakly supervised semantic segmentation for remote sensing hyperspectral imaging