English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The collection of a high number of pixel-based labeled training samples fortree species identification is time consuming and costly in operationalforestry applications. To address this problem, in this paper we investigatethe effectiveness of explanation methods for deep neural networks in performingweakly supervised semantic segmentation using only image-level labels.Specifically, we consider four methods:i) class activation maps (CAM); ii)gradient-based CAM; iii) pixel correlation module; and iv) self-enhancing maps(SEM). We compare these methods with each other using both quantitative andqualitative measures of their segmentation accuracy, as well as theircomputational requirements. Experimental results obtained on an aerial imagearchive show that:i) considered explanation techniques are highly relevant forthe identification of tree species with weak supervision; and ii) the SEMoutperforms the other considered methods. The code for this paper is publiclyavailable at https://git.tu-berlin.de/rsim/rs_wsss.

Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods