Improving self-supervised segmentation of urban scenes using NDSM-enhanced CRF

Kevin Qiu; Lars Wagenbach; Dimitri Bulatov; Dorota Iwaszczuk

doi:10.1117/12.3040373

3 January 2025 Improving self-supervised segmentation of urban scenes using NDSM-enhanced CRF

Kevin Qiu, Lars Wagenbach, Dimitri Bulatov, Dorota Iwaszczuk

Author Affiliations +

Proceedings Volume 13263, Land Surface and Cryosphere Remote Sensing V; 132630L (2025) https://doi.org/10.1117/12.3040373
Event: Asia-Pacific Remote Sensing, 2024, Kaohsiung, Taiwan

Conference Poster

Abstract

Semantic segmentation of urban areas is crucial for many applications. Self-supervised networks require few or no labels for training, making them highly appealing approaches. One such network is STEGO¹, which builds upon DINO² and operates without any labeled data, yet effectively segments buildings, vegetation, and roads in the ISPRS Potsdam dataset³. The resulting segmentations are refined using Conditional Random Fields (CRF). In remote sensing, additional channels like the Normalized Digital Surface Model (NDSM) enhance the segmentation task, as pixels of the same class often exhibit similar elevation characteristics, especially when adjacent. Since the transformer-based DINO network is built for RGB data, we extend the CRF with NDSM information to overcome this limitation, introducing a second pairwise potential that encourages neighboring pixels with similar elevation to have the same label. For evaluation in both the linear and cluster probe, we employ Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI) to assess the segmentation against the six classes of the Potsdam dataset, besides standard IoU and accuracy metrics. Enhancing the CRF with elevation information improves the mIoU by 0.83% over the RGB only baseline in the cluster probe, which constitutes a considerable improvement.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kevin Qiu, Lars Wagenbach, Dimitri Bulatov, and Dorota Iwaszczuk "Improving self-supervised segmentation of urban scenes using NDSM-enhanced CRF", Proc. SPIE 13263, Land Surface and Cryosphere Remote Sensing V, 132630L (3 January 2025); https://doi.org/10.1117/12.3040373

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE