Interpreting Black-Box Semantic Segmentation Models in Remote Sensing Applications (Unknown language)

How to get this document?

Commercial Copyright fee: €14.50 Basic fee: €4.00 Total price: €18.50
Academic Copyright fee: €4.50 Basic fee: €2.00 Total price: €6.50

In the interpretability literature, attention is focused on understanding black-box classifiers, but many problems ranging from medicine through agriculture and crisis response in humanitarian aid are tackled by semantic segmentation models. The absence of interpretability for these canonical problems in computer vision motivates this study. In this study we present a usercentric approach that blends techniques from interpretability, representation learning, and interactive visualization. It allows to visualize and link latent representation to real data instances as well as qualitatively assess strength of predictions. We have applied our method to a deep learning model for semantic segmentation, U-Net, in a remote sensing application of building detection. This application is of high interest for humanitarian crisis response teams that rely on satellite images analysis. Preliminary results shows utility in understanding semantic segmentation models, demo presenting the idea is available online.

Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing
Fan, Chaoran / Hauser, Helwig | 2019
Interpreting Black-Box Semantic Segmentation Models in Remote Sensing Applications
Janik, Adrianna / Sankaran, Kris / Ortiz, Anthony | 2019
Visual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curves
Silva, Carla / d'Orey, Pedro M. / Aguiar, Ana | 2019
Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks
Hamid, Sagad / Derstroff, Adrian / Klemm, Sören / Ngo, Quynh Quang / Jiang, Xiaoyi / Linsen, Lars | 2019