Accessibility navigation

Bi-gradient verification for Grad-CAM towards accurate visual explanation for remote sensing images

Song, W., Dai, S., Wang, J., Huang, D., Liotta, A. and Di Fatta, G. (2019) Bi-gradient verification for Grad-CAM towards accurate visual explanation for remote sensing images. In: 2019 International Conference on Data Mining Workshops (ICDMW), 8-11 Nov 2019, Beijing, China, pp. 473-479,

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1109/ICDMW.2019.00074


Gradient-weighted Class Activation Mapping (Grad-CAM) has been a successful technique to produce visual explanation for CNN-based models. In this paper, we verify its applicability in the task of remote sensing image classification. The results show Grad-CAM gives contradictory localization of the important regions for some remote sensing images. To solve this problem, we propose a new strategy, bidirectional gradient verification (BiGradV), to rectify the visual explanation produced by Grad-CAM. The BiGradV is based on the fact both positive and negative gradients can be sensitive to class discrimination of remote sensing images. It designs an internal feature map occlusion with confidence drop decision to verifying which directional gradients works for certain class. The verified gradients are then used to gain the correct visual explanation. Experiments on both remote sensing image dataset and general image datasets demonstrate our proposed strategy is effective and generalized. It could provide a good complement to the Grad-CAM based methods.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:89519

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation