Segmentation of Smoke Plumes Using Fast Local Laplacian Filtering

Abstract

In this paper, we address the problem of smoke plume segmentation from background clutter. Smoke plumes can be generated from fires, explosions, etc. In the mining industry, plumes from blasts need to be characterized in terms of their volume and concentration, for example. Plume segmentation is required in order to start such an analysis.

We present a new image processing approach based on a fast local Laplacian filtering (FLLF) technique. In addition, we discuss how we designed and executed our own field experiments to acquire actual test data of smoke plumes from RGB video cameras. Lastly, we show how the FLLF technique can be used to generate thousands of training samples with applications in machine learning.

Results show that the FLLF technique outperforms state-of-the-art approaches (i.e., SFFCM and an approach by Wang et al.) when tested using metrics such as Accuracy, the Jaccard Index, F1-score, False Alarms and Misses. We also show that the FLLF technique is more computationally efficient.

The authors gratefully acknowledge the IEEE Geoscience and Remote Senging Society (GRSS) for sanctioning a project under “ProjNET” where the Western New York, USA, GRSS Chapter has teamed up with the Kolkata, India, GRSS Chapter.