Comparison of single channel indices for U-Net based segmentation of vegetation in satellite images

Irem Ulku; Panagiotis Barmpoutis; Tania Stathaki; Erdem Akagunduz

doi:10.1117/12.2556374

31 January 2020 Comparison of single channel indices for U-Net based segmentation of vegetation in satellite images

Irem Ulku, Panagiotis Barmpoutis, Tania Stathaki, Erdem Akagunduz

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143319 (2020) https://doi.org/10.1117/12.2556374
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Hyper-spectral satellite imagery, consisting of multiple visible or infrared bands, is extremely dense and weighty for deep operations. Regarding problems related to vegetation as, more specifically, tree segmentation, it is difficult to train deep architectures due to lack of large-scale satellite imagery. In this paper, we compare the success of different single channel indices, which are constructed from multiple bands, for the purpose of tree segmentation in a deep convolutional neural network (CNN) architecture. The utilized indices are either hand-crafted such as excess green index (ExG) and normalized difference vegetation index (NDVI) or reconstructed from the visible bands using feature space transformation methods such as principle component analysis (PCA). For comparison, these features are fed to an identical CNN architecture, which is a standard U-Net-based symmetric encoder-decoder design with hierarchical skip connections and the segmentation success for each single index is recorded. Experimental results show that single bands, which are constructed from the vegetation indices and space transformations, can achieve similar segmentation performances as compared to that of the original multi-channel case.

Citation Download Citation

Irem Ulku, Panagiotis Barmpoutis, Tania Stathaki, and Erdem Akagunduz "Comparison of single channel indices for U-Net based segmentation of vegetation in satellite images", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143319 (31 January 2020); https://doi.org/10.1117/12.2556374

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