Abstract
The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and moisture in the life cycle on physiological changes, so the detection of newly grown leaves (NGL) is helpful for the estimation of tree growth and even climate change. This study focused on the detection of NGL based on deep learning convolutional neural network (CNN) models with sparse enhancement (SE). As the NGL areas found in forest images have similar sparse characteristics, we used a sparse image to enhance the signal of the NGL. The difference between the NGL and the background could be further improved. We then proposed hybrid CNN models that combined U-net and SegNet features to perform image segmentation. As the NGL in the image were relatively small and tiny targets, in terms of data characteristics, they also belonged to the problem of imbalanced data. Therefore, this paper further proposed 3-Layer SegNet, 3-Layer U-SegNet, 2-Layer U-SegNet, and 2-Layer Conv-U-SegNet architectures to reduce the pooling degree of traditional semantic segmentation models, and used a loss function to increase the weight of the NGL. According to the experimental results, our proposed algorithms were indeed helpful for the image segmentation of NGL and could achieve better kappa results by 0.743.
Highlights
IntroductionInternational Conference on Computer Vision Workshops (ICCVW), Venice, Italy, 22–29 October 2017; pp
This section combines the sparse enhancement (SE) preprocessed by Robust Principal Component Analysis (RPCA) with different kernels with the SegNet and U-Net of the Balanced Cross-Entropy (BCE) lost function and the proposed models used in the previous sections using Area 1 and Area 2
To review the differences among different kernels after preprocessing, the data in the previous section were sequenced according to area under the curve (AUC), true positive rate (TPR), FPR, and kappa coefficient (Kappa) to compile a line chart, as shown in Figures 16 and 17
Summary
International Conference on Computer Vision Workshops (ICCVW), Venice, Italy, 22–29 October 2017; pp. M.F.; De Lamare, R.C. Low-rank and sparse matrix recovery based on a randomized rank-revealing decomposition. In Proceedings of the 2017 22nd International Conference on Digital Signal Processing (DSP), London, UK, 23–25 August 2017; pp. M.; Kwan, C.; Ayhan, B.; Tran, T.D. Burn scar detection using cloudy MODIS images via low-rank and sparsity-based models. In Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, USA, 7–9 December 2016; pp. In Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, USA, 7–9 December 2016; pp. 177–181
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