INFLUENCE OF ADDITIONAL SPECTRAL BANDS FOR EPIPHYTE SEGMENTATION ON DRU-NET

Abstract

Dense Residual U-Net (DRU-Net) is a neural network used for image segmentation. It is based on the U-Net architecture and isa combination of modified ResNet as the encoder and modified DenseNet as the decoder blocks. DRU-Net captures both the local and contextual information. Previous studies on DRU-Net have not tested the influence of the spectral resolution of the images. In an earlier study, the DRU-Net was trained with grayscale images for epiphyte segmentation. The network trained and tested with grayscale images underperformed while varying the illumination and occupancy of the target in the frame. In this study, the same network was trained and tested with RGB images for assessing the increase in overall learning. The performance of the network in segmenting epiphytes under conditions such as good/poor illumination and high/low target occupancy was analyzed. Dice and Jaccard scores were used as evaluation metrics. The DRU-Net model trained with RGB images had an improvement of 20% over the grayscale model in both average Dice and average Jaccard scores of the target class. Based on the higher Dice and Jaccard scores, adding additional spectral information improves DRU-Net learning. The increased computation time required for training DRU-Net with RGB images will result in better output. This model could be further used for identifying multiple epiphytes in images with poor illumination and different occupancy conditions.