Improved Convolutional Networks in Forest Species Identification Task

摘要

Forest species identification is a special case of texture classification problem that can be solved with hand-crafted features. Convolutional Networks (ConvNet) is able to learn features adaptively and it has achieved impressive result in complicated recognition tasks. This paper presents an improvement to ConvNet-based approach inl for forest species identification. Due to the small amount of training data, we proposed the addition of dropout layer to ConvNet architecture and data augmentation to increase the size of training data. New classification process of combining the ConvNet outputs of each image patches is proposed. Our improved ConvNet-based method has achieved promising results.