基于全卷积神经网络的林木图像分割

针对传统方法进行图像分割易受噪声影响问题，提出了一种基于全卷积神经网络的林木图像分割方法。该方法不需要对图像进行预处理，利用上池化和反卷积层恢复图像分辨率，采用跳跃连接降低网络复杂度，同时避免了梯度消失问题，使用Dropout正则化随机激活网络隐藏单元以防止过拟合，后端结合全连接的条件随机场以恢复对象边缘的细节信息，进一步优化分割结果。该模型能够在林木图像上实现良好的分割。

Segmentation of Forest Image Based on Fully Convolutional Neural Network

As traditional image classification methods are susceptible to noise, this paper proposes a method for forest image recognition and classification based on fully convolutional neural network, which does not require preprocessing. The fully convolutional neural network model can extract features by the convolution layers, then image initial resolution is recovered by the unpooling layers and deconvolution layers. Due to the skip architecture, the model not only overcomes the problem of the gradient vanishing or explosion, but also simplifies the fully convolutional neural network. In order to prevent the over-fitting of the network, Dropout size is adopted. The hiding units are activated randomly. At the later stage, the details of the object’s edge is restored via full-connection conditional random field, which can optimize the final prediction result. The method sets a good performance at the forest image segmentation.