基于栈式自编码的水体提取方法

为了进一步提高利用遥感图像进行水体提取的准确率和自动化程度,提出一种基于栈式自编码(SAE)深度神经网络的水体提取方法。通过堆叠稀疏自编码器构建深度网络模型,使用逐层贪婪训练法依次训练每层网络,从像素层面无监督学习特征,避免传统神经网络等方法需进行人工特征分析与选取的问题;用学习到的特征结合相应的样本标签有监督训练softmax分类器;利用反向传播(BP)算法微调优化整个模型。采用塔里木河ETM+数据进行实验,基于SAE的水体提取方法准确率达到94.73%,比支持向量机(SVM)和反向传播(BP)神经网络方法分别高出3.28%和4.04%。实验结果表明,所提方法能有效提高水体提取的精度。

Water body extraction method based on stacked autoencoder

To improve the accuracy and automation of extracting water body by using remote sensing image, a method was proposed for water body extraction based on Stacked AutoEncoder (SAE). A deep network model was built by stacking sparse autoencoders and each layer was trained in turn with the greedy layerwise approach. Features were learnt without supervision from the pixel level to avoid the problem that methods such as traditional neural network needed artificial feature analysis and selection. Softmax classifier was trained with supervision by using the learnt features and corresponding labels. Back Propagation (BP) algorithm was used to fine-tune and optimize the whole model. The accuracy of SAE-based method reaches 94.73% by using the Tarim River's ETM+ data to do the experiment, which is 3.28% and 4.04% higher than that of Support Vector Machine (SVM) and BP neural network separately. The experimental results show that the proposed method can effectively improve the accuracy of water body extraction.