基于协同聚类和权重注意力稀疏自编码网络的变化检测方法

Change detection approach based on cooperative clustering and weightedattention sparse autoencoder

遥感变化检测对于监督和管理土地资源利用具有重要作用.针对监督变化检测需要人为干预训练样本的劣势、不平衡数据问题以及基于像素变化检测中的"椒盐"现象,提出基于协同聚类和权重注意力稀疏自编码网络的变化检测方法.方法采用模糊c均值和K-means对差异图协同聚类得到训练和待分类数据,同时在样本中考虑灰度共生矩阵特征,并利用合成少数过采样方法扩充变化样本以解决样本不平衡问题.通过逐层权重注意力模块加强网络对正权重的学习和削弱负权重的影响,自编码分类性能得到提升,其分类结果在差异图超像素分割边界的映射空间中根据约束条件剔除"椒盐"噪声生成变化检测图.所提出方法在变化检测中实现漏检测与误检测平衡,达到了提高变化检测精度的同时减少人为干预的目的.

Remote sensing change detection plays an important role in the supervision and management of land resource utilization. A change detection approach based on collaborative clustering and weighted-attention sparse autoencoders is proposed, which aims at the disadvantage of human intervention in training samples in supervision change detection, the problem of unbalanced data and the phenomenon of “salt and pepper”in change detection based on pixel-level. Fuzzy c-means and K-means are adopted to cluster difference image for training data and data to be classified. Meanwhile, the gray level co-occurrence matrix feature is considered in the samples, and the synthetic minority oversampling technique is utilized to expand the changed samples for solving issues of sample imbalance. Through the layer-wise weight-attention module that enhances the learning of positive weights and weakens the impact of negative weights, the classification performance of autoencoder is improved, and classification results of which in the mapping space of the superpixel segmentation boundary of the difference image eliminate “salt and pepper”noises for generation of change detection map according to the specific constraints. The change detection approach achieves the balance of missing detection and false detection, which increases the accuracy of change detection and reduces the human intervention at the same time.