A Sparse Deep Learning Model For Privacy Attack On Remote Sensing Images

Deep learning tools have been a new way for privacy attacks on remote sensing images. However, since labeled data of privacy objects in remote sensing images are less, the samples for training are commonly small. Besides, traditional deep neural networks have a huge amount of parameters which leads to over complexity of models and have a great heavy of computation. They are not suitable for small sample image classification task. A sparse method for deep neural network is proposed to reduce the complexity of deep learning model with small samples. A singular value decomposition algorithm is employed to reduce the dimensions of the output feature map of the upper convolution layers, which can alleviate the input burden of the current convolution layer, and decrease a large number of parameters of the deep neural networks, and then restrain the number of redundant or similar feature maps generated by the over-complete schemes in deep learning. Experiments with two remote sensing image data sets UCMLU and WHURS show that the image classification accuracy with our sparse model is better than the plain model, which is improving the accuracy by 3%, besides, its convergence speed is faster.