Scene Recognition of Remotely Sensed Images Based on Bayes Adjoint Batch Normalization
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摘要: 归一化(Normalization)方法作为特征预处理的关键部分，在浅学习和深度学习中都是至关重要的。针对批次归一化方法中存在对批次样本容量依赖较大的问题，当前的优化思路主要是从样本信息的其它维度(比如：通道、层、时间等)来弥补批次样本容量较小的不足。本文从贝叶斯理论的角度出发，通过将总体信息、先验信息和样本信息科学、合理地融合方式，来弥补批次样本容量不足的缺陷，从而可以更加准确地估计样本均值和样本方差，使得归一化后的特征地落入最佳的非饱和区域，以便更好地反应整个特征空间的原始表征，进而深度学习模型可以达到最佳的特征表达能力。实验与分析表明：本文提出的贝叶斯共轭批次归一化方法(BABN)是可行、有效的，在NWPU-RESISC45数据集上，其分类精度比批次归一化方法(BN)要高5.64%。而且，得益于总体信息和先验信息的帮助，BABN受批次样本容量的影响较小。Abstract: Objective: Normalization methods plays an important role in feature preprocessing phase not only in conventional machine learning domain but also in contemporary deep learning domain. Batch Normalization (BN) is very successful, but its performance very depends on the sample size. Therefore, many researchers try to improve it when the sample size is inadequate through adding the sample size merely in the sample information space. Methods: This paper utilizes Bayes theory to integrate general information, prior information and sample information, to offset the inadequate sample information. In this way, it is able to estimate sample mean and sample variance more precisely and more robust especially when the sample size is small, and makes the normalized feature better fall into non- saturating region of activation function, which enables deep learning model to better describe original feature space. Results: The top-1 test classification accuracy in the dataset of NWPU-RESISC45 has been improved by 5.64% than BN. Moreover, with the help of general information and prior information, the proposed method(BABN) is not sensitive to the sample size. Conclusions: The experiment results show that the proposed method (Bayes Adjoint Batch Normalization, BABN) is feasible and effective, and the new method performs better in the remotely sensed image scene recognition than Batch Normalization (BN) method and other variants.
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Keywords:
- scene recognition /
- remotely sensed image /
- normalization /
- Bayes /
- Adjoint
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