基于卷积特征和贝叶斯决策的双波段场景分类

针对可见光和近红外双波段场景分类存在图像标注样本少和特征融合质量低的问题,提出了一种基于卷积神经网络(CNN)特征提取和朴素贝叶斯决策融合的双波段场景分类方法。首先,将基于预训练的CNN模型作为双波段图像的特征提取器,避免标注样本少导致的过拟合问题;然后,通过主成分分析降维和特征归一化方法,提高支持向量机的计算速度和每个波段的分类性能;最后,以双波段后验概率为朴素贝叶斯先验概率,构建了决策融合模型,实现场景融合分类。在公开数据集上的实验结果表明,相比单一波段分类和双波段特征级联融合分类方法,本方法的识别率有明显提升,可达到94.3%;比基于传统特征的最优方法高6.4个百分点,与基于CNN的方法识别率相近,且执行简单高效。

Dual-Band Scene Classification Based on Convolutional Features and Bayesian Decision

Aiming at the problems of few labeled samples and low quality of feature fusion in visible and near infrared dual-band scene classification,a dual-band scene classification method based on convolutional neural network(CNN)feature extraction and naive Bayes decision fusion is proposed in this paper.First,the CNN model based on pre training is used as the feature extractor of dual-band image to avoid the over fitting problem caused by few labeled samples.Second,the calculation speed of support vector machine and the classification performance of each band are improved by the dimensionality reduction of principal component analysis and feature normalization method.Finally,using the dual band posterior probability as the naive Bayes prior probability,a decision fusion model is constructed to achieve scene fusion classification.Experimental results on the public dataset show that compared with single-band classification and dual-band feature cascade fusion classification methods,the recognition rate of the method is significantly improved,reaching 94.3%;it is 6.4 percentage points higher than the best method based on traditional features.The recognition rate is similar to the CNN-based method,and the execution is simple and efficient.