计算机工程与应用Issue(24)：173-177,5.DOI:10.3778/j.issn.1002-8331.1401-0392
基于稀疏自编码深度神经网络的林火图像分类
Forest fire image classification based on deep neural network of sparse autoencoder
摘要
Abstract
With the problem that forest fire and its similar objects are difficult to distinguish, this paper presents a new forest fire image classification approach based on deep neural network of sparse autoencoder. Using an unsupervised learning algorithm sparse autoencoder to learn features of large number of small patches from some unlabeled images has completed the training for deep neural network, and then with the learned features, the features can be extracted from large scale images and be convolved and pooled. It uses pooled features to train the softmax classifier by softmax regression. Experimental results show that this new image classification approach can more effectively distinguish forest fire and its similar objects, red flag, red leaves, etc. than traditional neural network does.关键词
稀疏自编码/无监督学习/卷积与池化/softmax回归Key words
sparse autoencoder/unsupervised learning/convolve and pooling/softmax regression分类
信息技术与安全科学引用本文复制引用
王勇,赵俭辉,章登义,叶威..基于稀疏自编码深度神经网络的林火图像分类[J].计算机工程与应用,2014,(24):173-177,5.基金项目
苏州市国际科技合作计划项目（No.SH201115）；湖北省自然科学基金（No.2009-514）。 （）
