关键词: 稀疏自编码, 无监督学习, 卷积与池化, softmax回归

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.

Key words: sparse autoencoder, unsupervised learning, convolve and pooling, softmax regression