CAJ | 학술논문

针对林火与相似目标很难区分的问题，提出一种基于稀疏自编码深度神经网络的林火图像分类新方法。采用无监督的特征学习算法稀疏自编码从无标签图像小块中学习特征参数，完成深度神经网络的训练；利用学习到的特征从原始大小分类图像中提取特征并卷积和均值池化特征；对卷积和池化后的特征采用softmax回归来训练最终softmax分类器。实验结果表明，跟传统的BP神经网络相比，新方法能够更有效区分林火与红旗、红叶等类似物体。
침대림화여상사목표흔난구분적문제，제출일충기우희소자편마심도신경망락적림화도상분류신방법。채용무감독적특정학습산법희소자편마종무표첨도상소괴중학습특정삼수，완성심도신경망락적훈련；이용학습도적특정종원시대소분류도상중제취특정병권적화균치지화특정；대권적화지화후적특정채용softmax회귀래훈련최종softmax분류기。실험결과표명，근전통적BP신경망락상비，신방법능구경유효구분림화여홍기、홍협등유사물체。
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.