Densely connected deep random forest for hyperspectral imagery classification
Densely connected deep random forest for hyperspectral imagery classification
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DOI：
10.1080/01431161.2018.1547932
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发表时间：
2019-05-03
影响因子：
3.4
通讯作者：
Jiao, Licheng
中科院分区：
文献类型：
--
作者：
Cao, Xianghai;Li, Renjie;Jiao, Licheng
近年来，基于深度学习的方法被广泛应用于高光谱图像的分类。然而，这些深层模型需要大量的训练样本来调整丰富的参数，计算量大。因此，这些算法中的大多数都需要使用高性能的图形处理单元(GPU)进行加速。提出了一种新的高光谱图像分类模型--密集连通深度随机森林模型(DCDRF)。该模型由多个前向连通的随机森林组成。DCDRF具有以下优点：1)利用少量的训练样本获得令人满意的分类精度；2)可以在中央处理器(CPU)上高效运行；3)训练过程中只涉及较少的参数。基于三幅高光谱图像的实验结果表明，该方法比传统的基于深度学习的分类方法具有更好的分类性能。
In very recent years, deep learning based methods have been widely introduced for the classification of hyperspectral images (HSI). However, these deep models need lots of training samples to tune abundant parameters which induce a heavy computation burden. Therefore, most of these algorithms need to be accelerated with high-performance graphics processing units (GPU). In this paper, a new deep model-densely connected deep random forest (DCDRF) is proposed to classify the hyperspectral images. This model is composed of multiple forward connected random forests. The DCDRF has following merits: 1) It obtains satisfactory classification accuracy with a small number of training samples, 2) It can be run efficiently on the central processing unit (CPU), 3) Only a few parameters are involved during the training. Experimental results based on three hyperspectral images demonstrate that the proposed method can achieve better classification performance than the conventional deep learning based methods.