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Densely connected deep random forest for hyperspectral imagery classification

Cao, Xianghai, Li, Renjie, Ge, Yiming, Wu, Bin, Jiao, Licheng
International journal of remote sensing 2019 v.40 no.9 pp. 3606-3622
algorithms, hyperspectral imagery, models, remote sensing
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.