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Improved cascade forest deep learning model for hyperspectral imagery classification

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Land, Environment and Disaster Monitoring. Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China

  • Email：wufuyu666@163.com
  • Introduction：武复宇，1993年生，男，博士研究生，研究方向为高光谱信息处理。E-mail: wufuyu666@163.com
WU Fuyu1,  
  • Affiliation：

    Key Laboratory of Land, Environment and Disaster Monitoring. Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China

WANG Xue1,  
  • Affiliation：

    The Second Surveying and Mapping Institute of Hebei, Shijiazhuang 050037, China

DING Jianwei3,  
  • Affiliation：

    Key Laboratory for Satellite Mapping Technology and Applications of Ministry of Natural Resources, Nanjing University, Nanjing 210023, China

DU Peijun4,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China

    Key Laboratory of Land, Environment and Disaster Monitoring. Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China

  • Email：tankuncu@126.com
  • Introduction：谭琨，1981年生，男，教授，研究方向为高光谱遥感，环境遥感。E-mail:tankuncu@126.com
TAN Kun21*

Resümee

With the release of GF-5 hyperspectral data, hyperspectral remote sensing plays an increasingly important role in environmental monitoring, emergency management, and object extraction. Classification is the primary problem of hyperspectral image applications. Some cascade forest models have been proposed to overcome the limitations of traditional deep neural networks, such as requiring excessive training samples and optimization of a large number of hyperparameters. Traditional cascade forest models have several disadvantages, such as (1) high model complexity, (2) homogeneous base classifiers, and (3) inability to discriminate the similar spectrum. In this study, a novel classification approach based on cascade forest is proposed to solve the above drawbacks.The proposed improved cascade forest is an accumulation of layers, and each layer consists of two decision tree forests and a logistic regression classifier. Compared with traditional models, the number of forests in the improved method is reduced from four to two with the same accuracy and efficiency. Meanwhile, the original completely random tree forest is replaced by an efficient rotation forest to improve the diversity. The logistic regression classifier is added to determine the separating hyperplane among similar spectra. The number of layers is determined by the accuracy of validation set.The proposed method is implemented on three hyperspectral datasets (GF-5, Indian Pines, and Pavia University datasets). DBN, SVM, RoF, RF, and original cascade forest are selected as the contrast methods. Experimental results on three different real hyperspectral datasets confirms the superiority of the proposed method, especially on the Indian Pines dataset, which has a similar spectrum. The improved cascade model can combine multiple classification results from different base classifiers to obtain the final results through the logistic regression classifier, and the quadratic discriminant process of the logistic regression classifier can effectively improve the classification accuracy. The impacts of the number of trees on the final results are discussed. The proposed model obtains the best performance with optimal parameters. Although the single training time is long, the insensitivity of model parameters immensely improves the training efficiency.Compared with DNN, the improved cascade forest has the following advantages: (1) adaptive to determine the number of layers on the basis of the classification accuracy, (2) few hyperparameters are required in the improved cascade forest, making it easy to optimize the structure, and (3) each forest is independently trained because the training process do not have backpropagation, thereby accelerating the improved cascade forest by the CPU.

Schlüsselwort

remote sensing;hyperspectral classification;cascade forest;rotation forest;ensemble learning;deep learning

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