Abstract
Conventional classification algorithms have shown great success in balanced hyperspectral data classification. However, the imbalanced class distribution is a fundamental problem of hyperspectral data, and it is regarded as one of the great challenges in classification tasks. To solve this problem, a non-ANN based deep learning, namely SMOTE-Based Weighted Deep Rotation Forest (SMOTE-WDRoF) is proposed in this paper. First, the neighboring pixels of instances are introduced as the spatial information and balanced datasets are created by using the SMOTE algorithm. Second, these datasets are fed into the WDRoF model that consists of the rotation forest and the multi-level cascaded random forests. Specifically, the rotation forest is used to generate rotation feature vectors, which are input into the subsequent cascade forest. Furthermore, the output probability of each level and the original data are stacked as the dataset of the next level. And the sample weights are automatically adjusted according to the dynamic weight function constructed by the classification results of each level. Compared with the traditional deep learning approaches, the proposed method consumes much less training time. The experimental results on four public hyperspectral data demonstrate that the proposed method can get better performance than support vector machine, random forest, rotation forest, SMOTE combined rotation forest, convolutional neural network, and rotation-based deep forest in multiclass imbalance learning.
Highlights
Hyperspectral imagery is simultaneously obtained by remote sensors in dozens or hundreds of narrow and contiguous wavelength bands [1,2,3,4,5]
Algorithm synthetic minority oversampling technique (SMOTE)-Rotation Forest (RoF) has balanced the dataset by synthesizing new samples, its classification performance is worse than SMOTE-Weighted Deep Rotation Forest (WDRoF)
Worth noting that support vector machine (SVM) algorithm is the worst performer as it pays no attention to the recognition of the minority classes, and its classification accuracy for Class 7 is 0
Summary
Hyperspectral imagery is simultaneously obtained by remote sensors in dozens or hundreds of narrow and contiguous wavelength bands [1,2,3,4,5]. Compared with traditional panchromatic and multispectral remote sensing images, hyperspectral imagery carry a wealth of spectral information, which enables more accurate discrimination of different objects. The hyperspectral image classification is a significant research topic and it centers on assigning class labels to pixels. I.e., the proportion of samples belonging to each class, plays an extremely important part in classification research. Some traditional classification methods, such as maximum likelihood classification [16], support vector machine (SVM) [17] and artificial neural network [18], have acquired satisfactory performance on the balanced hyperspectral data
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