SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification

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.

Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data Classification

“…CARTs are produced using the Gini impurity or mean-squared error criterion. Finally, using the majority vote criterion, the categorization result is produced [ 36 ].…”

“…The procedure was discovered to take a long time. This work is expanded upon in [ 36 ], where the SMOTE technique is employed to create balanced datasets by incorporating spatial information from surrounding pixels of samples. These datasets are loaded into the weighted rotation forest model, which combines the RoF and multilevel cascaded RF.…”

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

“…However, labeling each pixel requires human skill, which is arduous and time-consuming [ 21 ]. Lack of balance among interclass samples: The class imbalance problems, where each class sample has a wide range of occurrences, diminish the usefulness of many existing algorithms in terms of enhancing minority class accuracy without compromising majority class accuracy, which is a difficult task in and of itself [ 22 ]. The higher dimensionality: Due to incorporating more information in multiple channels, such high-band pictures increase estimation errors.…”

“…Lack of balance among interclass samples: The class imbalance problems, where each class sample has a wide range of occurrences, diminish the usefulness of many existing algorithms in terms of enhancing minority class accuracy without compromising majority class accuracy, which is a difficult task in and of itself [ 22 ].…”

A hybrid approach for the detection and monitoring of people having personality disorders on social networks

“…After an empirical study, we decide to use the SMOTE technique, since it has demonstrated significant effectiveness in various applications and fields (Quan et al. 2021 ; Ishaq et al. 2021 ) and this is a perfect fit for our case, as our corpus is not related to a specific field.…”