Morphological Attribute Profile Cube and Deep Random Forest for Small Sample Classification of Hyperspectral Image

Abstract

Deep learning based methods have made great progress in hyperspectral image classification. However, training a deep learning model often requires a large number of labeled samples, which are not always available in practical applications. In this paper, a simple but innovative classification paradigm to exploit morphological attribute profile cube is proposed to improve the small sample classification performance of hyperspectral image. First, morphological attribute profiles are constructed by applying different morphological filters to hyperspectral image. Morphological attribute profile cubes are then extracted as the feature of a sample. Second, the obtained morphological attribute profile cubes are scanned with multiple scale sliding windows to make full use of the rich spatial-spectral information. Finally, the features after multi-grained scanning are input into a deep forest classifier to complete the classification task. In this way, the proposed method could use a deep network structure to improve the classification accuracy. To demonstrate the effectiveness of the proposed method, the classification experiments are carried on three widely used hyperspectral data sets. The experimental results demonstrate that the proposed method can outperform the conventional semi-supervised methods and the state-of-the-art deep learning based methods. The demo code on the Salinas dataset is released on the page: https://github.com/liubing220524/ MAPC-DRF-HSI.

Keywords

• Hyperspectral image classification
• morphological attribute profile cube
• multi-grained scanning
• deep random forest