Hyperspectral remote sensing image semantic segmentation using extended extrema morphological profiles

Hyperspectral remote sensing images have been shown to be particularly beneficial for detecting the types of materials in a scene due to their unique spectral properties. This paper proposes a novel semantic segmentation method for hyperspectral image (HSI), which is based on a new spatial-spectral filtering, called extended extrema morphological profiles (EEMPs). Firstly, principal component analysis (PCA) is used as the feature extractor to construct the feature maps by extracting the first informative feature from the hyperspectral image (HSI). Secondly, the extrema morphological profiles (EMPs) are used to extract the spatial-spectral feature from the informative feature maps to construct the EEMPs. Finally, support vector machine (SVM) is utilized to obtain accurate semantic segmentation from the EEMPs. In order to evaluate the semantic segmentation results, the proposed method is tested on a widely used hyperspectral dataset, i.e., Houston dataset, and four metrics, i.e., class accuracy (CA), overall accuracy (OA), average accuracy (AA), and Kappa coefficient, are used to quantitatively measure the segmentation accuracy. The experimental results demonstrate that EEMPs can efficiently achieve good semantic segmentation accuracy.