SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATION

Spectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms

Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. This study proposes a modified spectral–spatial classification approach for improving the spectral–spatial classification of hyperspectral images. In the proposed method ten spatial/texture features, using mean, standard deviation, contrast, homogeneity, corr...

Morphological memories for feature extraction in hyperspectral images

Hyperspectral Images Classification by Combination of Spatial Features Based on Local Surface Fitting and Spectral Features

Hyperspectral sensors are important tools in monitoring the phenomena of the Earth due to the acquisition of a large number of spectral bands. Hyperspectral image classification is one of the most important fields of hyperspectral data processing, and so far there have been many attempts to increase its accuracy. Spatial features are important due to their ability to increase classification acc...

Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for improving the classification accuracy. However, this approach usually results in features of higher dimension and the curse of the dimensionality problem may arise resulting from the small ratio between the number of training samples and the dimensionality of features. To ease this problem, we propose a ...

Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery

In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervi...