An improved classification of hyperspectral imaging based on spectral signature and gray level co-occurrence matrix

Hyperspectral imaging (HSI) has been used to perform objects identification and change detection in natural environment. Indeed, HSI provide more detailed information due to the high spectral, spatial and temporal resolution. However, the high spatial and spectral resolutions of HSI enable to precisely characterize the information pixel content. In this work, we are interested to improve the classification of HSI. The proposed approach consists essentially of two steps: features extraction and classification of this data. Most conventional approaches treat the spatial information without considering the spectral information contained in each pixel, for that, we propose a new approach for features extraction based on spatial and spectral tri-occurrence matrix defined on cubic neighborhoods. This method enables the integration of the spectral signature in the classical model for calculating the cooccurrence matrix to result the 3D-Gray Level Co-occurrence Matrix (GLCM). Concerning the classification step, we are mainly interested in the supervised classification approach. We used the Support Vector Machine (SVM) allowing classification without using a dimensionality reduction. We will consequently test the proposed approach on an IHS that was recorded by an AVIRIS sensor. It’s an Indiana Pines scene which is a vegetation zone captures in north-western Indiana. It’s composed of two spatial dimensions of size 145X145 pixels and with spatial resolution of 20m per pixel, and a spectral dimension with 220 bands. The choice of this image is melted by the existence of a ground truth and its permanent use in all IHS analysis problems. The experimental results indicate a mean accuracy values of 70.73% for VGLCM. It shown the robustness of our perspective approach better classification rate and high accuracy.