Study on quasi-3-dimensional texture features for multi-, hyper-spectral remote sensing data analysis

According to the synchronous acquirement of multi-, hyper-spectral remote sensed imagery, a Gray Level Difference Associated Possibility matrix (GLDAP) method is proposed in the paper to analyze visual differences between multi-band data. The matrix is built on two bands of image that are selected in light of land-cover spectrum characteristics. Thereafter, the co-varying statistics of gray level in each image is recorded and quasi-3-dimention texture features are extracted based on GLDAP. During experiments, GLDAP is employed in classifications and annotations of land cover types, compared with GLCM method. The results reveal that the GLDAP has better performances than GLCM. Moreover, it could overcome the limitation of single band processing and understanding, on which GLCM based, and to a certain degree, decrease misrecognition rate caused by worse visual discrimination of land types at data level. The two methods have same time complexity; hence, GLDAP may be accepted as another choice in getting excellent precision and better performance under the same time consuming.