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

Abstract:

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 recor- ded 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 re- veal 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 com- plexity; hence, GLDAP may be accepted as another choice in getting excellent precision and better perform- ance under the same time consuming