Similarity Retrieval of Multi-source Spatial Data Based on Combination Features
Jin Chuanyang · Geo-information Science · 2006
In this paper, a new approach based on combining two radically different texture features is proposed for similarity retrieval of multi-source remote sensing database. One takes a statistical approach in the form of gray level co-occurrence matrix (GLCM), the other takes a signal processing approach with tree-structured wavelet transform or wavelet packets. Particularly, we also combine texture feature with spatial relation in the statistical method. The similarity retrieval contains six consecutive stages: preprocessing the images in the multisource remote sensing database, feature extraction, feature sequence normalization, weights for feature elements, similarity measure, and experimental evaluation. Through comparison with other methods: such as the gray level co-occurrence matrix, the tree-structured wavelet transform or wavelet packets, in combination GLCM with special relation, the proposed method shows good tradeoff between retrieval effectiveness and efficiency. The experimental results indicated that the combined texture retrieval way has powerful practical merits.