Image Information Mining from Geospatial Archives based on a Combination of the Wavelet Transform and Fourier Phase Descriptor

摘要

In general, reflectance and spatial patterns characterize geospatial data. Current semantic-enabled framework image retrieval systems for geospatial data extract primitive features based on color, texture (Spatial Gray Level Dependency -SGLD matrices), and shape from the segmented homogenous region. However, the form of extracting textural information is computationally expensive. The state-of-the-art image mining system for multimedia image archives uses the wavelet transform for feature extraction to quickly and efficiently capture color and texture information. Since an image consists of three bands, color information is captured by converting the RGB space into HSV space. Thus, a new approach is required to capture the complete reflectance pattern, an important characteristic of geospatial data.This work proposes a new method to perform fast coarse image segmentation using descriptors obtained by combining the 2D-wavelet transform along the spatial axis and the Fourier transform along the spectral axis to capture color and texture information for segmentation. These features are later on used for region-based retrieval in Earth observation data archives. Compared to traditional techniques, result shows that the proposed method provides good retrieval accuracy in terms of F-measure for land cover classes.