Link the remote sensing big data to the image features via wavelet transformation

With the development of remote sensing technologies, especially the improvement of spatial, time and spectrum resolution, the volume of remote sensing data is bigger. Meanwhile, the remote sensing textures of the same ground object present different features in various temporal and spatial scales. Therefore, it is difficult to describe overall features of remote sensing big data with different time and spatial resolution. To represent big data features conveniently and intuitively compared with classical methods, we propose some texture descriptors from different sides based on wavelet transforms. These descriptors include a statistical descriptor based on statistical mean, variance, skewness, and kurtosis; a directional descriptor based on a gradient histogram; a periodical descriptor based on auto-correlation; and a low-frequency statistical descriptor based on the Gaussian mixture model. We analyze three different types of remote sensing textures and contrast the results similarities and differences in three different analysis domains to demonstrate the validity of the texture descriptors. Moreover, we select three factors representing texture distributions in the wavelet transform domain to verify that the texture descriptors could be better to classify texture types. Consequently, the texture descriptors appropriate for describe remote sensing big data overall features with simple calculation and intuitive meaning.