Satellite Image Classification via Two-Layer Sparse Coding With Biased Image Representation

TLDR: This letter presents a method for satellites image classification involving visual attention into the satellite image classification; biologically inspired saliency information is exploited in the phase of the image representation, making the method more concentrated on the interesting objects and structures.

Abstract: This letter presents a method for satellite image classification aiming at the following two objectives: 1) involving visual attention into the satellite image classification; biologically inspired saliency information is exploited in the phase of the image representation, making our method more concentrated on the interesting objects and structures, and 2) handling the satellite image classification without the learning phase. A two-layer sparse coding (TSC) model is designed to discover the “true” neighbors of the images and bypass the intensive learning phase of the satellite image classification. The underlying philosophy of the TSC is that an image can be more sparsely reconstructed via the images (sparse I) belonging to the same category (sparse II). The images are classified according to a newly defined “image-to-category” similarity based on the coding coefficients. Requiring no training phase, our method achieves very promising results. The experimental comparisons are shown on a real satellite image database.