| Popis: |
In this work, a deep learning approach has been developed to carry out optical remote sensing pansharpening by the fusion of high spectral and spatial information from two different sources. In the proposed approach, the combination of multimodal information is achieved at multiple levels. The cross fusion deep network (CNet) is designed to directly integrate information from training dataset; this is accomplished by using trainable cross connections between the Multispectral (MS) and the Panchromatic (PAN) images processing branches. To further highlight the benefits of using multiples cross fusion levels for pansharpening, comparison with baselines networks was carried out in this work using three fusion strategies: early, late, and the newly proposed cross fusion. The proposed fusion strategy was evaluated on images from Quickbird sensor and achieved good performance. |
