Spatial–Spectral Fusion in Different Swath Widths by a Recurrent Expanding Residual Convolutional Neural Network.

The quality of remotely sensed images is usually determined by their spatial resolution, spectral resolution, and coverage. However, due to limitations in the sensor hardware, the spectral resolution, spatial resolution, and swath width of the coverage are mutually constrained. Remote sensing image fusion aims at overcoming the different constraints of remote sensing images, to achieve the purpose of combining the useful information in the different images. However, the traditional spatial–spectral fusion approach is to use data in the same swath width that covers the same area and only considers the mutually constrained conditions between the spectral resolution and spatial resolution. To simultaneously solve the image fusion problems of the swath width, spatial resolution, and spectral resolution, this paper introduces a method with multi-scale feature extraction and residual learning with recurrent expanding. To discuss the sensitivity of convolution operation to different variables of images in different swath widths, we set the sensitivity experiments in the coverage ratio and offset position. We also performed the simulation and real experiments to verify the effectiveness of the proposed framework with the Sentinel-2 data, which simulated the different widths. [ABSTRACT FROM AUTHOR]