Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

Modulation Transfer Function Compensation Through A Modified Wiener Filter For Spatial Image Quality Improvement.

Kebergunaan data imej yang diperolehi dari suatu sensor pengimejan amat bergantung kepada keupayaan sensor tersebut untuk meresolusikan perincian spatial ke satu tahap yang boleh diterima. The usefulness of image data acquired from an imaging sensor critically depends on the ability of the sensor to resolve spatial details to an acceptable level