Target Detection in Multispectral Images via Detail Enhanced Pansharpening

Object detection in high resolution satellite images has recently become a major concern in new geospatial information methods. The higher spatial resolution with spectral information provides better detection results. Therefore, increasing the image resolution prior to the object detection is important. For this purpose, pansharpening, which uses complementary information from MS and PAN images, is gaining popularity as it helps to increase spatial resolution while preserving the spectral information. This study proposes a detailed enhanced scheme for pansharpening to improve the detection results. Several deep learning models are trained on raw dataset, as well as on the detail enhanced pansharpened images. It is shown that the training stage using proposed detail enhanced scheme provides better detection results compared to classical pansharpening or raw data based training for different deep networks.