Improving Object Detection of Remotely Sensed Multispectral Imagery Via Pan-sharpening

Pan-sharpening is used to fuse multispectral images with low spatial resolution and a panchromatic (Pan) image with high spatial resolution to generate synthesized images featured with high spatial and multispectral properties. The pan-sharpened images are assumed valuable for further application. However, there have been a few investigations on the effectiveness of the pan-sharpened products in practice (i.e. object detection), compared with the fact many algorithms for pan-sharpening have been developed. In this paper, improvements contributed by pan-sharpening process for the object detection in multispectral imagery were investigated. Original multispectral images along with the corresponding Pan images acquired by Gaojing-1 (SuperView-1, as the first sub-meter high-resolution commercial remote sensing satellite independently developed in China) satellite were used. Seven algorithms widely used in pan-sharpening were applied separately and compared, while the object detection experiments were done by implementing Faster RCNN. The preliminary findings show: (1) the pan-sharpened images present obviously positive contribution to object detection with Faster RCNN, compared to the original multispectral images; (2) detection results of the pan-sharpened images vary with the algorithms used in pan-sharpening process. Furthermore, this investigation suggests none of the pan-sharpening algorithms showed absolute advantages in image fusion to achieve better object detection consequently.