Improving Oriented Object Detection in Optical Remote Sensing

A study published in Remote Sensing proposed a multilevel stacked context network (MSCNet) to enhance target detection accuracy and the feature pyramid network (FPN) representation by aggregating the logical relationships between different contexts and objects in remote sensing images. The remote sensing data's acquisition costs have been steadily decreasing, data sources have been steadily expanding, and image resolution and quality have improved due to the exponential growth of remote sensing technology. As a result, remote sensing imagery is increasingly adopted across various sectors. Convolutional neural networks have recently emerged as a powerful technique for analyzing remote sensing data. It generates feature representations directly from the original image pixels, while its deep stacked structure aids in extracting more abstract semantic features.