Hybrid Differential Morphological Profile Enabled Faster R-Cnn For Object Detection In High-Resolution Remote Sensing Imagery

Deep neural network (DNN) algorithms have increased dramatically across a wide variety of remote sensing applications. However, as computer vision (CV) researchers have utilized the power of DNN, additional shortcomings have become more clear, including the reliance of DNNs on texture information rather than shape information to perform feature extraction for CV tasks. This lack of shape can negatively impact many remote sensing applications, where shape information can provide the key discriminatory features in object detection. To improve the utilization of shape information in DNNs, a novel model architecture was developed that incorporated shape information extracted using the Differential Morphological Profile (DMP), which was directly embedded in the DNN. While this network showed the ability to outperform traditional DNNs in CV tasks, there existed several instances in which the tradeoff of spectral and shape information proved ineffective. In this study, we present a Hybrid DMP-enabled network that utilizes both the spectral information of traditional DNNs and the shape information extracted by DMP. Our results show that this network is capable of outperforming competing methods in overhead remote sensing object detection.