Evolutionary Learning of Differential Morphological Profile Structure for Shape Feature Enabled Faster R-CNN

Abstract

Recently, computer vision tasks such as classification and object detection have been dominated by deep neural net-work (DNN) approaches. As DNN methodologies have matured, researchers have found that some of the most common DNN techniques result in models that are highly dependent upon the textures and colors of the imagery, rather than the shape, leading to suboptimal network performance. This problem can be especially problematic in the remote sensing domain, where the discrimination of objects for classification or detection may rely heavily on their shape. To combat this lack of shape bias in DNNs, a network was developed to integrate the Differential Morphological Profile (DMP), an image processing technique for shape extraction, with standard convolutional DNNs for performing computer vision tasks on High Resolution Remote Sensing Imagery (HR-RSI). Previously, this network, known as DMPNet, has been applied to both classification and object detection in HR-RSI with high levels of success. However, the hyper-parametric nature of DMPNet structure required researchers to carefully select the parameters of shape extraction, a choice that could greatly help or hinder DMPNet performance. In this study, we utilize a evolutionary computation algorithm (ECA) to learn the parameters of shape extraction from the data presented to the DMPNet for object detection. Our results show that our DMP-enabled detection models perform better object detection in HR-RSI using an ECA to learn shape extraction parameters than manually selected parameters on the same dataset.