Remote Sensing Object Localization with Deep Heterogeneous Superpixel Features

Object detection and localization within high-resolution remote sensing imagery (HR-RSI) is a challenging task for a variety of reasons, such as the complexity and clutter of the image scene and the compactness of the intermixed object classes. Even the most comprehensive training datasets cannot adequately account for the rich diversity and complexity of anthropogenic objects and their contextual settings in large-scale HR-RSI collections. Recent approaches using deep learning techniques include bounding box approaches (e.g., YOLO), object nomination then recognition (e.g., R-CNN), and post-detection object localization of deep neural network detections. Herein, we propose a novel technique that leverages heterogeneous superpixels and deep neural feature extraction to classify the superpixel segmentation through relational analysis. In this preliminary research, we demonstrate the validity of this approach for object detection and localization, as well as its suitability for identifying the irregular shapes of objects (as opposed to a bounding box). Experiments are performed using a sub-set of the xView benchmark dataset with a goal of spearheading future techniques in cluttered scene object recognition that allows deep feature extractors to have more focus on the target objects instead of the surrounding area or nearby object pixels.