Superpixel-Based Multiscale CNN Approach Toward Multiclass Object Segmentation From UAV-Captured Aerial Images

Abstract

Unmanned aerial vehicles (UAVs) are promising remote sensors capable of reforming remote sensing applications. However, for artificial intelligence (AI)-guided tasks, like land cover mapping and ground-object mapping, most deep learning-based architectures fail to extract scale-invariant features, resulting in poor performance accuracy. In this context, the article proposes a superpixel-aided multiscale convolutional neural network (CNN) architecture to avoid misclassification in complex urban aerial images.The proposed framework is a two-tier deep learning-based segmentation architecture. In the first stage, a superpixel-based simple linear iterative cluster (SLIC) algorithm produces superpixel images with crucial contextual information. The second stage comprises a multiscale CNN architecture that uses these information-rich superpixel images to extract scale-invariant features for predicting the object class of each pixel. Two UAV-image-based aerial image datasets: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NITRDrone</i> dataset and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">urban drone dataset</i> (UDD) are considered to perform the experimentation. The proposed model outperforms the considered state-of-the-art methods with an intersection of union (IoU) of 76.39% and 86.85% on UDD and NITRDrone datasets, respectively. Experimentally obtained results prove that the proposed architecture performs superior by achieving better performance accuracy in complex and challenging scenarios.