Recognition of low-resolution objects in remote sensing images

Abstract

Object detection and recognition is one of the important problems in many remote sensing applications such as monitoring, security, rescue mission and other data analysis tasks. So a large number of approaches and techniques have been developed to improve the quality of object detection. Recently, deep learning methods have made significant progress in detecting objects. But when dealing with objects having small size in the image (which are the often case in monitoring or rescue mission), the quality of detection noticeably decreases. To study the performance and the limits of deep learning abilities for object detection in remote sensing imagery with degrading object resolution in the image, a special dataset of aerial images containing objects of interest (human, car) at different resolution has been collected. The dataset consists of images acquired at different distances to the objects of interest, providing representative subsets of object images of various scale. Two state-of-the-art object detection convolutional neural networks (Faster-RCNN and SSD) was evaluated on the collected dataset. The aim of the study was to find out how the object size in the image influences on the detection performance and to estimate the value of object image size at which the performance drops significantly. Also the approaches for improving the small size object recognition were developed and evaluated. First approach uses multimodal image fusion, the second one applies deep learning to increase the resolution of small objects in the image. The performed tests have proved that the developed approaches allow to improve the quality of object recognition when dealing with low resolution object images.