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Published in:
2023 | OriginalPaper | Chapter
Small Object Detection in Remote Sensing Images
Authors : Melvin Kuriakose, P. S. Hrishikesh, Densen Puthussery, C. V. Jiji
Published in: Advances in Small Satellite Technologies
Publisher: Springer Nature Singapore
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Abstract
Automatic interpretation of remote sensing images is a fundamental but challenging problem in the field of aerial and satellite image analysis. It plays a vital role in a wide range of applications and is receiving significant attention in recent years. Even though many great progress has been made in this field, the detection of multi-scale objects, especially small objects in high-resolution satellite (HRS) and drone images, has not been adequately explored. As a result, detection performance both in terms of detection speed and accuracy turns out to be poor. To address this problem, we propose a convolutional neural network (CNN)-based single-stage object detector for the real-time and accurate recognition of remote sensing images. Our model predicts bounding boxes and corresponding class probabilities directly from images in a single assessment. This will result in a real-time object detection of images without compromising accuracy.