APPLICATION OF SENSOR FUSION TO IMPROVE UAV IMAGE CLASSIFICATION
- CRC Laboratory in Advanced Geomatics Image Processing, Department of Geodesy and Geomatics Engineering, University of New Brunswick, 15 Dineen Dr, P.O. Box 4400, Fredericton, New Brunswick, CANADA E3B 5A3
Keywords: Image Fusion, UNB Pansharp, Image Classification, UAV Imagery
Abstract. Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.