Feature extraction from satellite images using segnet and fully convolutional networks (FCN)

Year 2020, Volume: 5 Issue: 3, 138 - 143, 01.10.2020

Batuhan Sariturk , Bulent Bayram , Zaide Duran , Dursun Zafer Seker

https://doi.org/10.26833/ijeg.645426

Cited By: 21

Abstract

Object detection and classification are among the most popular topics in Photogrammetry and Remote Sensing studies. With technological developments, a large number of high-resolution satellite images have been obtained and it has become possible to distinguish many different objects. Despite all these developments, the need for human intervention in object detection and classification is seen as one of the major problems. Machine learning has been used as a priority option to this day to reduce this need. Although success has been achieved with this method, human intervention is still needed. Deep learning provides a great convenience by eliminating this problem. Deep learning methods carry out the learning process on raw data unlike traditional machine learning methods. Although deep learning has a long history, the main reasons for its increased popularity in recent years are; the availability of sufficient data for the training process and the availability of hardware to process the data. In this study, a performance comparison was made between two different convolutional neural network architectures (SegNet and Fully Convolutional Networks (FCN)) which are used for object segmentation and classification on images. These two different models were trained using the same training dataset and their performances have been evaluated using the same test dataset. The results show that, for building segmentation, there is not much significant difference between these two architectures in terms of accuracy, but FCN architecture is more successful than SegNet by 1%. However, this situation may vary according to the dataset used during the training of the system.

Keywords

Photogrammetry, Deep Learning, Feature Extraction, SegNet, Fully Convolutional Networks

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