Representation Learning for Contextual Object and Region Detection in Remote Sensing

Date

2014-08-28

Author

Firat, Orhan
Can, Gulcan
Yarman Vural, Fatoş Tunay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The performance of object recognition and classification on remote sensing imagery is highly dependent on the quality of extracted features, amount of labelled data and the priors defined for contextual models. In this study, we examine the representation learning opportunities for remote sensing. First we attacked localization of contextual cues for complex object detection using disentangling factors learnt from a small amount of labelled data. The complex object, which consists of several sub-parts is further represented under the Conditional Markov Random Fields framework. As a second task, end-to-end target detection using convolutional sparse auto-encoders (CSA) using large amount of unlabelled data is analysed. Proposed methodologies are tested on complex airfield detection problem using Conditional Random Fields and recognition of dispersal areas, park areas, taxi routes, airplanes using CSA. The method is also tested on the detection of the dry docks in harbours. Performance of the proposed method is compared with standard feature engineering methods and found competitive with currently used rule-based and supervised methods.

Subject Keywords

Feature extraction, Vectors, Remote sensing, Object detection, Image resolution, Airplanes, Training

URI

https://hdl.handle.net/11511/43878

DOI

https://doi.org/10.1109/icpr.2014.637

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

O. Firat, G. Can, and F. T. Yarman Vural, “Representation Learning for Contextual Object and Region Detection in Remote Sensing,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43878.