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Recurrent feedback CNN for water region estimation from multitemporal satellite images
Author(s): Vinayaraj Poliyapram; Nevrez Imamoglu; Ryosuke Nakamura
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Paper Abstract

Water region estimation is considered as one of the fundamental classification tasks in remote sensing. Several previous research works focused on traditional practices such as spectral analysis, and statistical approaches for water region estimation. However, producing a consistent global scale water estimation results are still considered as relatively challenging task. On the other hand, in computer vision applications Convolutional Neural Network (CNN) emerged as greater tool for classification tasks. Recently, Recurrent Convolutional Neural Network(R-CNN) proposed for improved classification results. Therefore, inspired from R-CNN, this research proposes a Recurrent feedback Encoder-Decoder without max-pooling for global scale water region estimation using temporal Landsat-8 images. The proposed R-CNN uses three Landsat-8 images which consist of current observation (t0) to predict water region and two previous observation of the same location (t 􀀀 1, t 􀀀 2), and these three temporal observation of the same location were employed for training with the ground truth labelled data (water/non-water) from the current observation. Proposed R-CNN model uses temporal input data and results in multi-temporal output for water region estimation. Experiments show promising results especially while using concatenated recurrent feedback features. The model significantly outperforms baseline model and UNet (without recurrent and feedback structure). Detailed comparison study on temporal Landsat-8 images that highly affected by sunglint, cloud and other atmospheric conditions shows that the proposed model has a potential to produce reliable water region estimation where UNet, baseline model R-CNN single model fail.

Paper Details

Date Published: 7 October 2019
PDF: 8 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111550T (7 October 2019); doi: 10.1117/12.2533139
Show Author Affiliations
Vinayaraj Poliyapram, AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Lab. (Japan)
National Institute of Advanced Industrial Science and Technology (Japan)
Nevrez Imamoglu, National Institute of Advanced Industrial Science and Technology (Japan)
Ryosuke Nakamura, National Institute of Advanced Industrial Science and Technology (Japan)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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