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Conference Paper
A Method for Reducing Scanning Error Rate of LIDAR using a Galvanometer
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- Authors
- Gyudong Choi, Munhyun Han, Hongseok Seo, Bongki Mheen
- Issue Date
- 2019-10
- Citation
- SPIE Remote Sensing 2019 (SPIE 11155), pp.1-9
- Publisher
- SPIE
- Language
- English
- Type
- Conference 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 aected 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.
- KSP Keywords
- Baseline model, CNN model, Comparison study, Computer Vision(CV), Convolution neural network(CNN), Encoder and Decoder, LANDSAT-8, Labelled Data, Max-pooling, Proposed model, R-CNN