Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning
- A novel extraction method for urban water bodies based on deep learning is proposed for remote-sensing images. The proposed method combines the superpixel method with deep learning to extract urban water bodies and distinguish shadow from water.
- A new CNN architecture is designed, which can learn the characteristics of water bodies from the input data.
- In order to reduce the loss of image features during the process of pooling, we propose self-adaptive pooling (SAP).
2. Materials and Methods
2.1. Study Areas
2.2. Self-Adaptive Pooling Convolutional Neural Networks (CNN) Architecture
2.3.1. Color Space Transformation
2.3.2. Adaptive Simple Linear Iterative Clustering (A-SLIC) Algorithm
- Step 1. For an image containing pixels, the size of the pre-divided region in this algorithm is , then the number of regions is . Each pre-divided area is labeled as . In this paper, and are defined zero, and is defined one.
- Step 2. HIS transformation is performed on each pre-divided area. In the th region, according to Equation (10), the similarity between two pixels is calculated in turn.
- Step 3. According to Equations (14) and (16), the sum of and is calculated and the iteration begins.
- Step 4. If and no longer change or reach the maximum number of iterations, the iteration is terminated. The point where the sum of and is max is regarded as the cluster center (, where ).
- Step 5. Repeat steps 3 to 4 until the entire image is traversed, and adaptively determine the number of superpixels (). In this paper, the HSI value are the center of the pixel. Finally, complete the superpixel segmentation.
2.4. Network Semi-Supervised Training and Extraction Waters
2.5. Accuracy Assessment Method
- : true positives, i.e., the number of correct extraction pixels;
- : false negatives, i.e., the number of the water pixels not extracted;
- : false positives, i.e., the number of incorrect extraction pixels;
- : true negatives, i.e., the number of no-water bodies pixels that were correctly rejected.
3. Experiments and Discussion
3.1. Impact of the Superpixel Segmentation on the Performance of Water Mapping
3.2. Comparison between Different Model CNN Architectures
3.3. Distinguishing Shadow Ability of Different Methods
3.4. Comparison with Other Water Bodies Extraction Methods
Conflicts of Interest
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|Satellite Parameters||ZY-3 Multispectral Imagery||GF-2 Multispectral Imagery|
|Number of bands||4||4|
|Wavelength (nm)||Blue: 450–520; Green: 520–590||Red: 630–690; NIR: 770–890|
|Spatial resolution (m)||5.8||4|
|Radiometric resolution (bit)||1024||1024|
|Image Name||Parameter||Our Method||SLIC|
|Overall accuracy (OA) (%)||99.29||97.29|
|User’s accuracy (UA) (%)||92.16||93.46|
|ZY-3 multispectral imagery(Beijing)||Producer’s accuracy (PA) (%)||87.19||82.06|
|Edge overall accuracy (EOA) (%)||98.82||96.49|
|Edge omission error (EOE) (%)||0.42||1.39|
|Edge commission error (ECE) (%)||0.76||2.12|
|Image Name||Parameter||Self-Adaptive Pooling + CNN||Max Pooling + CNN||Average Pooling + CNN|
|ZY-3 multispectral imagery(Tianjin)||EOE (%)||0.94||2.63||6.24|
|Method of ||98.27%||89.21%||92.37%||2.61%||1.02%||96.37%|
|Method of ||97.38%||91.32%||88.91%||2.10%||0.83%||97.07%|
|Method of ||97.04%||91.79%||89.37%||3.03%||0.93%||96.04%|
|Method of ||96.21%||90.37%||88.26%||2.95%||1.04%||96.01%|
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Chen, Y.; Fan, R.; Yang, X.; Wang, J.; Latif, A. Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning. Water 2018, 10, 585. https://doi.org/10.3390/w10050585
Chen Y, Fan R, Yang X, Wang J, Latif A. Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning. Water. 2018; 10(5):585. https://doi.org/10.3390/w10050585Chicago/Turabian Style
Chen, Yang, Rongshuang Fan, Xiucheng Yang, Jingxue Wang, and Aamir Latif. 2018. "Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning" Water 10, no. 5: 585. https://doi.org/10.3390/w10050585