Chenxiao Feng, Xili Wang. Convolution-Deconvolution Image Segmentation Model for Fusion Features and Decision[J]. Laser & Optoelectronics Progress, 2019, 56(1): 011008

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- Laser & Optoelectronics Progress
- Vol. 56, Issue 1, 011008 (2019)

Fig. 1. Convolution-deconvolution image segmentation model for fusion features and decision

Fig. 2. Flow charts of data processing. (a) Data processing of CD-FFD; (b) each branch network data processing of CD-FFD

Fig. 3. Segmentation results of CD-FFD model. (a) RGB image; (b) gray image; (c) segmentation result of RGB-Net; (d) segmentation result of GRAY-Net; (e) segmentation result of CD-FFD; (f) ground-truth

Fig. 4. Segmentation results of CD-FFD model. (a) IRRG image; (b) DSM image; (c) segmentation result of IRRG-Net; (d) segmentation result of DSM-Net; (e) segmentation result of CD-FFD; (f) ground-truth

Fig. 5. Segmentation results of other images by CD-FFD model. (a) Original RGB images; (b) segmentationresults of CD-FFD
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Table 1. Evaluation results of CD-FFD on 200 validation images from Weizmann Horse
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Table 2. Evaluation results of 128 test images from Weizmann horse dataset
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Table 3. Evaluation results of 5 test images from vaihigen dataset
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Table 4. Comparison among existing results of 128 test images from Weizmann Horse dataset
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Table 5. Comparison among existing results of 5 test images from vaihigen dataset

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