Laser & Optoelectronics Progress, Volume. 57, Issue 4, 041012(2020)
Remote Sensing Image Segmentation Method Based on Multi-Level Channel Attention
Figures & Tables(13)
Fig. 3. Partial training set (first line) and test set (second line) images in Massachusetts road data set
Fig. 4. Partial training set (first line) and test set (second line) images in Inria aerial image labeling data set
Fig. 5. Depth model segmentation results in Massachusetts road data set. (a) RGB images; (b) ground-truth images; (c) Unet segmentation results; (d) RSIS-MLCA segmentation results
Fig. 6. Training process curves in Massachusetts road data set. (a) xloss curve on training set; (b) RIOU curve on test set
Fig. 7. Depth model segmentation results in Inria aerial image labeling data set. (a) RGB images; (b) ground-truth images; (c) Unet segmentation results; (d) RSIS-MLCA segmentation results
Fig. 8. Training process curves in Inria aerial image labeling data set. (a) xloss curve on training set; (b) RIOU curve on test set
Table 1. Network parameters of RSIS-MLCA
View tableTable 1. Network parameters of RSIS-MLCA
Operation Parameter Input_dimension Out_dimension Kernel_size Stride Padding Image_size Conv_1×2 3 64 3 1 1 256×256 AT_block_1 64 64 256×256 Maxpool_1 64 64 2 2 0 128×128 Conv_2×2 64 128 3 1 1 128×128 AT_block_2 128 128 128×128 Maxpool_2 128 128 2 2 0 64×64 Conv_3×2 128 256 3 1 1 64×64 AT_block_3 256 256 64×64 Maxpool_3 256 256 2 2 0 32×32 Conv_4×2 256 512 3 1 1 32×32 AT_block_4 512 512 32×32 Maxpool_4 512 512 2 2 0 16×16 Conv_5 512 1024 3 1 1 16×16 AT_block_5 1024 1024 16×16 Deconv_6 1024 512 2 2 0 32×32 Conv_6 1024 512 3 1 1 32×32 Deconv_7 512 256 2 2 0 64×64 Conv_7 512 256 3 1 1 64×64 Deconv_8 256 128 2 2 0 128×128 Conv_8 256 128 3 1 1 128×128 Deconv_9 128 64 2 2 0 256×256 Conv_9 128 64 3 1 1 256×256 Conv_10 64 1 1 1 0 256×256
Table 2. Evaluation results for each picture in
Fig. 5 and test set%View tableTable 2. Evaluation results for each picture in
Fig. 5 and test set%Model Image1 Image2 Image3 Image4 Image5 Test set P RIOU P RIOU P RIOU P RIOU P RIOU P RIOU Unet 88.0 70.0 89.9 50.4 87.9 63.0 74.6 41.4 88.5 76.6 83.0 74.6 RSIS-MLCA 89.3 80.9 91.2 83.4 90.1 70.5 81.2 65.6 90.7 80.3 85.2 76.5
Table 3. Evaluation results for each picture in
Fig. 7 and test set%View tableTable 3. Evaluation results for each picture in
Fig. 7 and test set%Model Image1 Image2 Image3 Image4 Image5 Test set P RIOU P RIOU P RIOU P RIOU P RIOU P RIOU Unet 89.3 89.9 90.1 70.2 91.8 76.2 93.3 87.7 81.1 71.2 80.6 71.1 RSIS-MLCA 89.7 91.2 91.4 73.0 94.3 83.8 94.3 89.1 87.3 78.3 85.6 75.5
Table 4. Comparison between existing results and RSIS-MLCA%
View tableTable 4. Comparison between existing results and RSIS-MLCA%
Method P R RF1-score FCN-4s[ 15 ]71.0 66.0 68.4 FCN-no-skip[ 16 ]74.2 74.2 74.2 FCN-8s[ 16 ]76.2 76.2 76.2 SegNet[ 16 ]77.3 76.5 76.8 ELU-SegNet[ 17 ]85.2 73.3 78.8 ELU-SegNet-R[ 17 ]84.7 78.0 81.2 RSIS-MLCA 85.2 81.4 83.2
Table 5. Comparison between existing results and RSIS-MLCA in Inria aerial image labeling data set
View tableTable 5. Comparison between existing results and RSIS-MLCA in Inria aerial image labeling data set
Method RIOU/% FCN[ 18 ]53.96 FCN-Skip[ 18 ]63.17 FCN-MLP[ 18 ]64.88 Mask R-CNN[ 19 ]59.37 RiFCN[ 20 ]74.49 RSIS-MLCA 75.53
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Shuai Yu, Xili Wang. Remote Sensing Image Segmentation Method Based on Multi-Level Channel Attention[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041012
Paper Information
Category: Image Processing
Received: Jun. 22, 2019
Accepted: Aug. 12, 2019
Published Online: Feb. 20, 2020
The Author Email: Xili Wang (wangxili@snnu.edu.cn)
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