Remote Sensing Building Detection Based on Binarized Semantic Segmentation

Convolution type	Input	Kernel size	Stride	Padding	Output
BinaryConv2D	256×256×3	3×3×64	1	Same	256×256×64
BinaryConv2D	256×256×64	3×3×64	1	Same	256×256×64
Pool	256×256×64	2×2	1		128×128×64
BinaryConv2D	128×128×64	3×3×128	1	Same	128×128×128
BinaryConv2D	128×128×128	3×3×128	1	Same	128×128×128
Pool	128×128×128	2×2	1		64×64×128
BinaryConv2D	64×64×128	3×3×256	1	Same	64×64×256
BinaryConv2D	64×64×256	3×3×256	1	Same	64×64×256
Pool	64×64×256	2×2			32×32×256
BinaryConv2D	32×32×256	3×3×512	1	Same	32×32×512
BinaryConv2D	32×32×512	3×3×512	1	Same	32×32×512
Pool	32×32×512	2×2			16×16×512
︙	︙	︙	︙	︙	︙
BinaryConv2D	256×256×128	3×3×64	1	Same	256×256×64
BinaryConv2D	256×256×64	3×3×16	1	Same	256×256×16
Conv2D	256×256×16	1×1×1	1	Same	256×256×1

Table 2. Results of training loss and accuracy of five networks
View table
Table 2. Results of training loss and accuracy of five networks
Algorithm Training loss Training accuracy /%
FU-Net 0.01 99.56
GBU-Net 0.41 87.73
MBU-Net 0.16 93.94
Deeplab 0.01 99.70
ENet 0.23 92.21

Table 3. Test indexes of five networks on test set

View table

Table 3. Test indexes of five networks on test set

Algorithm	${β_{PA}}_{}$ /%	P /%	R /%	${F_{1 ⁃ score}}_{}$ /%	Memory /MB	Time /ms
FU-Net	81.88	77.07	66.20	71.22	96.60	357
GBU-Net	74.74	84.41	54.22	66.03	3.02	47
MBU-Net	82.33	82.76	65.54	73.15	3.02	49
Deeplab	82.03	78.63	67.11	72.41	123.47	489
ENet	78.49	85.37	56.19	67.77	13.73	107

Table 4. Comparison of energy consumption of addition and multiplication operations for different types of parameters^[32]
View table
Table 4. Comparison of energy consumption of addition and multiplication operations for different types of parameters^[32]
Operation Energy consumption /pJ
Multiplication Addition
8 bit integer 0.20 0.03
32 bit integer 3.10 0.10
16 bit floating point 1.10 0.40
32 bit floating point 3.70 0.90

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Tianyou Zhu, Feng Dong, Huixing Gong. Remote Sensing Building Detection Based on Binarized Semantic Segmentation[J]. Acta Optica Sinica, 2019, 39(12): 1228002

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