Small Object Detection in Hyperspectral Images Based on Radial Basis Activation Function

Bofan Wang; Haitao Zhao

doi:10.3788/AOS202141.2311001

Journals >Acta Optica Sinica >Volume 41 >Issue 23 >Page 2311001 > Article

Acta Optica Sinica
Vol. 41, Issue 23, 2311001 (2021)

Small Object Detection in Hyperspectral Images Based on Radial Basis Activation Function

Bofan Wang and Haitao Zhao^*

Author Affiliations

School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

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DOI: 10.3788/AOS202141.2311001 Cite this Article Set citation alerts

Bofan Wang, Haitao Zhao. Small Object Detection in Hyperspectral Images Based on Radial Basis Activation Function[J]. Acta Optica Sinica, 2021, 41(23): 2311001 Copy Citation Text

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Fig. 1. Schematic diagram of the radial basis activation function for spectral screening (RBAF-SS)

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Fig. 2. Attention-based resolution reconstruction network (ABRRN)

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Fig. 3. Radial basis object output network (RBOON)

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Fig. 4. Examples of hyperspectral data sets. (a) Small objects; (b) medium objects

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Fig. 5. Qualitative analysis of spectral selection based on RBAF

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Fig. 6. Spectral curve of airplane object features

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Fig. 7. Experimental results of the proposed method are compared with four approaches with sigmoid based object output layers. (a) Faster RCNN^[1] (ResNet-50); (b) YOLOv3^[25] (Darknet-53); (c) FCOS^[32] (ResNet-50); (d) CenterNet^[24] (ResNet-18); (e) proposed method (ResNet-18)

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Fig. 8. False alarm rate of different approaches under different IoU threshold

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Stage	Sub-network	Block	Layer details				Output shape /(pixel×pixel×pixel)
Input	-	-	-		-		384×192×25
1	ABRRN	-	Deconv		Conv+ReLUConvRBAF-SSGAP		768×384×6
	ABRRN	-	Element-wise multiplicationSame as the above ABRRN				1536×768×3
2	ResNet-18backbone	Down sampling	Conv+BN+ReLU				384×192×64
		Down sampling	Max pooling				384×192×64
		ResBlock(no down sampling)	Conv+BN+ReLUConv+BN		Identity		384×192×64
		ResBlock(no down sampling)	Element-wise Addition				384×192×64
		ResBlock(down sampling)	Conv+BN+ReLUConv+BN		Conv+BN		192×96×128
		ResBlock(down sampling)	Element-wise addition				192×96×128
		ResBlock(no down sampling)	Same as the above ResBlock(no down sampling)				192×96×128
		ResBlock(down sampling)	Same as the above ResBlock(down sampling)				96×48×256
		ResBlock(no down sampling)	Same as the above ResBlock(no down sampling)				96×48×256
		ResBlock(down sampling)	Same as the above ResBlock(down sampling)				48×24×512
		Up sampling block	DCN+BN+ReLUDeconv+BN+ReLU				96×48×256
3	Up samplingnetwork	Up sampling block	Same as the above upsampling block				96×48×128
		Up sampling block	Same as the above upsampling block				96×48×64
4	RBOON	-	Conv+ReLU	Conv+ReLU		Conv+ReLU
			RBAF-SOD	Conv		Conv	96×48×1/2/2

Table 1. Overall structure of the detection network

Methods	AP50 /%			AP50:95 /%			Time /s
Methods	Small	Medium	All	Small	Medium	All	Time /s
Faster RCNN^[1](ResNet-50)	59.4	69.5	62.2	21.8	30.4	24.2	0.044
YOLOv3^[25](Darknet-53)	51.8	67.4	56.2	18.5	28.5	21.3	0.015
FCOS^[32](ResNet-50)	25.0	46.7	31.1	9.0	9.7	8.6	0.041

Table 2. Detection accuracy and ablation experiment

Methods	AP50 /%			AP50:95 /%			Time /s
Methods	Small	Medium	All	Small	Medium	All	Time /s
CenterNet^[24](ResNet-18)	60.2	70.4	63.1	21.5	28.3	22.7	0.011
Proposed-ABRRN(ResNet-18)	64.9	73.8	67.4	23.4	27.0	24.4	0.020
Ours-RBOON(ResNet-18)	60.7	70.6	63.5	21.6	28.4	22.9	0.011
Proposed(ResNet-18)	65.0	74.2	67.6	23.5	26.7	24.4	0.020

Table 3. Ablation experiment

Bofan Wang, Haitao Zhao. Small Object Detection in Hyperspectral Images Based on Radial Basis Activation Function[J]. Acta Optica Sinica, 2021, 41(23): 2311001

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