Classification and detection of radiation anomalies in Chinese optical satellite images by integrating multi-scale features

doi:10.6046/zrzyyg.2021377

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Abstract

With the rapid development of China’s aerospace remote sensing industry, the types of Chinese civilian optical remote sensing satellites have continuously increased. Consequently, the data volume of optical images shows a leapfrogging growth. This brings huge challenges to the daily quality inspection of the calibration products for optical remote sensing image sensors. The inspection of image radiation anomalies is a key step in image quality inspection. However, the inspection faces many problems such as a lack of automated inspection technical capabilities, high manual participation, and low efficiency. To address the above problems, this study proposed a deep learning network model that integrates multi-scale features for the classification and detection of radiation anomaly data. This network model employed a hollow space convolutional pooling pyramid based on the EfficientNet-B0 model. The features of radiation anomaly data on different scales were collected by setting different expansion rates and then processed through channel splicing, pooling, and convolution. Furthermore, they were merged with the features extracted using the EfficientNet-B0 model to improve the precision of the classification and detection model. The experimental results show that the proposed classification and detection model has a higher classification precision for the detection and classification of radiation anomaly data of optical images than other models. Therefore, this study will help to improve the automation level of radiation quality inspection of remote sensing images.

Keywords EfficientNet deep learning radiation anomaly classification detection

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Issue Date: 27 December 2022

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	Hai TAN
	Rongjun ZHANG
	Wenfeng FAN
	Yifang ZHANG
	Hang XU

Cite this article:

Hai TAN,Rongjun ZHANG,Wenfeng FAN, et al. Classification and detection of radiation anomalies in Chinese optical satellite images by integrating multi-scale features[J]. Remote Sensing for Natural Resources, 2022, 34(4): 97-104.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021377 OR https://www.gtzyyg.com/EN/Y2022/V34/I4/97

Fig.1 Examples of CCD stitching problem data

Fig.2 Examples of garbled problem data

Fig.3 Examples of tap problem data

Fig.4 Examples of missing problem data

Fig.5 Examples of color cast problem data

Fig.6 EfficientNet-B0 structure diagram

Fig.7 Improved EfficientNet-B0 network model

Fig.8 Receptive field size of dilated convolution

Fig.9 Change curve of overal classification accuracy and loss value during training

Tab.1 Comparison of classification accuracy of different models for different problems

Tab.2 Comprehensive performance comparison of different classification models

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