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

TAN Hai; ZHANG Rongjun; FAN Wenfeng; ZHANG Yifang; XU Hang

doi:10.6046/zrzyyg.2021377

2022 Vol. 34, No. 4

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Article navigation > Remote Sensing for Natural Resources > 2022 > 34(4): 97-104

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TAN Hai, ZHANG Rongjun, FAN Wenfeng, ZHANG Yifang, XU Hang. 2022. Classification and detection of radiation anomalies in Chinese optical satellite images by integrating multi-scale features. Remote Sensing for Natural Resources, 34(4): 97-104. doi: 10.6046/zrzyyg.2021377

Citation:

TAN Hai, ZHANG Rongjun, FAN Wenfeng, ZHANG Yifang, XU Hang. 2022. Classification and detection of radiation anomalies in Chinese optical satellite images by integrating multi-scale features. Remote Sensing for Natural Resources, 34(4): 97-104. doi: 10.6046/zrzyyg.2021377

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

1. Land Satellite Remote Sensing Application Center, MNR, Beijing 100048, China；
2. School of Geomatics, Liaoning Technical University, Fuxin 123000, China

Received Date: 10 November 2021

Revised Date: 15 December 2022

Publish Date: 27 December 2022

Abstract

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.
- EfficientNet /
- deep learning /
- radiation anomaly /
- classification detection

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References

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