Mr. Chen Guo Profile

Chen Guo

at Beihang Univ

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Proceedings Article | 26 October 2022 Presentation + Paper

Fast on-board ship detection in panchromatic images based on micro-nano satellite

Jing Qi, Chen Guo, Haopeng Zhang

Proceedings Volume 12267, 1226702 (2022) https://doi.org/10.1117/12.2636229

KEYWORDS: Satellites, Target detection, Remote sensing, Detection and tracking algorithms, Satellite imaging, Convolution, Data processing, Image processing, Data transmission, Visual process modeling

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With the continuous improvement of the number and capability of micro-nano satellites, on-board intelligent data processing becomes a necessary configuration. The constellation with hundreds micro-nano satellites has the ability to high-frequency detection of ship targets and realizes continuous awareness of the global ocean, which is of great significance in maritime rescue, waterway management and combating illegal fishing. In this paper, a fast on-board ship detection method for panchromatic image is proposed. Firstly, GPU (graphics processing unit) of commercial devices is used to form high performance and low power computing capability on the micro-nano satellite. Then, according to the characteristics of ship targets, a convolutional neural network based on lightweight model is designed to quickly obtain accurate number and location information of ship targets. The algorithm deployed on micro-nano satellite can transform massive remote sensing data into target slices, greatly reduce the pressure of satellite-ground data transmission and improve the application efficiency of remote sensing data. We test our method on a dataset of more than 90 panchromatic images. The results show that the detection rate of this algorithm is better than 0.95, and the average processing speed for an image block of 1024 × 1024 pixel is less than 0.2 seconds, which has a wide application prospect.

Proceedings Article | 26 October 2022 Poster + Paper

Weakly supervised ship detection in remote sensing images

Chen Guo, Zhiwen Tan, Meng An, Zhiguo Jiang, Haopeng Zhang

Proceedings Volume 12267, 1226711 (2022) https://doi.org/10.1117/12.2636240

KEYWORDS: Transformers, Remote sensing, Sensors, Chromium, Computer programming, Image processing, Aerospace engineering, Performance modeling

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Ship detection in remote sensing images is important for maritime surveillance. With the rapid development of earth observation technology, high-resolution imaging satellites can provide more observational information. In the face of massive remote sensing data, object-level annotation requires a lot of time and manpower. Weakly supervised object detection is trained using only image-level annotations, thus reducing the requirement for object-level annotations. However, there are still some problems in the detection of weakly supervised ships in remote sensing images, because of the complex, dense distribution and diverse scale characteristics of the ship environment. We propose a weakly supervised object detection method that combines Transformer with weakly supervised learning for ship detection in remote sensing images. First, Proposal Clustering Learning (PCL) for weakly supervised object detection is used as the baseline to detect ships, and the network is continuously refined for better detection performance. Second, the prior location and size information is added to the features of the proposal through the transformer module. This additional information can be used as an important basis for judging whether the proposal is optimal, thereby improving the detection performance. To evaluate the effectiveness of our method, extensive experiments are conducted on a complex dataset of large-scene remote sensing ships. Experimental results show that our method achieves better detection performance than other methods.

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