Remote Sensing Ship Target Detection and Recognition System Based on Machine Learning

• Published in: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium pp. 1272 - 1275
• Main Authors: Zong-ling, LI, Lu-yuan, WANG, Ji-yang, YU, Bo-wen, CHENG, Liang, HAO, Shuai, JIANG, Zhen, LI, Jian-feng, YIN
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: Convolution Neural Network; Image recognition; Machine learning; Marine vehicles; Object detection; Real-time systems; Remote sensing; Target Detection and Recognition; Target recognition; VGG16 Model
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS.2019.8898599

In this paper, the ship target detection and recognition system of remote sensing imaging based on machine learning is designed according to the sparsity of interest targets in optical remote sensing image, and proposes a method of target detection and recognition based on morphological matching and machine learning. The slices of suspected targets are extracted quickly by visual enhancement technology that the amount of data processed is greatly reduced. The target information of interest is extracted in depth and the false alarm rate of detection is greatly reduced by using machine learning method to classify objects. In the system function and performance verification test, the real-time and accuracy index through 227 targets of 32 scenes GF-2 satellite images are tested what can detect and recognize about 20 objects per second. The recall rate of the system is more than 92%, and the efficiency of target detection method based on traditional morphological matching is less than 60% while the target recognition method base on machine learning improves the precision rate to over 97%.