Robust 3D ISAR Ship Classification
- Resource Type
- Conference
- Authors
- Pui, Chow Yii; Ghio, Selenia; Ng, Brian; Giusti, Elisa; Rosenberg, Luke; Martorella, Marco
- Source
- 2023 IEEE Radar Conference (RadarConf23) Radar Conference (RadarConf23), 2023 IEEE. :1-6 May, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Fields, Waves and Electromagnetics
Robotics and Control Systems
Signal Processing and Analysis
Point cloud compression
Three-dimensional displays
Databases
Training data
Radar
Feature extraction
Libraries
- Language
Classification of inverse synthetic aperture radar (ISAR) ship imagery is difficult due to the motion of the sea causing a wide variation of the observed images. The three dimensional (3D)-ISAR technique was developed as an alternative representation with the target represented by a 3D point cloud. In this paper, we compare two different approaches to classification using 3D-ISAR point clouds of maritime targets. The first approach makes use of features extracted from the 3D-ISAR generated point cloud of the target from different perspectives (i.e. side, top and front views) to form three point density images (PDI). These are then fed into a convolutional neural network (CNN) to classify the targets. The second approach uses an offline target database comprising size and a coarse target silhouette, with classification performed using a few simple rules. Both algorithms do not require knowledge of the aspect angle making them robust when applied in an operational scenario.