Multi-Source Remote Sensing Image Fusion for Ship Target Detection and Recognition

Jinming Liu,Yu Wang,Hao Chen

doi:10.3390/rs13234852

Abstract

The active recognition of interesting targets has been a vital issue for remote sensing. In this paper, a novel multi-source fusion method for ship target detection and recognition is proposed. By introducing synthetic aperture radar (SAR) sensor images, the proposed method solves the problem of precision degradation in optical remote sensing image target detection and recognition caused by the limit of illumination and weather conditions. The proposed method obtains port slice images containing ship targets by fusing optical data with SAR data. On this basis, spectral residual saliency and region growth method are used to detect ship targets in optical image, while SAR data are introduced to improve the accuracy of ship detection based on joint shape analysis and multi-feature classification. Finally, feature point matching, contour extraction and brightness saliency are used to detect the ship parts, and the ship target types are identified according to the voting results of part information. The proposed ship detection method obtained 91.43% recognition accuracy. The results showed that this paper provides an effective and efficient ship target detection and recognition method based on multi-source remote sensing images fusion.

Highlights

It can be seen from the above test results, compared with the single sensor optical image target detection and heterogeneous support tensor machine (HSTM) and adaptive heterogeneous support tensor machine (AHSTM) [31], that each task performance of the proposed ship target detection method that is based on joint shape analysis and further characteristics of multi-source remote sensing image classification has been greatly improved, and the false detection of targets has been reduced
A ship target detection and recognition method based on multi-source remote sensing image fusion was proposed
For the ship detection errors caused by the complex optical image port environment and serious interference of the ship target shadow in the port slice image, the paper proposed a ship target detection method based on joint shape analysis and multi-feature classification and the ship target in scene was successfully detected

Summary

Introduction

It is a challenge to accurately detect and identify targets from high-resolution remote sensing images in a timely manner. The advancement of high temporal and spatial resolution data and multi-source data fusion technology has provided unprecedented opportunities for remote sensing information application fields. Optical data and SAR data are the two most common data types in the field of satellite remote sensing; the two sensors have different advantages in Earth observation due to the different imaging mechanisms. Optical remote sensing images can intuitively reflect texture, color, shape and other information to users, but the ability of data acquisition is limited due to the limitation of light and weather [3].

Methods

Results

Discussion

Conclusion