A spatial-spectral feature based target detection framework for high‑resolution HSI

ABSTRACT

Due to the development of hyperspectral image (HSI) technology, the high-resolution hyperspectral image (HRHSI) in remote sensing is becoming widely used. Compared to traditional HSI, HRHSI has extremely high resolution in both spatial and spectral domains. It contains more texture and spectral information than the low-resolution HSI (LRHSI), which can improve the target detection performance of HSI. However, the majority of the existing automatic target detection methods are only applicable to LRHSI. Therefore, this paper brings forward to a spatial-spectral feature-based target detection framework for HRHSI. First, a two-channel residual network is proposed, which aims to learn jointly spatial-spectral features from the spectral domain and spatial domain of HRHSI. Second, a spatial-spectral feature space is constructed to describe the distribution of the spatial-spectral feature of HRHSI, which can overcome the limitation of the number of training samples. A combined loss function is used to minimize within-class differences and maximize between-class distance in the spatial-spectral feature space. Finally, the detection map is received in the spatial-spectral feature space by calculating the Mahalanobis Distance and analysing the credibility of the target. The experimental results show that our algorithm achieves better target detection accuracy when the number of training samples is limited.

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Nos. 61771319, 61871154), Natural Science Foundation of Guangdong Province (Nos. 2017A030313343, 2019A1515011307), Shenzhen Science and Technology Project (No. JCYJ20180507182259896) and the other project (No. WDZC20195500201) .

Conflicts of interest

The authors declare no conflicts of interest and informed consent was obtained from all the individual participants and co-authors.

Additional information

Funding

This work was supported by the Natural Science Foundation of China [61771319,61871154]; Natural Science Foundation of Guangdong Province [2017A030313343,2019A1515011307]; the other project [WDZC20195500201]; Shenzhen Science and Technology Project [JCYJ20180507182259896, 20200826154022001]; Innovation fund for postgraduate of Shenzhen University [315-0000470829].