Onboard target detection in hyperspectral image based on deep learning with FPGA implementation

Onboard target detection of Hyperspectral Imagery (HSI) is widely adopted in the field of remote sensing. It requires high detection accuracy and low computational complexity for processing a large volume of HSI data. In this study, a Locally Preserving Discriminative Broad Learning (LPDBL) was introduced for target detection due to its simple, excellent generalization ability, and its competitive performance. The detection was done through spatial-spectral information, band selection, and estimation of the covariance matrix. The fisher discriminant method was used to reduce the dimension of HSI data. Weights was adjusted through manifold regularization in order to enhance the detection ability of the proposed method. To study the performance of the proposed LPDBL, experiment was conducted on two different datasets of HSI. The results revealed that the proposed method performed better and suitable for target detection. The LPDBL was implemented on Virtex-7 Field Programmable Gate Array (FPGA) board. Furthermore, the LPDBL technique was practically validated by two different techniques such as a broad learning system (BLS) and Automatic Target Detection in HSI (ATD-HSI). The result obtained from the FPGA was very close to the actual target position.