Target Detection

In target detection, it is normally assumed that the ground truth signature, collected in laboratory environment, of a target is available and we can then use it to search for targets in a given hyperspectral image. However, directly applying the ground truth signature to the real data is not appropriate due to the environmental differences between the ground truth data and real data. The environmental factors include, but not limited to, illumination, seasonal changes, temperature differences, and sensor characteristics (sensor in laboratory and sensor in use). By compensating for the environmental factors, the target detection performance could be improved significantly. We developed a Hybrid ISAC (H-ISAC) method to automatically adapt laboratory target signature to out-door environments. Our H-ISAC method uses a training image to establish a mapping between ground truth signature domain and testing image domain. Real data experimental results showed that the H-ISAC algorithm provides excellent compensation to environmental effects. After compensation, the performance of target detection using receiver operating characteristics (ROC) is significantly improved.

Original Signatures and Compensated Signatures

'lab' indicates the signature collected in laboratory environment. 'nov' indicates the ground truth real signature in November. 'lab2nov1' and 'lab2nov2' indicates two compensated signatures

Comparison of Target Detection Performance

cmp1 and cmp2 are results using compensated signature and cmp-merge is the result using m

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