Abstract
Abstract. Polarimetric synthetic aperture radar (POLSAR) is an advantageous data for information extraction about objects and structures by using the wave scattering and polarization properties. Hyperspectral remote sensing exploits the fact that all materials reflect, absorb, and emit electromagnetic energy, at specific wavelengths, in distinctive patterns related to their molecular composition. As a result of their fine spectral resolution, Hyperspectral image (HIS) sensors provide a significant amount of information about the physical and chemical composition of the materials occupying the pixel surface. In target detection applications, the main objective is to search the pixels of an HSI data cube for the presence of a specific material (target). In this research, a hierarchical constrained energy minimization (hCEM) method using 5 different adjusting parameters has been used for target detection from hyperspectral data. Furthermore, to detect the built-up areas from POLSAR data, building objects discriminated from surrounding natural media presented on the scene using Freeman polarimetric target decomposition (PTD) and the correlation coefficient between co-pol and cross-pol channels. Also, target detection method has been implemented based on the different polarization basis for using the more information. Finally a majority voting method has been used for fusing the target maps. The polarimetric image C-band SAR data acquired by Radarsat-2, over San Francisco Bay area was used for the evaluation of the proposed method.
Highlights
We consider how combination of polarimetric synthetic aperture radar (PolSAR) data and hyperspectral images can be used to enhance the detection of targets
We use the hierarchical constrained energy minimization (hCEM) method with the purpose of improving the performance of traditional constrained energy minimization (CEM) detector
The first to detect the built-up areas from Polarimetric synthetic aperture radar (POLSAR) data, according to the Fig.1., we used the double bounce component of Freeman decomposition
Summary
We consider how combination of polarimetric synthetic aperture radar (PolSAR) data and hyperspectral images can be used to enhance the detection of targets (built-up area). One shortcoming of HSI is that it provides no surface penetration. To overcome these limitations and enhance HSI system performance, we fuse HSI data with PolSAR sensor data. HSI, on the other hand, is capable of subpixel detection and material identification. Both SAR and HSI systems may suffer substantial false-alarm and missed detection rates because of their respective background clutter, but we expect that combining SAR and HSI data will greatly enhance detection and identification performance
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