A two-dimensional microwave radiation imaging method based on data fusion

Two-dimensional microwave imaging radiometers (TDMIR) are potential powerful instruments for high resolution observation of planetary surfaces. The prototype of TDMIR based on minimum-redundancy linear array and mechanical scanning is described. This paper deals with the reconstruction of radiometric brightness temperature maps from TDMIR measurements. Due to the inherent physical properties of passive radiometer imaging and associated hardware, the images by TDMIR have poor contrast and low signal to noise ratio. Standard segmentation algorithms are unable to detect objects. Algorithms for improving the brightness temperature image based on data fusion are presented. First, we remove the noise by subtracting the background, then use CLEAN method to detect object. Experimental results show that the proposed radiometer can give brightness temperature image of natural scenes and improve image quality of the radiometer for object detection.