This paper presents a new paradigm for feature extraction and segmentation of SAR imagery. Most of the existing segmentation algorithms explore the features based on the variations in image intensity, contrast and texture, mimicking human SAR scene analysts. Like medical ultrasound imaging, CT imaging and magnetic resonance imaging, the imaging modality of SAR is not consistent with the natural ability of human vision. That is why we need trained experts to analyze those medical images as well as SAR images. In the ATR application, SAR imagery will be processed and segmented by automatic computer algorithms without human analysts in the loop. Therefore, in order to fully utilize the capability of SAR as an advanced surveillance instrument, we need to develop a feature space that is based on the physics of SAR imaging modality, not the human visual perception. After the definition of feature space, we can process the SAR sensor data in the image domain or even before image formation. In this research, we try to focus on establishing a new SAR image segmentation processing paradigm based on the discrete frame theory. We will show the framework of the paradigm on a limited feature space covering some SAR attributes like targets and shadows. After setting up the feature space, we will develop a discrete frame to transform SAR sensor data into a feature space representation. The feature space representation consists of transform coefficients that indicate the location and strength of the features. Those transform coefficients can be further manipulated by some classification algorithms for ATR exploitation.