Spatial-Spectral Chromatic Coding of Interference Signatures in SAR Imagery: Signal Modeling and Physical-Visual Interpretation

Synthetic Aperture Radar (SAR) images are conventionally visualized as grayscale amplitude representations, which often fail to explicitly reveal interference characteristics caused by external radio emitters and unfocused signals. This paper proposes a novel spatial-spectral chromatic coding method for visual analysis of interference patterns in single-look complex (SLC) SAR imagery. The method first generates a series of spatial-spectral images via spectral subband decomposition that preserve both spatial structures and spectral signatures. These images are subsequently chromatically coded into a color representation using RGB/HSV dual-space coding, using a set of specifically designed color palette. This method intrinsically encodes the spatial-spectral properties of interference into visually discernible patterns, enabling rapid visual interpretation without additional processing. To facilitate physical interpretation, mathematical models are established to theoretically analyze the physical mechanisms of responses to various interference types. Experiments using real datasets demonstrate that the method effectively highlights interference regions and unfocused echo or signal responses (e.g., blurring, ambiguities, and moving target effects), providing analysts with a practical tool for visual interpretation, quality assessment, and data diagnosis in SAR imagery.

Principal Component Maximization: A Novel Method for SAR Image Formation from Raw Data without System Parameters

Synthetic aperture radar (SAR) imaging traditionally requires precise knowledge of system parameters to implement focusing algorithms that transform raw data into high-resolution images. These algorithms require knowledge of SAR system parameters, such as wavelength, center slant range, fast time sampling rate, pulse repetition interval (PRI), waveform parameters (e.g., frequency modulation rate), and platform speed. This paper presents a novel framework for recovering SAR images from raw data without the requirement of any SAR system parameters. Firstly, we introduce an approximate matched filtering model that leverages the inherent shift-invariance properties of SAR echoes, enabling image formation through an adaptive reference echo estimation. To estimate this unknown reference echo, we develop a principal component maximization (PCM) technique that exploits the low-dimensional structure of the SAR signal. The PCM method employs a three-stage procedure: 1) data block segmentation, 2) energy normalization, and 3) principal component energy maximization across blocks, effectively handling non-stationary clutter environments. Secondly, we present a range-varying azimuth reference signal estimation method that compensates for the quadratic phase errors. For cases where PRI is unknown, we propose a two-step PRI estimation scheme that enables robust reconstruction of 2-D images from 1-D data streams. Experimental results on various SAR datasets demonstrate that our method can effectively recover SAR images from raw data without any prior system parameters.