Target recognition in Millimeter-wave Interferometric Synthetic Aperture Radiometer (MMW InSAR) imaging is always a crucial task. However, the recognition performance of conventional algorithms degrades when facing unpredictable noise interference in practical scenarios and information-loss caused by inverse imaging processing of InSAR. These difficulties make it very necessary to develop general-purpose denoising techniques and robust feature extractors for InSAR target recognition. In this paper, we propose a denoising convolutional neural network (D-CNN) and demonstrate its advantage on MMW InSAR automatic target recognition problem. Instead of directly feeding the MMW InSAR image to the CNN, the proposed algorithm utilizes the visibility function samples as the input of the fully connected denoising layer and recasts the target recognition as a data-driven supervised learning task, which learns the robust feature representations from the space-frequency domain. Comparing with traditional methods which act on the MMW InSAR output images, the D-CNN will not be affected by information-loss accused by inverse imaging process. Furthermore, experimental results on the simulated MMW InSAR images dataset illustrate that the D-CNN has superior immunity to noise, and achieves an outstanding performance on the recognition task.

Object detection in millimeter-wave Interferometric Synthetic Aperture Radiometer (InSAR) imaging is always a crucial task. Facing unpredictable and numerous objects, traditional object detection models running after the InSAR system accomplishing imaging suffer from disadvantages such as complex clutter backgrounds, weak intensity of objects, Gibbs ringing, which makes a general purpose saliency detection system for InSAR necessary. This letter proposes a spectrum-based saliency detection algorithm to extract the salient regions from unknown backgrounds cooperating with sparse sensing InSAR imaging procedure. Directly using the interferometric value and sparse information of scenes in the basis of the Discrete Cosine Transform (DCT) domain adopted by InSAR imaging procedure, the proposed algorithm isolates the support of saliency region and then inversely transforms it back to calculate the saliency map. Comparing with other detecting algorithms which run after accomplishing imaging, the proposed algorithm will not be affected by information-loss accused by imaging procedure. Experimental results prove that it is effective and adaptable for millimeter-wave InSAR imaging.

We propose a progressive transform-based phase unwrapping (PU) technique that employs a recursive structure. Each stage, which is identical with others in the construction, performs PU by FFT method that yields a solution and a residual phase error as well. The residual phase error is then reprocessed by the following stages. This scheme effectively improves the gradient estimate of the noisy wrapped phase image, which is unrecoverable by conventional global PU methods. Additionally, by incorporating computational strength of the transform PU method in a recursive system, we can realize a progressive PU system for prospective near real-time topographic-mapping radar and near real-time medical imaging system (such as MRI thermometry and MRI flow imager). PU performance of the proposed system and the conventional PU methods are evaluated by comparing their residual error quantitatively with a fringe-density-related error metric called FZX (fringe's zero-crossing) number. Experimental results for simulated and real InSAR phase images show significant, progressive improvement over conventional ones of a single-stage system, which demonstrates the high applicability of the proposed method.

Two-dimensional phase unwrapping (PU) process usually causes a noise-induced distortion in the geographical information of a wrapped phase image obtained by, for example, interferometric synthetic aperture radar (InSAR). This paper presents a novel method to reduce the phase-unwrapping distortion by being based on two-dimensional fractional Brownian motion (fBm) theory. The method incorporates fractal geometry estimation with conventional global-transform PU. For the spatial-frequency spectrum of an observed phase image, we estimate the fractal dimension by assuming an almost constant dimension over the image. Then, according to the estimation, we compensate the distorted spectrum of the tentatively computed global PU result. We obtain a better topographical map as the inverse Fourier transform of the compensated spectrum. It is demonstrated that the proposed method increases the signal-to-noise ratio of PU results for simulated data with various noise levels. Evaluations on an actual InSAR phase image also show that the method significantly improves the quality of the conventional global-transform PU result, in particular in its fine structure.

Complex-valued region-based-coupling image clustering (continuous soft segmentation) neural networks are proposed for interferometric radar image processing. They deal with the amplitude and phase information of radar data as a combined complex-amplitude image. Thereby, not only the reflectance but also the distance (optical length) are consistently taken into account for the clustering process. A continuous complex-valued label is employed whose structure is the same as that of input raw data and estimation image. Experiments demonstrate successfully the clustering operations for interferometric synthetic aperture radar (InSAR) images. The method is applicable also to future radar systems for image acquisition in, e.g., invisible fire smoke places and intelligent transportation systems by generating a processed image more recognizable by human and automatic recognition machine.