Synthetic Aperture Radar Image Compression Research Articles

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model.

SAR Image Compression Based on Multi-Resblock and Global Context

The synthetic aperture radar(SAR) image is widely used in many remote sensing applications. In order to store and transmit the increasing SAR image data, more efficient compression algorithms are needed. The purpose of this paper is to introduce a new framework for compressing SAR images. Firstly, we propose a novel analysis and synthesis transform based on multi-Resblocks for transforming the original SAR image into a compact latent representation. Then, a Gaussian mixture model is used to estimate the latent representation’s distribution. In order to explore the redundancy within the latent representation, the entropy model parameter is estimated by combining the local context, global context, and hyperprior information. In order to evaluate the performance of the proposed algorithm, we conduct experiments on a dataset of SAR images. The results show that the proposed algorithm outperforms JPEG2000 and some state-of-the-art learned image compression schemes in terms of compression performance.

SAR Image Compression Using Discretized Gaussian Adaptive Model and Generalized Subtractive Normalization

Synthetic aperture radar (SAR) image compression plays an important role in the manipulation of images. However, existing optical compression methods cannot properly handle SAR compression due to the absence of feature learning and representation for SAR images. In this letter, we propose an end-to-end trainable model to effectively fit the feature distribution and reduce information dependencies toward SAR image compression. To better parameterize the distribution of latent codes, a discretized Gaussian adaptive model is designed to achieve a flexible entropy process. To further remove the remaining redundancies, generalized subtractive normalization is introduced to reduce the statistical dependencies in SAR images. Extensive experiments show that the proposed compression method outperforms the traditional compression methods and learning-based algorithms on both the ICEYE and Sandia datasets.

Synthetic Aperture Radar Image Compression Based on a Variational Autoencoder

Given the uniqueness of synthetic aperture radar (SAR) images, traditional optical image compression algorithms cannot fully exploit their redundant information. To improve SAR image compression in terms of rate–distortion performance and visual perception, an end-to-end SAR image compression convolutional neural network (CNN) model based on a variational autoencoder is proposed. The proposed CNN model consists of a main autoencoder and a hyper autoencoder. To reduce dependencies in latent space, a joint transform of linear CNN and nonlinear generalized divisive normalization (GDN) activation is applied in the main autoencoder. Moreover, residual blocks are combined with the transforms to boost the efficiency of feature learning and make use of subpixels to improve the quality of reconstructed images. Instead of a fixed entropy model, a conditioned entropy model that works with a hyperprior network is used to learn the distribution of latents, which helps to further improve the compression quality. During training, the model is optimized by evaluating the rate–distortion performance. The experimental results show that the proposed method can achieve better distortion performance than JPEG, JPEG2000, and the available CNN-based method in terms of objective evaluation criteria and human vision perception quality.

Drone SAR Image Compression Based on Block Adaptive Compressive Sensing

In this paper, an adaptive block compressive sensing (BCS) method is proposed for compression of synthetic aperture radar (SAR) images. The proposed method enhances the compression efficiency by dividing the magnitude of the entire SAR image into multiple blocks and subsampling individual blocks with different compression ratios depending on the sparsity of coefficients in the discrete wavelet transform domain. Especially, a new algorithm is devised that selects the best block measurement matrix from a predetermined codebook to reduce the side information about measurement matrices transferred from the remote sensing node to the ground station. Through some modification of the iterative thresholding algorithm, a new clustered BCS recovery method is proposed that classifies the blocks into multiple clusters according to the compression ratio and iteratively reconstructs the SAR image from the received compressed data. Since the blocks in the same cluster are concurrently reconstructed using the same measurement matrix, the proposed structure mitigates the increase in computational complexity when adopting multiple measurement matrices. Using existing SAR images and experimental data obtained by self-made drone SAR and vehicular SAR systems, it is shown that the proposed scheme provides a good tradeoff between the peak signal-to-noise ratio and the computational load compared to conventional BCS-based compression techniques.

Synthetic aperture radar image compression based on multi-scale geometric transforms

Image representation in separable orthogonal basis cannot take advantage of geometrical regularity contained in basic images. When, explored efficiently geometrical regularity improves image compression. In this paper, we propose to experiment and compare an adaptive multi-scale geometric decomposition for synthetic aperture radar (SAR) image compression, called multi-scale bandelet transform, and a non-adaptive multi-scale geometric representation called ridgelet transform. The second generation of bandelet transform adopted in this work, is constructed in discrete domain with bandeletisation of warped wavelet transform along the optimal direction of geometric flow that minimises the Lagrangian. We discuss the criteria and results to assess SAR image compression performances using wavelet, bandelet, and ridgelet transforms. Our experiments revealed that during the compression phase, the speckle noise is removed from the SAR images inducing further improvements of the coding efficiency. In order, to evaluate the robustness of bandelet transform, we have proposed a progressive compression scheme based on the second generation of bandelet transform combined to SPIHT encoder, which is generally integrated with the wavelet transform.

Synthetic aperture radar image compression based on multi-scale geometric transforms

Synthetic aperture radar image compression based on multi-scale geometric transforms

Complex‐valued interferometric inverse synthetic aperture radar image compression base on compressed sensing

Complex-valued interferometric inverse synthetic aperture radar (InISAR) image compression is discussed in this study. The target scene has its continuity and is compressible. However, because of the random phase of each resolution cell, the frequency spectrum of an ISAR image is wide and the complex-valued image is hard to compress. A complex-valued ISAR image compression approach is proposed. Using two or more antennas and interferometry processing, the random phase of image pixel can be cancelled and the frequency spectrum becomes sparse. Therefore the theory of compressed sensing can be introduced to the process of the complex-valued image compression. Hence, the complex-valued InISAR image compression and reconstruction can be completed. Results on real data are presented to validate the method. In comparison with results of the conventional compression techniques, the proposed method shows the better ability to preserve both the imaging magnitude and interferometric phase.

SAR Image Compression and Reconstruction Based on Compressed Sensing

The appearance of multiplicative speckle noise in Synthetic Aperture Radar (SAR) imagery makes it very difficult to visualization and compression. This paper proposes a novel framework of Compressed Sensing (CS) to compress the SAR image. It combined the Nonsubsampled Contourlet Transform (NSCT) shrinkage method to SAR image sparse represent. And a modified Smoothed L0 norm (SL0) algorithm is used for SAR image accurate reconstruction. The experimental results show the proposed method is very effective and can get better reconstruction performances.

Perceptual quality assessment of SAR image compression

Image quality assessment (IQA) is of fundamental importance for image compression applications. Traditional IQA measures for synthetic aperture radar (SAR) image compression do not consider the properties of the human visual system (HVS). Since human beings are still the final users in most SAR image applications, the objective evaluation coordinate to human perception is the most acceptable and practical IQA method. In this article, we carried out an extensive subjective quality assessment experiment in which four different SAR image compression techniques and a total of 300 test images were estimated by 100 human observers. The test result provides a straightforward comparison of these four compression methods. Then the performances of seven classical objective IQA algorithms were validated based on obtained subjective evaluations. Moreover, we propose a novel objective approach by taking into account the HVS and SAR image characteristics. Experimental results demonstrate that the proposed metric correlates well with human perception for SAR image compression.

SAR Image Compression Using Multiscale Dictionary Learning and Sparse Representation

In this letter, we focus on a new compression scheme for synthetic aperture radar (SAR) amplitude images. The last decade has seen a growing interest in the study of dictionary learning and sparse representation, which have been proved to perform well on natural image compression. Because of the special techniques of radar imaging, SAR images have some distinct properties when compared with natural images that can affect the design of a compression method. First, we introduce SAR properties, sparse representation, and dictionary learning theories. Second, we propose a novel SAR image compression scheme by using multiscale dictionaries. The experimental results carried out on amplitude SAR images reveal that, when compared with JPEG, JPEG2000, and a single-scale dictionary-based compression scheme, the proposed method is better for preserving the important features of SAR images with a competitive compression performance.

A Novel CS Model and Its Application in Complex SAR Image Compression

In this paper, we consider the optimization of measurement matrix in Compressed Sensing (CS) framework. Based on the boundary constraint, we propose a novel algorithm to make the “mutual coherence” approach a lower bound. This algorithm is implemented by using an iterative strategy. In each iteration, a neighborhood interval of the maximal off-diagonal entry in the Gram matrix is scaled down with the same shrinkage factor, and then a lower mutual coherence between the measurement matrix and sparsifying matrix is obtained. After many iterations, the magnitudes of most of off-diagonal entries approach the lower bound. The proposed optimization algorithm demonstrates better performance compared with other typical optimization methods, such as t-averaged mutual coherence. In addition, the effectiveness of CS can be used for the compression of complex synthetic aperture radar (SAR) image is verified, and experimental results using simulated data and real field data corroborate this claim.

Complex SAR Image Compression Based on Directional Lifting Wavelet Transform With High Clustering Capability

We propose two synthetic aperture radar (SAR) complex image compression schemes based on DLWT_IQ and DLWT_FFT. DLWT_IQ encodes the real parts and imaginary parts of the images using directional lifting wavelet transform (DLWT) and bit plane encoder (BPE), while DLWT_FFT encodes the real images converted by fast Fourier transform (FFT). Compared with discrete wavelet transform-IQ (DWT_IQ), DLWT_IQ improves the peak signal-to-noise ratio (PSNR) up to 1.28 dB and reduces the mean phase error (MPE) up to 21.74%; and compared with DWT_FFT, DLWT_FFT improves the PSNR up to 1.22 dB and reduces the MPE up to 20.32%. Moreover, the proposed schemes increase the PSNR up to 3.34 dB and decrease the MPE up to 50.43% as compared with the set partitioning in hierarchical trees (SPIHT) algorithm. In addition to this, we observe a novel phenomenon, that is, DLWT with direction prediction achieves a higher clustering capability for complex SAR images than DWT. Then, coding algorithm based on DLWT requires fewer coding bits than DWT for the same number of coding coefficients, and DLWT outperforms DWT in terms of rate-distortion performance even if the K-term nonlinear approximation of DWT is better than that of DLWT.

Multi-polarimetric SAR image compression based on sparse representation

The use of sparse representation in signal and image processing has gradually increased over the past few years. Obtaining an over-complete dictionary from a set of signals allows us to represent these signals as a sparse linear combination of dictionary atoms. By considering the relativity among the multi-polarimetric synthetic aperture radar (SAR) images, a new compression scheme for multi-polarimetric SAR image based sparse representation is proposed. The multilevel dictionary is learned iteratively in the 9/7 wavelet domain using a single channel SAR image, and the other channels are compressed by sparse approximation, also in the 9/7 wavelet domain, followed by entropy coding of the sparse coefficients. The experimental results are compared with two state-of-the-art compression methods: SPIHT (set partitioning in hierarchical trees) and JPEG2000. Because of the efficiency of the coding scheme, our method outperforms both SPIHT and JPEG2000 in terms of peak signal-to-noise ratio (PSNR) and edge preservation index (EPI).

A Synthetic Aperture Radar Image Compression Algorithm Based on Compressed Sensing

A Synthetic Aperture Radar Image Compression Algorithm Based on Compressed Sensing

Synthetic aperture radar image compression using tree-structured edge-directed orthogonal wavelet packet transform

Currently, wavelet-based coding algorithms are popular for synthetic aperture radar (SAR) image compression, which is very important for reducing the cost of data storage and transmission in relatively slow channels. However, standard wavelet transform is limited by spatial isotropy of its basis functions that is not completely adapted to represent image entities like edges or textures, which means wavelet-based coding algorithms are suboptimal to image compression. In this paper, a novel tree-structured edge-directed orthogonal wavelet packet transform is proposed for SAR image compression. Inspired by the intrinsic geometric structure of images, the new transform improves the performance of standard wavelet by filtering along the regular direction first and then along the orthogonal direction with directional lifting structure. The cost function of best basis selection is designed by textural and directional information for tree-structured edge-directed orthogonal wavelet packet transform. The new transform including speckle reduction can be used to construct SAR image coder with the embedded block coding with optimal truncation for transform coefficients, and arithmetic coding for additional information. The experimental results show that the proposed approach outperforms JPEG2000 and Fast wavelet packet (FWP), both visually and item of PSNR values.

Applications of Compressed Sensing for SAR Moving-Target Velocity Estimation and Image Compression

Compressed sensing (CS) is a recently introduced concept that enables the recovery of signals sampled below the Nyquist rate. A prerequisite for its application is the sparsity of the concerned signals in a certain basis. The concept has been applied to medical imaging, seismic imaging, optical remote sensing, and, recently, radar. This paper describes two possible applications of CS for synthetic aperture radar (SAR): 1) estimation of moving-target velocities in the case of high and low signal-to-clutter ratios, as well as more than one scatterer in a single pixel making use of clutter cancellation, and 2) online compression of processed SAR images with the help of curvelet sparsity-promoting offline decoding. CS for SAR offers the possibility of using lesser amount of data, as well as a convenient way of knowing motion and position parameters of multiple moving targets. The CS technique is useful for simplification of hardware or reduction of data that are useful in the case of limited onboard data storage capacity. The results are demonstrated by means of numerical experiments using simulated data for moving-target velocity estimation and MiniSAR data for SAR image compression.

SAR image compression with target region extraction and preservation in decomposed structural component

With the bandwidth restriction in airborne Synthetic Aperture Radar (SAR)-based human-in-the-loop applications, the acquired SAR images should be compressed with loss to overcome the conflict of image quantity and the response time. In this letter a framework of SAR image compression is described. The SAR image is decomposed into two components, namely structural and textural components. The target region mask is used to retain the important target information by allocating more bits during compression, while less bits is allocated for the background region. The obtained results show that the compressed image using the proposed algorithm has better visual effect under the same bit rate compared with JPEG2000 algorithm.

Object-based SAR image compression using vector quantization

A simple and elegant algorithm is presented to encode images with rich content, which allows easy access to various objects. An object-plane-based encoding method for compression of synthetic aperture radar (SAR) imagery is developed, with different object planes for target classes and background. A variable-rate residual vector quantization (VQ) algorithm is developed to encode the background information. This algorithm is very powerful as indicated by the experimental results. The proposed coding scheme allows compression matched to the final application of the images, which in this case is target recognition and classification.

SAR image compression with the Gabor transform

A compression system based on the Gabor transform is applied to detected synthetic aperture radar (SAR) imagery. The Gabor transform is a combined spatial-spectral transform that provides local spatial-frequency analyses in overlapping neighborhoods of the image. Gabor coefficients are efficiently computed using the fast Fourier transform (FFT), and a technique for visualizing the coefficients is demonstrated. Theoretical and practical constraints imposed by the Gabor transform are discussed. The compression system includes bit allocation, quantization, and lossless encoding and decoding stages. Bit allocation tradeoffs are discussed and related to perceptual image quality as well as computational measures of image fidelity. Adaptive scalar, vector, and trellis-coded quantizers are compared. Multifrequency codebooks are designed using ten training images derived from data collected at different aspect angles. Subjective image quality assessment experiments indicate that the Gabor transform/trellis-coded quantizer compression system performs significantly better than adaptive scalar and vector quantizers and JPEG on these SAR images.