With the development of airborne synthetic aperture radar (SAR) technology, the 3D SAR point cloud reconstruction has emerged as a crucial development trend in the current SAR community. However, due to measurement errors, environmental interference, radar decoherence, and other noises associated with the SAR system, the reconstructed tomogram is often deteriorated by numerous noisy scatterers. As a result, it becomes challenging to obtain high-quality 3D point clouds of the observed object, making it difficult to further process the point cloud and realize target identification. To address these issues, we propose a K nearest neighbor comprehensive weighted filtering algorithm. The filtered point cloud is evaluated quantitatively using three-dimensional entropy. In this study, we adopted various filtering methods for simulated data, P-band data of Genhe, and Ku-band data of Yuncheng to refine the tomogram and compare their performances. Both qualitative and quantitative analyses demonstrate the superiority of the filtering algorithm proposed in this paper.

This paper investigates the potential of the time-frequency optimization on the basis of the sublook decomposition for forest height estimation. The optimization is deemed to be capable of extracting a relatively accurate volume contribution when P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) systems are adopted to observe forest-covered areas. The highest and the lowest phase centers acquired by the time-frequency optimization modify the conventional three-stage inversion process. This paper presents, for the first time, a performance assessment of the time-frequency optimization on P-band Pol-InSAR data over boreal forests. Simultaneously, to alleviate the model inversion errors caused by topographic fluctuations, forest height is estimated based on the sloped Random Volume over Ground (S-RVoG) model in which the incidence angle is corrected with the terrain slope. The E-SAR P-band Pol-InSAR data acquired during the BIOSAR 2008 campaign in Northern Sweden is utilized to evaluate the performance of the proposed method. From the results of the forest height estimation preprocessed with time-frequency optimization, the root mean square error (RMSE) of Random Volume over Ground (RVoG) and S-RVoG model on negative slope are 5.09 m and 4.71 m, respectively. It is concluded that the time-frequency processing and negative terrain slope compensation improve the inversion performance by $41.49\%$ and $11.96\%$ , respectively.

We present a quantum secure imaging (QSI) scheme based on the phase encoding and weak+vacuum decoy-state BB84 protocol of quantum key distribution (QKD). It allows us to implement a computational ghost imaging (CGI) system with more simplified equipment and reconstructed algorithm by using a digital micro-mirror device (DMD) to preset the specific spatial distribution of the light intensity. What is more, the quantum bit error rate (QBER) and the secure key rate analytical functions of QKD are used to see through the intercept-resend jamming attacks and ensure the authenticity of the imaging information. In the experiment, we obtained the image of the object quickly and efficiently by measuring the signal photon counts with a single-photon detector (SPD), and achieved a secure key rate of 571.0 bps and a secure QBER of 3.99%, which is well below the lower bound of QBER of 14.51%. Besides, our imaging system uses a laser with invisible wavelength of 1550 nm, whose intensity is as low as single-photon, that can realize weak-light imaging and is immune to the stray light or air turbulence, thus it will become a better choice for quantum security radar against intercept-resend jamming attacks.

The Gaussian vertical backscatter (GVB) model has a pivotal role in describing the forest vertical structure more accurately, which is reflected by P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) with strong penetrability. The model uses a three-dimensional parameter space (forest height, Gaussian mean representing the strongest backscattered power elevation, and the corresponding standard deviation) to interpret the forest vertical structure. This paper establishes a two-dimensional GVB model by simplifying the three-dimensional one. Specifically, the two-dimensional GVB model includes the following three cases: the Gaussian mean is located at the bottom of the canopy, the Gaussian mean is located at the top of the canopy, as well as a constant volume profile. In the first two cases, only the forest height and the Gaussian standard deviation are variable. The above approximation operation generates a two-dimensional volume only coherence solution space on the complex plane. Based on the established two-dimensional GVB model, the three-baseline inversion is achieved without the null ground-to-volume ratio assumption. The proposed method improves the performance by 18.62% compared to the three-baseline Random Volume over Ground (RVoG) model inversion. In particular, in the area where the radar incidence angle is less than 0.6 rad, the proposed method improves the inversion accuracy by 34.71%. It suggests that the two-dimensional GVB model reduces the GVB model complexity while maintaining a strong description ability.

Ocean surveillance is one of the important applications of synthetic aperture radar (SAR). Polarimetric SAR provides multi-channel information and shows great potential for monitoring ocean dynamic environments. Oil spills are a form of pollution that can seriously affect the marine ecosystem. Dual-polarimetric SAR systems are usually used for routine ocean surface monitoring. The hybrid dual-pol SAR imaging mode, known as compact polarimetry, can provide more information than the conventional dual-pol imaging modes. However, backscatter measurements of the hybrid dual-pol mode depend on the transmit wave polarization, which results in lacking consistent interpretation for various compact polarimetric (CP) images. In this study, we will explore the capability of different CP modes for oil spill detection and discrimination. Firstly, we introduce the general CP formalism method to formulate an arbitrary CP backscattered wave, such that the target scattering vector is characterized in the same framework for all CP modes. Then, a recently proposed CP decomposition method is investigated to reveal the backscattering properties of oil spills and their look-alikes. Both intensity and polarimetric features are studied to analyze the optimal CP mode for oil spill observation. Spaceborne polarimetric SAR data sets collected over natural oil slicks and experimental biogenic slicks are used to demonstrate the capability of the general CP mode for ocean surface surveillance.

In this paper, we propose a classification scheme for forest growth stage types and other cover types using a support vector machine (SVM) based on the Polarimetric SAR Interferometric (PolInSAR) data acquired by Chinese Multidimensional Space Joint-observation SAR (MSJosSAR) system. Firstly, polarimetric, texture, and coherence features were calculated from the PolInSAR data. Secondly, the capabilities of the polarimetric, texture, and coherence features in land use/cover classification were quantified independently through histograms. Following this, the polarimetric features were used for the classification of land use/cover types, followed by a combination of texture and coherence features. Finally, the three classification results were validated against test samples using the confusion matrix. It was shown that, with the integration of texture and coherence features, the producer’s accuracy for afforested land, young forest land, medium forest land, and near-mature forest land improved by 6%, 31%, 11%, and 6%, respectively, compared with the former experiment using solely polarimetric features. Our study indicates that the forest and non-forest lands can be discriminated by the polarimetric features, which also play an important role in the separation between afforested land and other forest types as well as medium forest land and near-mature forest land. The texture features further discriminate afforested land and other forest types, while the coherence features obviously improved the separation of young forest land and medium forest land. This paper provides an effective way of identifying various land use/cover types, especially for distinguishing forest growth stages with SAR data. It would be of great interest in regions with frequent cloud coverage and limited optical data for the monitoring of land use/cover types.