Data quantity and quality are both critical for information extraction and analyzation in remote sensing. The current remote sensing datasets, however, often fail to meet these two requirements, for which the cloud is a primary factor degrading the data quantity and quality. This limitation affects the precision of results in remote sensing applications, particularly those derived from data-driven techniques. In this article, a physical law embedded generative cloud synthesis (PGCS) method is proposed to generate diverse,ealistic cloud images to enhance real data and promote the development of algorithms for subsequent tasks, such as cloud correction, cloud detection, and data augmentation for classification, recognition, and segmentation. The PGCS method involves two key phases: spatial synthesis and spectral synthesis. In the spatial synthesis phase, a style-based generative adversarial network is used to simulate the spatial characteristics, generating an infinite number of single-channel clouds. In the spectral synthesis phase, the atmospheric scattering law is embedded through a local statistics and global fitting method, converting the single-channel clouds into multispectral clouds. The experimental results demonstrate that PGCS achieves a high accuracy in both phases and performs better than three other existing cloud synthesis methods. Two cloud correction methods are developed from PGCS and exhibits a superior performance compared to state-of-the-art methods in the cloud correction task. The application of PGCS with data from various sensors was, furthermore, investigated and successfully extended. Code will be provided athttps://github.com/Liying-Xu/PGCS.

Recent years have witnessed the remarkable success of the vision-language model in various computer vision tasks. However, how to exploit the semantic language knowledge of the vision-language model to advance real-world stereoscopic image super-resolution remains a challenging problem. This paper proposes a vision-language model-based stereoscopic image super-resolution (VLM-SSR) method, in which the semantic language knowledge in CLIP is exploited to facilitate stereoscopic image SR in a training-free manner. Specifically, by designing visual prompts for CLIP to infer the region similarity, a prompt-guided information aggregation mechanism is presented to capture inter-view information among relevant regions between the left and right views. Besides, driven by the prior knowledge of CLIP, a cognition prior-driven iterative enhancing mechanism is presented to optimize fuzzy regions adaptively. Experimental results on four datasets verify the effectiveness of the proposed method.

In order to alleviate the impact of ambient light on the quality of captured images, correcting multi-exposure images has become a popular topic. Most existing multi-exposure image correction methods mainly focus on the adjustment of lightness levels, but ignore the significant issue of structural information loss in incorrectly exposed images. Taking into consideration both lightness adjustment and structural reconstruction, this article proposes an adaptive multi-exposure image correction network by jointly exploring the lightness and structure information, named LSANet. Specifically, the proposed LSANet first extracts lightness and structure representations of the input image in the frequency domain, and then performs exposure level adjustment and structure detail reconstruction based on the lightness and structure representations. In the proposed network, the lightness- and structure-aware adaptive module is designed to achieve adaptive correction by predicting dynamic kernels under the guidance of the lightness and structure representations. Experimental results on the widely used ME and SICE datasets demonstrate that the proposed LSANet achieves excellent performance and generates images with well-exposed levels and rich structural details.

Shadow removal is beneficial for various remote sensing applications, such as semantic segmentation and object detection. However, traditional shadow removal methods perform poorly when applied to high-resolution remote sensing images. Some deep learning-based models exhibit high-level accuracy of removal, but it is difficult to be expanded to multi-scenarios due to insufficient pairs of shadow/shadow-free data in reality. In this paper, a novel framework is proposed from shadow synthesis to shadow removal, aiming to improve the usability of deep learning-based models in the task of shadow removal in high-resolution remote sensing images. First, we combine the physical shadow illumination model and a domain alignment network to synthesize a large variety of realistic shadows. Then, a shadow removal network considering global-local features is built to restore the ground surface details finely. Numerous experiments have shown that the proposed framework can effectively remove various shadows and is superior to existing methods.

There is no "cloudy & cloud-free" paired images with totally identical surface information under the same spatial and temporal condition in reality, which limits the development of supervised deep learning methods in the field of cloud removal and detection. In this regard, a high-fidelity thin cloud synthetic method is proposed, which is more challenging than the synthesis of thick clouds. This method combines physical model and data-driven methods. The expression of scattering laws at the pixel level is extended to the channel level to synthesize multi-channel cloud from cirrus band with controlled cloud thickness. Besides, spatial and spectral features are learned from real data using generative adversarial networks and transformed to synthetic data. Based on it, a dataset containing infinite number of "cloudy & cloud-free" pairs can be constructed. Experimental results show that the proposed method has the best visual effect with highest quantitative evaluations compared with current methods.

Cloud contamination is inevitable in optical satellite images, especially for those in visible and near-infrared (VNIR) spectra. A general thin cloud correction method for satellite VNIR images is proposed in this study by coupling statistical information with a scattering model to solve the abovementioned problem. A thin cloud map (TCM) is created by utilizing the characteristics of land surface and thin clouds to depict the thin cloud spatial distribution and relative intensity. Furthermore, different cloud reflectance estimation (CRE) algorithms are proposed for different VNIR bands by considering the scattering properties of thin clouds. For coastal and blue bands with short wavelengths, the images are divided into multiple layers to search for dark pixels based on TCM so that thin clouds can be estimated via robust regression. The CRE of the green, red, and near-infrared bands with long wavelengths is realized via the scattering model by taking the thin clouds of coastal or blue bands as a reference. Experiments are performed on cloud-covered VNIR images captured by different satellites to validate the universality of the proposed method. Two traditional methods and one deep learning method are utilized for a comparison. Compared with the benchmark methods, the proposed method yields totally cloud-free images and more credible color. The quantitative measures obtained by the proposed method are the closest to the ideal values among the four methods. Discussions of the novelties of the proposed method, the extended applications of TCM, and the parallax problem in experiments is also performed to complete the evaluation.

Since chronic loneliness is both a painful individual experience and an increasingly serious social problem, robot companions have emerged as a result of robotization of social work to confront this issue. We foresee that social robots will become pervasive in the near future. Thus, it is crucial to pinpoint the relationship between chronic experiences of loneliness (i.e., trait loneliness) and both anthropomorphism and acceptance of such artificial intelligent agents. Previous research demonstrated that experimentally induced state loneliness increases anthropomorphic inferences about nonhuman agents such as pets. However, in the present research we found that trait (vs. state) loneliness - a permanent personality disposition that is not easily relieved (vs. transitory experiences caused by circumstance, and easily relieved) - reduced participants' anthropomorphic tendencies and acceptance of a social robot (regardless of the form: a picture of the robot, an on-site robot, or direct interaction with the robot). In particular, believing that the robot lacks good "unique humanness" traits (i.e., Humble, Thorough, Organized, Broadminded, and Polite) is one reason why dispositionally lonely participants are less likely to anthropomorphize a robot, which further prompts reduced acceptance of it. This finding suggests that unique humanness, exemplifying secondary emotions, is vital, not only in interpersonal contexts, but in establishing connections with social robots.

Compact polarimetric interferometric synthetic aperture radar (CPolInSAR) has been widely used due to its low complexity and costs. But in the field of land cover classification, there are very few studies on CPolInSAR images. In this paper, a novel classification method is proposed to investigate the performance of CPolInSAR images for land cover classification. Specifically, the m - δ decomposition and the optimal polarimetric coherence coefficient are first employed to extract the features of CPolInSAR images, and then support vector machine (SVM) is utilized for classification. The experimental results show that 1) the optimal polarimetric coherence coefficient can be used to achieve higher accuracy especially in wood land and built-up areas; 2) CPolInSAR has a greater potential for land cover classification than PolSAR and compact PolSAR.

Memristor-based logic design gives an alternative solution to improve the energy efficiency of computing systems, benefiting from combining the memory with computing units. Inspired by this thought, previous work has demonstrated various memristor-based logic families with different attributes and computation patterns. Besides, some logic synthesis tools are designed for specific memristive logic implementations. However, the poor universality and the neglect of realistic constraints in memory largely restrict the utility of these logic synthesis tools. In this paper, we propose a general logic synthesis framework for memristor-based logic design, containing a universal abstract description method for memristive logic, a mapping rules generator, and a synthesis and mapping flow. The proposed logic synthesis framework is suitable for various types of existing memristor-based logic families and takes the memory status into consideration. It is also possible to handle future memristive devices and logic families by providing the universal abstraction interface. Furthermore, we also design a circuit-partitioning-based synthesis acceleration strategy to tackle with the long synthesis time problem. Experimental results show that, our framework can generate mapping results under the restriction of limited resource, while the existing synthesis tools may fail under the same restriction, and achieve comparable synthesis results with the same resource as the existing synthesis tools, which is enough for computation and storage. And the proposed acceleration scheme can achieve ~ 1000× speedup compared with the initial one.

The spatial localization of interference source is a critical procedure in Radio frequency interference (RFI) suppression. It is not suitable to use traditional multi-station interference localization methods in most synthetic aperture radar (SAR) systems. This paper proposes a novel method of single RFI localization based on conjugate cross-correlation of dual-channel SAR signals. First, interference detection technology is applied to check whether echo signal is interfered. Then, conjugate cross-correlation is performed on SAR signals, and the distance difference between interference source and receiving channels is calculated. Finally, spatial localization equations of interference source are established, and optimization algorithm is applied to solve the coordinates of interference source. The results of simulation experiments illustrate the effectiveness of the proposed method.

Polarimetric synthetic aperture radar (SAR) Interferometry (PolInSAR) becomes a hot topic in SAR application areas, such as terrain classification. However, the system parameter design is mostly based on traditional Polarimetric SAR (PolSAR) or interferometric SAR (InSAR) methods. The accuracy of polarimetric calibration is the major factor for the PolInSAR system design, so it is necessary to analyze the affect of polarimatric calibration errors on terrain classification accuracy. In this paper, error transfer link of typical classification algorithm is investigated, then, the minimum requirements on polarimetric distortions including channel imbalance and crosstalk are investigated for terrain classification application for the first time. The result is verified by E-SAR datasets, which can provide the important theoretical basis and reference for quantitative calibration requirements of classification accuracy.

Compared with SAR interferometry(InSAR), polarimetric SAR interfermotry(PolInSAR) acquires plenty information to estimate parameters, and on the other side, the penetrability of low frequency electromagnetic wave is better than high frequency. So an accurate retrieval of the topography can be implemented from low frequency quad polarimetric SAR interfermotry on the basis of the Random Volume over Ground (RVoG) scattering model abstractly. But there is no paper validation about this theory with experiment data from quantitative point of view. In this paper, analysis and comparison of the quantitative topography accuracy between InSAR and PolInSAR, between X-band PolInSAR and L-band PolInSAR are presented firstly with data validation. Simulated data are used to prove the validity.