The use of low-frequency spaceborne fully polarized synthetic aperture radar (SAR), such as the ALOS PALSAR, to detect the structure of ionospheric has been widely validated in recent years. In terms of total electron content (TEC) retrieval, SAR exhibits significant advantages over GPS with regard to both accuracy and resolution at high latitude. However, TEC cannot reflect the vertical structure of the ionosphere and still has limitations. On this basis, this letter presents a study into the application of fully polarimetric PALSAR in computerized ionospheric tomography (CIT), which extends the horizontal ionospheric structure to the vertical, i.e., the 3-D ionospheric electron density (IED) distribution. The reconstruction results from three groups of PALSAR measured data in Alaska are compared with the results from the nearby incoherent scatter radar (ISR). The accuracy has improved by 28.9%, 33.2%, and 47.6%, respectively, compared to the ionosphere model. This shows that the PALSAR has the ability to not only determine the horizontal structure of the ionosphere but also accurately reconstruct the 3-D IED, making it a promising approach for ionosphere sounding at high latitude.

The utilization of Global Positioning System (GPS) for three-dimensional ionospheric electron density reconstruction, i.e., computerized ionospheric tomography (CIT), provides significant importance in investigating the internal structure, variations, and disturbances within the ionosphere. However, the ill-posed problem caused by insufficient observational data or uneven distribution will rely heavily on the selection of initial values, which are typically derived from empirical models with low precision. Aiming at this issue, this paper uses the TEC obtained by the spaceborne full polarization synthetic aperture radar (PolSAR) to correct the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM), thus improving the authenticity of the initial value. This study makes full use of the advantages of low frequency full PolSAR in ionospheric sounding, including high precision and resolution, as well as all-day and all-weather operation without a ground receiver. Therefore, the precision of GPS-based tomography can be enhanced, particularly for small-scale anomalies, and it is also simple and easy to achieve. Numerical and measured experiments using GPS, incoherent scatter radar, PolSAR, and E-CHAIM data in Alaska demonstrate that the reconstruction accuracy of the proposed CIT is significantly improved than that of the tomography results using only empirical model. In addition, the effects of PolSAR system errors and voxel size on CIT are analyzed to demonstrate the robustness of the method proposed in this paper.

Hyperspectral image classification (HSIC) has attracted widespread attention due to its important application in environment alterations and geophysical disaster monitoring. However, surface cultivation is not static as time passes, which leads to different hyperspectral images information collected from the same area at different time periods. Therefore, researchers are currently eager to construct a HSIC model that continuously acquires new classes of data. During the continuous learning process, the model is expected to not only effective in extracting unique spatial-spectral features of the hyperspectral image, but also ensures the ability to maintain the old classes knowledge while learning new data. To achieve this purpose, we propose a method which based on geometric spatial-spectral feature integration network with class incremental learning (GS2FIN-CIL) framework in continuous learning to make the model adaptable to new classes data and not overly forgetting the old classes knowledge during the training process. We conduct extensive experiments with the proposed GS2FIN-CIL method on widely-used hyperspectral datasets including Indian Pines, PaviaU and Salinas. The experimental results show that our GS2FIN-CIL method can achieve significantly improved results compared to current state-of-the-art class incremental learning methods, allowing for efficient adaptation and utilization of spatial-spectral features in processing new classes of hyperspectral images and alleviating the problem of catastrophic forgetting of learned old classes knowledge. The GS2FIN-CIL method could be successfully applied to the challenge of adding new classes data in HSIC task.

The GPS-based computerized ionospheric tomography (CIT) has the capacity to reconstruct the three-dimensional ionosphere (i.e., electron density distribution), making it one of the most important techniques for ionospheric observation. However, the CIT technology is unable of high vertical resolution due to the restricted viewing angle. Therefore, the precision of CIT cannot be substantially enhanced with GPS only. Aiming at this issue, this paper proposes a bi-iteration algorithm that integrates GPS, Phased Array L-band Synthetic Aperture Radar on board the Advanced Land Observing Satellite (ALOS PALSAR), and ionosonde data. The key is that the joint retrieval of PALSAR and ionosonde may offer high-precision one-dimensional electron density profile along the whole path, which can effectively enhance the authenticity of the iterative initial value. The corrected initial value is then fused into the process of CIT, which can finally improve the precision of vertical resolution after two iterations. Experimental verification demonstrates that due to the ability to obtain more realistic initial values, the reconstruction accuracy of the algorithm proposed in this paper is 51.4 percent and 45.1 percent higher than the accuracy with GPS alone and with GPS and ionosonde data, respectively. This indicates that the combination of these three kinds of data can effectively improve the precision of CIT.

The Qinghai-Tibet Plateau (QTP) is located at the roof of the world. Since its unique geography, the spatial-temporal characteristics of sporadic E layer (Es) over and around there could be hints of the coupling between upper and lower atmosphere. It also plays an important role on radio propagation at HF and VHF band. In this letter, the intensity and spatial distribution of Es over the QTP, and the response to solar activity are studied. With the bonus of the Chinese ionosonde network for more than 60 years, some conclusions are arrived at firstly for the QTP. (1) The diurnal and seasonal variations of Es layer are significant. The intensity and critical frequency are the highest at 11:00 and 12:00 LT in summer, while the lowest at sunrise in winter. (2) It is found that the Es intensity is negatively correlated with the solar cycle. In 2020, the solar minimum of 200 years, Es intensity is obvious higher than other years. (3) The Es intensity positively correlates with the terrain. Es intensity in the southeast is higher than northwest of the Plateau significantly.