Conventional Kalman filter (KF) relies heavily on a priori knowledge of the potentially unstable process and measurement noise statistics. Insufficiently known a priori filter statistics will reduce the precision of the estimated states or introduce biases to the estimates. We propose an adaptive KF based on the autoregressive (AR) predictive model for vehicle navigation. First, the AR model is incorporated into the KF for state estimation. The closed-form solution of the AR model coefficients is obtained by solving a convex quadratic programming problem, which is according to the criterion of minimizing the mean-square error, and subject to the polynomial constraint of vehicle motion. Then, an innovation-based adaptive approach is improved based on the KF with the AR predictive model. In the proposed adaptive algorithm, the process noise covariance is computed using the real-time information of the innovation sequence. Simulation results demonstrate that the KF with the AR model has a higher estimated precision than the KF with the traditional discrete-time differential model under the condition of the same parameter setting. Field tests show that the positioning accuracy of the proposed adaptive algorithm is superior to the conventional adaptive KF.

Neural network models play an important role in crop extraction based on remote sensing data. However, when dealing with high-dimensional remote sensing data, these models are susceptible to performance degradation. In order to address the challenges associated with multi-source Gaofen satellite data, a novel method is proposed for dimension reduction and crop classification. This method combines the benefits of the stacked autoencoder network for data dimensionality reduction, and the convolutional neural network for classification. By leveraging the advantages of multi-dimensional remote sensing information, and mitigating the impact of dimensionality on the classification accuracy, this method aims to improve the effectiveness of crop classification. The proposed method was applied to the extraction of crop-planting areas in the Yangling Agricultural Demonstration Zone, using multi-temporal spectral data collected from the Gaofen satellites. The results demonstrate that the fusion network, which extracts low-dimensional characteristics, offers advantages in classification accuracy. At the same time, the proposed model is compared with methods such as the decision tree (DT), random forest (RF), support vector machine (SVM), hyperspectral image classification based on a convolutional neural network (HICCNN), and a characteristic selection classification method based on a convolutional neural network (CSCNN). The overall accuracy of the proposed method can reach 98.57%, which is 7.95%, 4.69%, 5.68%, 1.21%, and 1.10% higher than the above methods, respectively. The effectiveness of the proposed model was verified through experiments. Additionally, the model demonstrates a strong robustness when classifying based on new data. When extracting the crop area of the entire Yangling District, the errors for wheat and corn are only 9.6% and 6.3%, respectively, and the extraction results accurately reflect the actual planting situation of crops.

The UNESCO Agenda 2030 emphasizes the preservation of cultural heritage sites, focusing on coastal heritage preservation, which still poses substantial difficulties. While earlier studies have addressed the overall consequences of natural hazards along the Alexandria coastline, there's a gap in how they specifically affect coastal heritage sites, e.g., the Qaitbay citadel (our case study). This work seeks to bridge this gap by assessing the critical hazards faced by the Qaitbay citadel, including crustal deformation due to tectonic events, earthquakes, and Sea Level Rise (SLR) resulting from climate change. To comprehensively assess these challenges, a stack of Sentinel-1 SAR datasets (2017-2021) was processed using the Persistent Scatterer InSAR (PS-InSAR) technique to conduct spatial and temporal deformation variations of the citadel's site and its buildings. GPS measurements of Alexandria's (e.g., ALX2) station were correlated with InSAR results within the same period. Furthermore, satellite altimetry data from 1993-2021 covering the citadel and surroundings were processed to highlight long-term SLR trends. The findings indicate (1) A subsidence rate of -1 ± 0.2 mm/yr, associated with the citadel's foundational structures and identified through the PS-InSAR analysis, was caused by load-bearing effects and sand migration beneath the citadel, (2) a vertical displacement of -1.3± 0.6 mm/yr obtained from ALX2-GPS station, consistent with the LOS velocity rate of PS-InSAR time series analysis at that specific location, (3) a SLR trend of +3.96 mm/yr, with notable peaks potentially related to episodes of Northern Ionian Gyre reversal, that could result in changes in water mass redistribution in the surrounding region.

Three-dimension acceleration, azimuth dependence, and real-time processing are the main issues to be solved in highly-squinted frequency-modulation continuous-wave (FMCW) SAR with a curved trajectory. To overcome these issues, a modified wavenumber-domain algorithm is developed in this paper. The proposed modified wavenumber-domain algorithm mainly includes the following six aspects. Firstly, a modified range model considering three-dimension acceleration for a curved trajectory is established. Then, the spectrum compression and rotation operations are performed to guarantee further processing. Afterward, residual phase terms introduced by acceleration are bulk compensated for. Moreover, the azimuth dependence is eliminated by the azimuth resampling to achieve uniform focusing. Subsequently, the range-azimuth coupling is removed by the modified Stolt interpolation. Finally, to avoid image aliasing, the azimuth wavenumber domain is selected to focus final image. The experimental results of both numerical and raw-measured FMCW-SAR data demonstrate the superiority of the proposed method.

Multi‐baseline phase unwrapping operation is a very important step for multi‐baseline Synthetic Aperture Radar Interferometry (InSAR). However, the conventional methods applied for single pixel suffer seriously from the phase noise that exists numerously in the interferometric phase images (i.e. interferograms). In order to improve the robustness to noise, this study proposes an innovative multi‐baseline InSAR phase unwrapping method based on a mixed‐integer optimisation model. The proposed method combines the central and its neighbouring pixels to jointly construct the mixed‐integer optimisation model under the assumption that the pixels within a local window can be approximated by a small slant plane terrain. Furthermore, considering the practical case, the optimal window size is estimated according to the deviation from the interferometric wrapped phase to the assumed linear terrain, and the fast Fourier transform technique is adopted to reduce computational cost. The theoretical analysis and computer simulation demonstrate that the proposed method is able to improve the multi‐baseline phase unwrapping performance and can be applied to reconstruct the digital elevation models for complicated topographies.

The accurate acquisition of farmland boundary information is an important means for agricultural production data statistics. Due to the extreme imbalance of categories in farmland boundary extraction tasks, direct use of neural networks for boundary pixel prediction in remote sensing images often results in poor performance. This paper investigates the beneficial effects of multi-source remote sensing data fusion and the use of semantic segmentation to assist in boundary extraction. The research area of this work covers an area of approximately 600 km ² of farmland located in northern Xinjiang. And obtained Sentinel-2 multispectral images covering the area, as well as Sentinel-1 dual polarized synthetic aperture radar (SAR) images with multiple temporal phases. A novel cascaded model of semantic segmentation and edge detection is proposed for the boundary extraction task of farmland. Using the fused data of Sentinel-1 and Sentinel-2 as the raw data input of the model for semantic segmentation of farmland and non-farmland. Then, the semantically segmented feature map is overlaid with the original data to detect the edge and extract the farmland boundary. The results show that after adding SAR data, the extraction effect of farmland boundaries has been significantly improved. Moreover, compared to directly using neural network models for boundary extraction, using semantic segmentation information to assist in boundary extraction can achieve higher extraction accuracy. The experimental results validate the effectiveness of the proposed model and the feasibility of combining SAR remote sensing information to optimize the extraction of farmland boundaries.