Quantifying the precision of Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring is a fundamental aspect of ensuring its reliability in operational applications. Traditional uncertainty estimation methods, which are based on phase noise and error propagation laws, tend to overestimate monitoring precision. In this study, we systematically investigate the combined effects of coherence, deformation magnitude, SAR wavelength, and spatial resolution on monitoring uncertainty through multi-parameter-controlled experiments. Based on this, a wavelength-coherence integrated function model, referred to as the InSAR Deformation Monitoring Uncertainty model (In-DMU), was developed to estimate the precision of the deformation measurements. To rigorously define applicability of the model, a Pettitt test based on sliding window standard deviation (Pt-SWStd) is used to detect change points in the error distribution, thus establishing critical gradient thresholds of 6.433 mm/m (L-band), 1.407 mm/m (C-band), and 0.456 mm/m (X-band). Beyond these thresholds, phase unwrapping constraints lead to the retrieval breakdown regime, limiting reliable deformation estimation. Furthermore, the influence of multi-looking processing on the In-DMU model was systematically quantified. To assess practical applicability, the In-DMU model was validated across diverse observational scenarios using data from five representative SAR satellites: ALOS-1 (L-band), ALOS-2 (L-band), Radarsat-2 (C-band), Sentinel-1 (C-band), and TerraSAR-X (X-band). The In-DMU model provides a universal tool for priori estimation of InSAR monitoring precision, offering valuable guidance for SAR data selection and optimization of multi-looking configurations.

Interferometric phase optimization is a critical step in multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR). The advent of multi-polarimetric SAR satellites has boosted the polarimetric interferometric phase optimization techniques, where the polarimetric information can be used to enhance the interferometric phase quality. Polarimetric interferometric phase optimization approaches mainly include polarimetric coherence optimization (PCO) and polarimetric phase linking (PPL) algorithms. PCO algorithms that with good performance like Exhaustive Search Polarimetric Optimization (ESPO) are with high computational cost, especially for quad-polarimetric data cases. PPL algorithms mainly focus on distributed scatterers (DSs) and do not adopt the adaptive optimization for PS pixels. In addition, their DSs selection merely depends on the number of statistically homogeneous pixels (SHPs) through setting a threshold whose determination approach is usually not clearly defined. Moreover, the interferometric phase estimation results of PPL are affected by heterogeneous pixels. To overcome these limitations, by employing the total power polarization optimization and non-local PL, an adaptive polarimetric interferometric phase optimization algorithm for both PS and DS pixels is developed. The proposed algorithm has been evaluated using the simulated data, quad-polarimetric UAVSAR data, and dual-polarimetric Sentinel-1 data. The results demonstrate that in comparison with the previous methods, the proposed algorithm achieves superior interferometric phase quality, along with improved effectiveness and efficiency.

The Non-Line-of-Sight (NLOS) phenomenon can hinder signal propagation and significantly reduce the accuracy of UWB for indoor positioning and navigation. The Channel Impulse Response (CIR) sequence generated during UWB ranging is widely used for channel identification. However, existing deep learning algorithms struggle to balance the local and global features of the CIR sequence effectively. To address this, this paper constructs a Bidirectional Encoder Representations from Transformers (BERT) channel identification model using the self-attention mechanism to improve the NLOS identification. The identification Accuracy, LOS recall, and F2 scores in multiple scenarios are 96.65%, 97.13%, and 0.9703 respectively. Comparing to state-of-art algorithms such as LS-SVM, CNN, and LSTM, our algorithm outperformed by 17.9%, 11.86%, and 10.80% respectively. For NLOS ranging errors, a fine-grained classification model is constructed with error correction model based on BERT. In multiple scenarios, the average NLOS ranging error is reduced by 41.97% and outperforms LS-SVM, CNN, and LSTM by 34.04%, 31.99%, and 16.81% respectively. In the overall positioning experiment, our algorithm achieves better performance than the existing algorithms by 32.13%.

Accurately locating goafs is critical for identifying illegal mining, preventing mining-related geohazards, and facilitating the development and utilization of underground spaces. Conventional methods for locating goafs with InSAR techniques primarily rely on the Probability Integral Model (PIM), which tends to overestimate the ground deformation under subcritical extraction. On the other hand, the number of subcritical extraction working faces significantly rises with mining depth. Under these circumstances, accurately locating subcritical underground goafs using existing methods becomes challenging. To this end, a novel method, which incorporates the improved probability integral model (IPIM) and InSAR technique for locating subcritical goafs, is proposed, named the locating goaf method based on IPIM (LGM-IPIM). Firstly, based on the IPIM, a model between the subcritical goaf parameters and InSAR-derived deformation is built. Then, to reduce the influence of surrounding mining, the goaf azimuth angle is determined with textures and patterns of the InSAR-derived deformation time series. Finally, the genetic algorithm-particle swarm optimization (GA-PSO) is employed to determine the goafs’ parameters. The effectiveness of the proposed algorithm has been verified by simulation and real data. The results demonstrate that the proposed LGM-IPIM outperforms conventional methods, presenting the best performance and the highest accuracy. Specifically, compared to the locating goaf method based on PIM (LGM-PIM), the proposed LGM-IPIM improves the location accuracy of goaf boundary points by 28.90% and 86.23% in Areas A and B, respectively. In addition, the proposed LGM-IPIM has robustness against minor errors within the deformation monitoring and IPIM parameters.

Underground coal fires are global catastrophes that result in energy waste, carbon emission, and eco-environment pollution. Remote sensing (RS) detection is essential for underground coal fire extinguishing engineering, and the most used is thermal infrared (TIR) RS. It can well obtain the thermal anomalies of land surface temperature (LST), which is the most direct surface feature of underground coal fires. However, most studies using TIR RS simply delineate underground fire sources vertically according to LST anomalies, which has relatively little impact when initially determining coal fire area locations on the large scale. As for the precise location of small-scale subsurface fire sources, the deviation between subsurface fire source locations inferred and real locations could lead to errors or even mistakes to fire extinguishing engineering. There is a lack of subsurface fire source evolution model reconstruction method, and the spatiotemporal correlations characteristic of LST thermal anomalies and underground fire sources have not yet been discussed. To this end, taking Miquan coalfield (Western China) as an example, a 3-D empirical Bayesian Kriging (EBK3D) method is first proposed to reconstruct the 4-D temperature fields of underground fire sources. Then, the feasibility of the vertical correspondence approach to inferring small-scale subsurface fire sources through LST thermal anomalies detected by unmanned aerial vehicle TIR RS and satellite TIR RS is analyzed. Finally, the spatiotemporal correlation characteristic of LST thermal anomalies and subsurface fire sources is analyzed. As the results show, it is feasible to reconstruct the underground fire source evolution model by the EBK3D method. The reconstructed 4-D temperature fields can dynamically reflect the evolutionary states of underground fire sources in three time periods, with cross-validated root mean square errors of 52.2 °C, 49.6 °C, and 37.1 °C and$R^{2}$of linear regressions of 0.925, 0.9145, and 0.8429, respectively. The LST thermal anomalies show a significant spatiotemporal delay with respect to the subsurface fire source evolution. This makes the locations of the underground fire sources traced by the vertical correspondence method deviate from the real ones. The offsets of underground fire sources relative to surface thermal anomalies in the coal seam strike and dip directions for different time periods at depths of (T1: −44.43 m, T2: −27.72 m, and T3: −20.04 m) are (T1: 73.80 m, T2: 52.33 m, and T3: 45.06 m), and (T1: 16.79 m, T2: 17.27 m, and T3: 24.82 m), respectively.$R^{2}$’s for the linear regression model of the offset averages in three directions versus time and fire source size are (0.9247, 0.7949, and 0.9564) and (0.8739, 0.85 and 0.9152), respectively.

Smartphone-based WiFi ranging positioning based on fine time measurement (FTM) always collapses in real-life scenarios. In this work, a novel map-aided particle filter (PF)-based WiFi FTM fingerprinting approach is proposed to address the poor performance of the WiFi FTM ranging positioning. Different from manually collecting fingerprints, this approach utilizes the theoretical received signal strength and geometric distances between the access points and reference points as the fingerprints, which means less labour-intensive work. For accurate WiFi position estimation, a map-aided PF is designed to find the optimal position. Extensive experiments are carried out in the non-line-of-sight (NLoS) and mixed line-of-sight/non-line-of-sight (LoS/NLoS) environments, and the testing results show that the accuracy and stability of FTM fingerprinting are improved by using the mixed RSS and ranging data fingerprints. The minimal mean location errors (MEs) of the PF-based WiFi FTM fingerprinting in NLoS and mixed LoSINLoS conditions are 1.70 m and 1.85 m, respectively. Compared to the classic weighted least square method, the MEs are reduced by 54.91 % and 45.43 %, respectively. The testing results demonstrate that the PF-based FTM fingerprinting is an effective approach that provides satisfactory localization results in real-life indoor environments.

This letter presents a novel spatial-attraction-based Markov random field (MRF) (SAMRF) approach for high spatial resolution multispectral imagery (HSRMI) classification. First, the initial class label and class membership for each pixel are obtained by applying the maximum likelihood classifier (MLC) classification for the HSRMI. Second, to reduce the oversmooth classification in the traditional MRF, an adaptive weight MRF model is introduced by integrating the spatial attraction model into the traditional MRF. Finally, the initial classification map, generated in the first step, will be refined though the SAMRF regularization. Two different experiments were performed to evaluate the performance of the SAMRF, in comparison with standard MLC and MRF. Experimental results indicate that the SAMRF method achieved the highest accuracy, hence, providing an effective spectral-spatial classification method for the HSRMI.

This letter presents a novel spatial features extraction method for the high spatial resolution multispectral imagery (HSRMI) classification. First, Canny filter algorithm is applied to extract the edge information to obtain the fuzzy edge map. Secondly, adaptive threshold value for each pixel's homogeneous region (PHR) calculation is determined based on the fuzzy edge map and original image. Next, the PHR for every pixel is obtained based on the fuzzy edge map, adaptive threshold value and original image. And then, the pixel shape feature set (PSFS) is extracted based on the PHR. Lastly, SVM classifier is applied to classify the hybrid spectral and PSFS. Two different experiments were performed to evaluate the performance of PSFS, in comparison with spectral, gray level co-occurrence matrix (GLCM) and the existing pixel shape index (PSI). Experimental results indicate that the PSFS achieved the highest accuracy, hence, providing an effective spectral-spatial classification method for the HSRMI.