Multisource remote sensing images (RSIs) can capture the complementary information of ground objects for use in semantic segmentation. However, there can be inconsistency and interference noise among the multimodal data from different sensors. Therefore, it is a challenge to effectively reduce the differences and noise between the different modalities and fully utilize their complementary features. In this article, we propose a universal cross-fusion transformer framework (CFFormer) for the semantic segmentation of multisource RSIs, adopting a parallel dual-stream structure to extract features separately from the different modalities. We introduce a feature correction module (FCM) that corrects the features of the current modality by combining features from the other modalities in both the spatial and channel dimensions. In the feature fusion module (FFM), we employ a multihead cross-attention mechanism to interact globally and fuse features from the different modalities, enabling the comprehensive utilization of the complementary information in multisource RSIs. Finally, comparative experiments demonstrate that the proposed CFFormer framework not only achieves state-of-the-art (SOTA) accuracy but also exhibits outstanding robustness when compared to the current advanced networks for semantic segmentation of multisource RSIs. Specifically, CFFormer achieves a mean intersection over union (mIoU) of 58% and an overall accuracy (OA) of 85.35% on the WHU-OPT-SAR dataset, outperforming the second-ranked network by 4.71% and 1.74%, respectively. On the Vaihingen and Potsdam datasets, CFFormer also achieves the best results, with mIoU and OA values of 84.31%/91.88% and 88.62%/92.64%, respectively. The source code is available athttps://github.com/masurq/CFFormer.

As one of the most important means for earth observation, synthetic aperture radar (SAR) ship detection has been playing an increasingly important role recently. However, the accuracy of one-stage detectors is relatively low when ships are densely arranged in SAR images. It means that current detectors are insufficient to meet the application requirements. In this paper, an improved YOLOv5 detector, which is based on the attention mechanism and upsampling operator, is proposed. We adopt the ECA attention mechanism to solve the issue of insufficient feature extraction capability for ship targets. Besides, we propose the CARAFE upsampling operator to enhance the proportion of ship detail information in reconstructed feature maps. Experiments on the SAR ship detection dataset (SSDD) demonstrate that the detection P, R, and mAP metrics have improved by 4.1%, 4.8%, and 2% compared with the original YOLOv5 algorithm.

Efficient classification is crucial to mastering wetland land cover types and facilitating conservation. Due to the advantages of Synthetic Aperture Radar (SAR) imaging, it enables continuous monitoring and penetration through vegetation canopies. In general, wetland types in SAR imagery mainly rely on backscattering, which hard to distinguish and differentiate land cover using single features. Effectively leveraging SAR features can improve classification accuracy in wetlands. However, there is rarely research discussing feature effects in classification. In this paper, physical and data-driven-based feature extraction methods are analyzed in wetland classification. Firstly, physical and data-driven models are combined for feature extraction. Furthermore, the performance of common classifiers is compared to evaluate the effectiveness of different feature types, such as Support Vector Machine (SVM), Random Forest(RF), and Extreme Gradient Boosting(XGB). The experimental results indicate that the combined feature extraction method of the physical and data-driven model performs the best in classifying the Yellow River Delta. It also improves the accuracy of all the classifiers in the comparative experiment. Notably, the RF classifier achieves the highest classification accuracy, with an overall accuracy(OA) of 94% and a Kappa coefficient of 0.93.

Sea ice is regarded as an indicator of temperature change. In recent years, the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technology has made remarkable progress in sea ice detection. Delay-Doppler maps (DDMs) as one of significant observations can reflect different characteristics for sea ice and open water, and a single DDM is usually viewed as the unit of feature extraction; however, it is easily influenced by wave height, wind and other factors. Therefore, this paper proposes building scene semantic objects to enhance the reliability of observation and reflect the object characteristics. The synergism between DDMs and the spatial correlation of specular points was considered. Afterwards, histogram features were extracted to express the distribution of scattered energy. The random forest (RF) model was developed to distinguish sea ice from open water. The performance of the method by using TechDemoSat-1 (TDS-1) dataset was evaluated with the sea ice concentration products provided by OSISAF. The results show that the overall accuracy is 98.17%, which outperforms traditional observation methods.

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.

The natural surface soil is often mixed with a lot of sand and gravel. In this paper, a new soil moisture inversion method for gravel areas from polarimetric SAR (PolSAR) data is proposed. First, the backscattering of gravel areas is divided into two parts: surface scattering and volume scattering, which are obtained by the polarimetric decomposition method. For the volume scattering part, the Dense Medium Radiative Transfer (DMRT) model is used to obtain the soil moisture, and the Advanced Integral Equation Model (AIEM) and the Oh model are used for the surface scattering part. Finally, the weighted sum of the inversion results of the two parts is taken as the final inversion result. The accuracy of the proposed method was evaluated by field soil moisture data from Wuhai city, Inner Mongolia and ALOS-2 PolSAR data.

A new flood extraction method which combines semi-automatic thresholding and change detection is proposed. First, a line across land and water is drawn manually. The locally optimal threshold is calculated automatically along the line from two endpoints to middle. Using this threshold, the low backscattering regions are extracted to generate a preliminary flood map. Then, the neighborhood-based change detection method combined with the entropy thresholding is adopted to detect the changed area. Finally, pixels in both the low backscattering regions and the changed regions are marked as “flood”. The effectiveness and practicality of the flood extraction method was demonstrated by a set of Sentinel-1A data and ground truth data provided by the Copernicus Emergency Management Service (EMS).

Long-term continuous monitoring of coal mining activities is conducive to master the development and potential risk factors of the mining area. Radar reflected echo signal contains the intensity and phase information, which is suitable for detecting different types of mining areas. However, relevant coal mining detection research mainly focuses on phase information, which results in the application based on underground coal mines more than open-pit coal mines. In order to take full advantage of Synthetic Aperture Radar (SAR) data, in this paper, a novel mine detection method based on intensity and phase information is introduced to detect the underground and open pit coal mine using the multi-temporal ALOS data in Shenmu County, China. Firstly, Interferometric SAR (InSAR) technology is used to detect the deformation information caused by underground coal mine. Secondly, the coherence information is used to detect the deformation from both underground and open-pit coal mine. Moreover, change detection method based on intensity information is used to detect the open-pit coal mining. Finally, the results of phase, coherence and intensity are combined to detect the underground and open-pit coal mine. The experimental results of Shengdong mine show the effectiveness of the proposed method.

The mean shift algorithm shows a good performance in optical image segmentation. However, conventional mean shift algorithm performs poorly if it is used directly to synthetic aperture radar (SAR) image due to the large dynamic range and strong speckle noise. Recently, a generalized mean shift (GMS) algorithm with an adaptive variable asymmetric bandwidth was proposed for polarimetric SAR (PolSAR) image filtering. In this paper, it is further developed and extended for PolSAR image segmentation. The proposed algorithm can be used for PolSAR image superpixel segmentation directly without any preprocessing steps. Experiments using AirSAR and ESAR L-band PolSAR data demonstrate the effectiveness of the proposed superpixel segmentation algorithm.

The simple linear iterative clustering (SLIC) algorithm shows good performance in superpixel generation for optical imagery. However, SLIC can perform poorly when there is too much noise in the image. To solve this problem, we have improved the cluster center initialization step and the postprocessing step, and then introduce the SLIC superpixel segmentation algorithm to the polarimetric synthetic aperture radar (PolSAR) image processing field. Experiments using AirSAR and ESAR L-band PolSAR data show that the improved SLIC algorithm can overcome the effect of speckle noise in PolSAR imagery, and it shows a better performance in detail preservation than the original SLIC algorithm and the normalized cuts superpixel segmentation algorithm.

This paper proposes a polarimetric homogeneity measurement and applies it to the speckle filtering of polarimetric synthetic aperture radar (PolSAR) data. First, a line-and-edge (LAE) detector that can detect both the lines and edges in one scan is developed based on the traditional edge detector. A polarimetric homogeneity measurement is then derived by combining the equivalent number of looks and the LAE maps and is used to distinguish the homogeneous and heterogeneous regions. Finally, a new adaptive-window PolSAR filtering algorithm based on the LAE detector and the polarimetric homogeneity measurement is proposed. The proposed speckle filter adjusts the filtering windows in both shape and size, based on the homogeneity and gradient information. Consequently, it uses small and nonsquare windows in heterogeneous regions to preserve the detail information and uses large and square windows in homogeneous regions to maximize the suppression of speckle noise. EMISAR and ESAR L-band PolSAR data were used to demonstrate the effectiveness of the proposed filter in speckle suppression, detail preservation, and polarimetric information preservation.

The statistical region merging (SRM) algorithm exhibits efficient performance in solving significant noise corruption and does not depend on the data distribution. These advantages make SRM suitable for the segmentation of synthetic aperture radar (SAR) images, which are characterized by speckle noise and different distributions of various data types and spatial resolutions. However, the original SRM algorithm is designed for RGB and gray images characterized by additive noise and having a range of [0, 255]. In this letter, the SRM algorithm is generalized so that it can be applied to images with larger range and multiplicative noise. The original 4-neighborhood models are also generalized into 8-neighborhood models. The effectiveness of the generalized SRM (GSRM) algorithm is demonstrated by AirSAR and ESAR L-band Polarimetric SAR (PolSAR) data. Given that the input data of the GSRM algorithm can be single- or multi-dimensional, the proposed GSRM algorithm can be used for single- and multi-polarized as well as for fully polarimetric SAR data.

The mean shift algorithm, which uses a moving window and utilizes both spatial and range information contained in an image, is widely employed in digital image filtering and segmentation. However, because of the large dynamic range of synthetic aperture radar (SAR) images, applying the conventional mean shift algorithm directly to SAR image filtering will not produce meaningful results. This paper proposes an adaptive variable asymmetric bandwidth selection approach to be used in a newly derived generalized mean shift algorithm. The proposed mean shift algorithm is very versatile and can be used for SAR and polarimetric SAR (PolSAR) image filtering directly without any preprocessing steps. Monte Carlo-simulated PolSAR data are used to demonstrate the effectiveness of the proposed algorithm in speckle filtering by comparing it with other filters. Experimental Synthetic Aperture Radar (ESAR) L-band and Radarsat-2 C-band PolSAR data are used to evaluate its ability to preserve the polarimetric information of PolSAR data. The effects of initial value estimating and multilook processing on the filtered results are discussed at the end of this paper.