Multi-source 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 paper, we propose a universal cross-fusion transformer framework (CFFormer) for the semantic segmentation of multi-source 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 multi-head cross-attention mechanism to interact globally and fuse features from the different modalities, enabling the comprehensive utilization of the complementary information in multi-source RSIs. Finally, comparative experiments demonstrate that the proposed CFFormer framework not only achieves a state-of-the-art accuracy but also exhibits outstanding robustness when compared to the current advanced networks for semantic segmentation of multi-source RSIs. Specifically, CF-Former 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 at https://github.com/masurq/CFFormer.

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method.

To better interpret the scene and facilitate the subsequent processing of large-scale point cloud, clustering is often implemented in the preprocessing stage. However, when the original density-based spatial clustering of application with noise (DBSCAN) approach is used for point cloud clustering, it is easy to categorize closely spaced vegetation points and nonvegetation points into the same cluster by mistake. Aiming at the problem, this article presents an improved DBSCAN by embedding a strategy of class constraint, which is called CC-DBSCAN. Specially, based on the RGB and label information of each point in the training samples, by using the logistic regression model, the logistic regression color index (LRCI) is calculated for each point in the clustering samples. Then, points to be clustered are classified as vegetation points and nonvegetation points through the LRCI. Furtherly, the class information of point is introduced as a constraint for ensuring the core point and its directly density-reachable points belong to the same class, thus, solving the problem that confusion cluster of the adjacent vegetation points and nonvegetation points. We evaluate our approach on the benchmark SensatUrban dataset, where Cambridge_28 scene dataset is taken as the training set and Cambridge_18 scene dataset is as the dataset to be clustered. Experimental results show that our method achieved 97.20% purity of point cluster, which outperforms the other DBSCAN methods. At the same time, it takes only 24.25 s for clustering 2 million points, which indicates that CC-DBSCAN has high computational efficiency and good practicability.

The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction ($f_{veg}$) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination ($R^2$) about 0.7390.