As we all know, phase unwrapping (PhU) is one of the key steps affecting interferometric synthetic aperture radar (InSAR) data processing. However, due to the residues, it is difficult to obtain ideal results in the areas with high noise and large-gradient changes. Therefore, how to effectively deal with residues becomes the top priority of the PhU. To address this issue, in this letter, a novel residue degenerate PhU (RDPhU) method is proposed. We use the fast iterative shrinkage thresholding algorithm (FISTA) to solve the residue degradation problem, which introduces a novel branch-cut strategy that can effectively prevent error propagation. To the best of our knowledge, FISTA is first applied to the PhU residues degradation problem. In addition, we introduce regularization theory into$L^{1}$-norm PhU to further improve the robustness of PhU. More interestingly, the RDPhU method can effectively solve the problem of low accuracy of PhU in the areas with large-gradient changes, while the PhU efficiency of the RDPhU method is greatly improved. Through simulation and TanDEM-X InSAR datasets, it is proved that the proposed method is an efficient and high-accuracy PhU method.

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.

Sea surface rainfall intensity is an important sea state parameter and has an important impact on marine navigation safety and global climate. At present, the methods for measuring sea surface rainfall intensity include rain gauge, weather radar and remote sensing. However, increasing the spatial and temporal resolution and decreasing the cost are still a challenge. GNSS reflectometry (GNSS-R) is an emerging remote sensing technology, which may effectively handle the resolution and cost issues. In this paper, by making use of spaceborne GNSS-R data, three models based on random forest are developed for retrieving sea surface rainfall intensity. The normalized bistatic radar cross section (NBRCS), the leading edge slope (LES), and the signal to noise ratio (SNR) from the CYGNSS are used as the key variables for the rainfall intensity retrieval. The results show that the model only considering wave effects has the best accuracy, with the coefficient of determination (fi2) of 0.79 and the root mean square error (RMSE) of 1.05 mm/hr. Compared with the model without considering wind and wave effects, the fi2improves by 12.9% and the RMSE improves by 16%. The results also show that the effect of wind on rainfall intensity retrieval should not be considered, if the effect of wave is already considered.

Automatic extraction of building footprints from images is one of the vital means for obtaining building footprint data. However, due to the varied appearances, scales, and intricate structures of buildings, this task still remains challenging. Recently, the vision transformer (ViT) has exhibited significant promise in semantic segmentation, thanks to its efficient capability in obtaining long-range dependencies. This article employs the ViT for extracting building footprints. Yet, utilizing ViT often encounters limitations: extensive computational costs and insufficient preservation of local details in the process of extracting features. To address these challenges, a network based on an enhanced ViT (EViT) is proposed. In this network, one convolutional neural network (CNN)-based branch is introduced to extract comprehensive spatial details. Another branch, consisting of several multiscale enhanced ViT (EV) blocks, is developed to capture global dependencies. Subsequently, a multiscale and enhanced boundary feature extraction block is developed to fuse global dependencies and local details and perform boundary features enhancement, thereby yielding multiscale global-local contextual information with enhanced boundary feature. Specifically, we present a window-based cascaded multihead self-attention (W-CMSA) mechanism, characterized by linear complexity in relation to the window size, which not only reduces computational costs but also enhances attention diversity. The EViT has undergone comprehensive evaluation alongside other state-of-the-art (SOTA) approaches using three benchmark datasets. The findings illustrate that EViT exhibits promising performance in extracting building footprints and surpasses SOTA approaches. Specifically, it achieved 82.45%, 91.76%, and 77.14% IoU on the SpaceNet, WHU, and Massachusetts datasets, respectively. The implementation of EViT is available athttps://github.com/dh609/EViT.

The Global Navigation Satellite Systems (GNSS) water vapor tomography technique has been successfully used as a promising tool for sensing atmospheric water vapor and applied to weather forecasting in recent years. In most tomography models, the single grid resolution, i.e., the same horizontal resolution, is widely adopted to divide the three-dimensional (3D) tomographic domain into many small voxels. However, the single resolution GNSS tomography (SRGT) method implements the grid-based parametrization of the physical domain and does not follow the vertical spatial heterogeneity of atmospheric water vapor. To this end, we develop a new multi-resolution GNSS tomography (MRGT) method that incorporates the vertical decline tendency of water vapor. The MRGT method generates different resolution tomographic water vapor products in the lower, middle, and upper domains of the troposphere. Besides, a new indicator, known as the integrated water vapor (IWV) lapse rate, is introduced to determine the appropriate non-uniform stratification strategy. Eight tomography schemes were analyzed to compare the tomography results obtained from different tomography models based on the GNSS data in Hong Kong region during June and July 2015. The results show that with respect to radiosonde data, the MGRT method reconstructs a more accurate water vapor distribution than the SRGT approach, with the average root mean square error of tomographic results reduced by 12%. Moreover, in rainfall conditions, the tomographic water vapor profiles from the MGRT model agree well with the radiosonde profiles, which highlights the potential of multi-resolution tomographic water vapor products for rainfall-related studies.

Accurately extracting buildings from high-resolution remote sensing images is important to obtain urban information, and promote the development of smart cities. At present, the knowledge-driven building extraction methods mostly rely on building prior knowledge of manual design, resulting in low automation and poor versatility. The data-driven methods usually rely too much on training samples, resulting in insufficient pertinence for the features of building extraction and low generalization ability of the model. Therefore, this study proposes a novel buildings edge-aware refined deep learning network (BEARNet) for building extraction from high-resolution remote sensing images. The network takes the building edge as a priori knowledge, learns the building edge features by decoupling the building body and edge, and further optimizes the network by combining the multi-objective loss function to strengthen the pertinence of building edge features extraction. Experimental results show that on the WHU building dataset, which is less difficult to extract buildings, compared with other methods, BEARNet has the highest Precision, F1, IoU and OA values of 97.70%, 97.42%, 95.3% and 98.67%. On the Massachusetts building dataset, where building extraction is difficult, BEARNet has the highest Precision, Recall, F1, IoU and OA compared to other methods, with values of 84.92%, 85.27%, 85.09%, 75.82% and 93.99%, respectively. Our proposed method is more accurate in extracting complex shapes and dense small-scale buildings, and the building edges are more refined and complete.

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.

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).

In recent years, global navigation satellite systems-reflectometry (GNSS-R) technology has been increasingly considered for applications in sea surface monitoring. This paper presents a new method to retrieve the density of sea surface green algae by using the reflected signals of geostationary Earth orbit (GEO) satellites collected by shipborne receiver. Because GEO satellites are stationary relative to a fixed receiver on the earth’s surface, the reflected GEO satellite (GEO-R) signals are not affected by Doppler frequency or elevation angle, which can greatly simplify the modeling of the reflected power and realize continuous green algae monitoring in the same area. Specifically, the influence of green algae on GEO-R power through varying reflection coefficient and roughness was analyzed. Then, an empirical model was established to retrieve the green algae density by using the GEO-R power. Finally, the experimental data collected in the Qingdao Jiaozhou bay were used to verify the developed models, and the results show that the inversion accuracy of the green algae density model is better than 4%.

This letter presents a multiscale convolutional neural network with color vegetation indices (MCCNN) for semantic labeling of point cloud directly in a 3-D model. First, color vegetation indices are calculated for each point with RGB information. Second, based on classic Point convolutional neural network (CNN), a new multiscale network is designed to incorporate multiscale information through the spatial contexts of different sizes around each point by setting different convolution of kernels$K$, and then multiscale features produced by different convolutional layers are aggregated and unsampled. Finally, via Fully Connection layer and Softmax classifier, each point is labeled. Two different datasets, Semantic3D and Vaihingen3D, are used to evaluate the performance of the proposed method, and the results are compared with those produced by other existing approaches. Experimental results indicate that the proposed method achieves 84.5% in terms of overall accuracy on Semantic3D, and 85.2% on Vaihingen3D, which is the highest among the considered methods.

Due to the continuous intensification of human activities in the ocean, the frequent outbreaks of red tide have caused great harm to the marine environment and ecology. Thus, the rapid detection and monitoring of red tide become particularly important. At present, the main monitoring methods depend on artificial and buoy data, as well as optical satellite remote sensing. However, these methods may not be able to effectively deal with the characteristics of red tide bloom, such as suddenness and unpredictability. The global navigation satellite system-reflectometry (GNSS-R) is an emerging technology that makes use of navigation signals as a remote sensing opportunity to obtain Earth surface information. GNSS-R has already been proved to be capable of retrieving sea surface parameters (e.g., dielectric constant and sea surface roughness) closely related to the outbreak of a red tide. In this article, we proposed a new method to estimate red tide density, which utilizes an all-new model associating GNSS-R observations with sea surface red tide density. This method can remove the weather influence and greatly decrease the revisit period, which is much longer for optical red tide remote sensing methods. The Landsat-8 near-infrared data and TechDemoSat-1 (TDS-1) GNSS-R data of a red tide outbreak in the sea off the Tsingtao coast in China are used to build and test the proposed method. The results demonstrate that the correlation coefficient is 0.73, and the root mean square error of retrieved red tide density is 2.84%, which shows that the GNSS-R technology shows great potential to perform the rapid and preliminary red tide monitoring and judgment.

High spatiotemporal resolution atmospheric water vapor can be retrieved using the Global Navigation Satellite System (GNSS) tomography technique, in which the remained ill-posed problem of the tomography system resulting from the acquisition geometry is a vital issue to be addressed. Remote sensing (RS) water vapor data, with high-resolution and global coverage, show great potential for retrieval of slant water vapor (SWV) observations to improve the tomographic geometrical distribution. In this article, we develop a GNSS-RS (GNSS combining RS) tomography model to fully exploit the value of observation signals from GNSS and RS measurements. The two key factors of retrieving the RS SWV are performed by calibrating the original precipitable water vapor (PWV) images and adding the tropospheric horizontal gradients. The results reveal that when introducing the RS SWV observations into the tomography model, the acquisition geometry is significantly improved, with the average rate of voxels crossed by rays from 62% to 95% and the mean number of observation signals from 395 to 508 during the tomographic periods. Independent radiosonde data are used to validate the tomographic water vapor fields. The mean root-mean-square error (RMSE) and bias of the water vapor profiles derived from GNSS-RS solutions are decreased by 28% and 45% with respect to the GNSS-only results, respectively. Such improvements highlight that GNSS-RS troposphere tomography has significant potential to improve the reconstruction of the atmospheric water vapor fields.