Increasing attention has been garnered by LiDAR points and multi-view images fusion based on Transformer for supplementing another modality in 3D object detection. However, challenges persist for cross-modal fusion methods due to the heterogeneity of these two modalities, leading to issues such as inaccurate detection results encountered by Transformer-based methods. In this work, a one-way mid-level fusion based framework for 3D object detection named Modal Fusion Transformer (MFT) using LiDAR points and multi-view images is introduced. It comprises a Depth-Guided Generation(DGG) module, Position Encoding Generation (PEG) module and Cross Modal Fusion(CMF) module. Specifically, depth information from point cloud is utilized for both gathering the image depth map and initializing object queries in DGG. PEG unifies the form of position encoding from LiDAR features and multi-view image features. Depth and position information of object from images is aggregated to point clouds by CMF, which fully explores dual-modal information. Furthermore, a Modal Fusion Network with deformable attention named fast-MFT is introduced to reduce the relatively large computational cost associated with global attention. Our MFT and fast-MFT achieve competitive performance while maintaining a faster inference speed than other models.

Increasing attention has been garnered by LiDAR points and multi-view images fusion based on Transformer to supplement another modality in 3D object detection. However, most current methods perform data fusion based on the entire scene, which entails substantial redundant background information and lacks fine-grained local details of the foreground objects to be detected. Furthermore, global scene fusion results in coarse fusion granularity, and the excessive redundancy leads to slow convergence and reduced accuracy. In this work, a novel Dual Scenes Transformer pipeline (DSTR), which comprises a Global-Scene Integration (GSI) module, Local-Scene Integration (LSI) module and Dual Scenes Fusion (DSF) module, is presented to tackle the above challenge. Concretely, features from point clouds and images are utilized for gathering the global scene information in GSI. The insufficiency issues of global scene fusion are addressed by extracting local instance features for both modalities in LSI, supplementing GSI in a more fine-grained way. Furthermore, DSF is proposed to aggregate the local scene to the global scene, which fully explores dual-modal information. Experiments on the nuScenes dataset show that our DSTR has state of the art (SOTA) performance in certain 3D object detection benchmark categories on validation and test sets.

ABSTRACT In high‐precision image segmentation tasks, even slight deviations in the segmentation results can bring about significant consequences, especially in certain application areas such as medical imaging and remote sensing image classification. The precision of segmentation has become the main factor limiting its development. Researchers typically refine image segmentation algorithms to enhance accuracy, but it is challenging for any improvement strategy to be effectively applied to images of different objects and scenes. To address this issue, we propose a two‐step refinement method for image segmentation, comprising region expansion and minor contour adjustments. First, we design an adaptive gradient thresholding module to provide gradient‐based constraints for the refinement process. Next, the region expansion module iteratively refines each segmented region based on colour differences and gradient thresholds. Finally, the minor contour adjustments module leverages local strong gradient features to refine the contour positions further. This method integrates region‐level and pixel‐level information to refine various image segmentation results. This method was applied to the BSDS500, Cells, and WHU Building datasets. The results demonstrate that the refined closed contours align more closely with the ground truth, with the most notable improvement observed at contour inflection points (corner points). Among the results, the Cells dataset showed the most significant improvement in segmentation accuracy, with the F‐score increasing from 87.51% to 89.73% and IoU from 86.83% to 88.40%.

Interferometric Synthetic Aperture Radar (InSAR) has become a widely used and efficient tool for monitoring large-scale, long-term land subsidence. Most applications, especially those related to risky assessment, utilize only the mean deformation rate derived through a linear fit of the InSAR-derived time series deformations without considering the full-time sequence. In this way, the embedded information, such as the onset, duration, and patterns of abnormal deformations, is ignored. This information, however, should not be compromised as it is critical for assessing the risky deformations and recognizing the causal factors. Due to the large data volume, processing the full-time-series information derived by InSAR analysis can be a challenge. In this study, we propose to use the Time-series Anomaly Detection Generative Adversarial Network (TadGAN) to deal with the time series InSAR deformation and recognize the onset and duration of abnormal epochs. The proposed method has been tested with InSAR-derived results over the Hong Kong airport region. It performs better than conventional risk assessment methods, such as the one based on Mean Absolute Deviation (MAD) outlier detection.

Interferometric Synthetic Aperture Radar (InSAR) has been recognized as an effective remote sensing tool for Earth Observation (EO) thanks to its capacity to penetrate clouds and forests. In the field of InSAR deformation monitoring, coherence is used as an indicator for the stability of the interferometric phase. Actually, interferometric coherence itself contains valuable information about land surface and can be used to classify land cover types. Currently, there are few studies about the possibility of retrieving forest canopy height from interferometric coherence, especially its multitemporal variation. In this study, we emphasize the relationship between multi-temporal InSAR coherence and forest canopy height by investigating the InSAR coherence variation of multi-temporal Sentinel-1 data regarding the tree height measured by the NASA Global Ecosystem Dynamics Investigation (GEDI). Results show that forest canopy height can be estimated by using the Random Forest (RF) regression model trained by samples of GEDI height and InSAR coherence variation. InSAR coherence performs better in estimating the forest canopy height than using other remote sensing data, such as multispectral reflectance from Sentinel2 and SAR backscatter intensity.

Analyzing road network subsidence and sinkholes is crucial for ensuring urban traffic and people's safety. InSAR technique is a non-contact measurement technique with high spatiotemporal resolution, wide monitoring range, and unaffected by road conditions, which is of great significance for the analysis of deformation hazards of man-made linear infrastructures, such as road networks and high-speed railways. In this study, we adopt 44 Sentinel-1A images from January 2022 to July 2023 to study the deformation of the road network in Longgang District, Shenzhen, China based on PS-InSAR. The road deformation is further analyzed associated with sinkhole information from field investigation. The results show that the deformation rate of the road network is between -38.3 mm/year and 16.9 mm/year, and the correlation coefficient between the top burial depth of the sinkholes and the maximum deformation rate is 0.58.

Interferometric synthetic aperture radar (InSAR) has been more and more applied in acquiring long-term deformation of land surface in a large coverage and is becoming a routine investigation technique. Validation of the InSAR results depends largely on thein situmeasurements. These measurements are usually point-wise and of high cost, as many sensors need to be set up for the long-term monitoring. In applications associated with a large area, qualitative and low-cost validation may be more necessary at the first place, which is still a challenge. In the recent decade, the crowdsourcing and volunteered geographic information (VGI) have been more and more accepted in the field of geoinformatics. Inspired by these, this letter proposes an InSAR crowdsourcing annotation system to integrate the InSAR displacements and the photographs uploaded by volunteers. We processed 119 Sentinel-1A data ranging from 2017 to 2021 to derive long-term displacements. Then, based on the InSAR displacements, task areas were selected and published to public via the system. Volunteers online accepted the task and uploaded photographs, indicating land displacements. In a coastal city, Shenzhen of China, 135 task areas were selected and published in total, and 1742 useful photographs were uploaded. The uploaded photographs were then inspected to validate and analyze the InSAR results. Post-analysis found some high correlation between the uploaded photographs with the InSAR alerting displacements. The proposed system is a prototype, and its interface and functions can be further extended in the future toward an effective and efficient alerting system for risking land deformations.

The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art algorithms adopt an approximate likelihood function to calculate the likelihood by replacing the true coherence matrix with its estimation, more specifically, the sample coherence matrix (SCM). However, this approximation has a drawback in that the coherence estimates are greatly biased when the coherence is low. In this study, we derive a new likelihood function without such an approximation. Accordingly, a DSI framework using this function for phase estimation and point selection is provided. In this framework, the new likelihood function serves as a cost function for phase estimation and a quality measure for DS selection. Its performance is investigated by experiments in a simulation study and a real-world case study using Sentinel-1 data over Shenzhen airport in China. The results reveal that the proposed DSI framework outperforms the existing state-of-the-art approaches in different scenarios, in terms of providing a more accurate estimation and improving DS density and coverage.

As a component of interferometric phase, atmospheric phase influences accuracy of millimetric land deformation measurement. Though Atmospheric Phase Screen (APS) can be estimated by Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology, they are pseudo without consideration of actual atmospheric state and topography. This paper researches into ineffective separation of atmospheric phase in PS-InSAR methods when applied to extended cloudy area, and proposes an improved PS-InSAR processing flow by introducing Weather Research Forecast (WRF) simulations to remove atmospheric phase before interferometric phase analysis of PSI technique to obtain accurate land deformation velocity.

Using conventional two-pass interferometry to generate DEM with high frequency SAR data may be confronted with the problem of poor coherence in areas covered by dense vegetation, while DEM generated from low frequency SAR data interferometry is less sensitive to the topographic relief. Therefore, this article aims at generating high quality DEM by fusing multi-frequency interferometric results obtained from two-pass interferometry by using Kalman filter. Meanwhile, a process of coregistration of multi-frequency SAR data which are of different pixel sizes and imaging geometries is proposed. And data from Envisat ASAR and TerraSAR-X are used for case study and the accuracy of fused DEM are verified by ASTER GDEM.

The sensor calibration accuracy is an important indicator of SAR satellite system's performance and data quality. Towards geometric and radiometric calibration of Radarsat- 2 satellite (RS2), this paper researched the procedure and key techniques of space-borne SAR field calibration, including corner reflectors' (CRs) design, CRs' arrangement in the field, acquirement of CRs' peak RCS value and the surveying scheme. Besides, RS2's calibration accuracy was validated, giving reference for its quantitative application.