Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs)

There has been an increase in the adoption of Linked Data and subsequently representing data in the form of knowledge graphs across a wide spectrum of domains. There has also been significant interest in the remote sensing community to publish Earth Observation data in the form of Linked Data. As the geospatial Linked Data cloud on the internet grows, there arises a need for efficient methods of exploratory analysis of such information-rich geospatial knowledge graphs. Knowledge graph representation of remote sensing scenes has proved to add significant value for effective mining of implicit information in addition to seamless integration with other data sources. This work is geared towards visual exploration of semantically enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs). In this paper, we propose and implement an interactive web-based interface to visually explore and interact with RSS-KGs using Cesium. The proposed interface seeks to visualize the know ledge graph in the form of nodes and edges, mapped over the remote sensing scene consisting of different land use land cover regions and their inferred characteristics in addition to their spatial relationships with one another. It is envisaged that visualization in the form of nodes and edges would aid in visually validating the spatial relations in the knowledge graph, thus enhancing the understanding of the geospatial knowledge graph from the end user perspective. We demonstrate the efficacy of the interface through the visual exploration of an enriched geospatial knowledge graph of a remote sensing scene captured during an urban flood event.
Localization of Subsurface Pipes in Radar Images by 3D Convolutional Neural Network and Kirchhoff Migration

Ground Penetrating Radar (GPR) is a promising tool for subsurface utility pipe detection due to its dense and highspeed 3D monitoring. However, because of enormous amount of radar data and difficulty of interpretation, inspection time and cost are the bottlenecks. In this research, a novel detection algorithm by the combination of 3D Convolutional Neural Network (3D-CNN) and Kirchhoff migration was proposed. The developed model estimated pipes' existences and directions. 3D-CNN utilized the 3D geometries of the pipes to achieve high classification accuracy compared to 2D-CNN. Kirchhoff migration was applied to localize pipes by extracting peaks. From the result of experimental field data, the algorithm provides the clear understandings of pipes' 3D positions and arrangement with reasonable calculation time.
Representation Learning of Remote Sensing Knowledge Graph for Zero-Shot Remote Sensing Image Scene Classification

Although deep learning has revolutionized remote sensing image scene classification, current deep learning-based approaches highly depend on the massive supervision of the predetermined scene categories and have disappointingly poor performance on new categories which go beyond the predetermined scene categories. In reality, the classification task often has to be extended along with the emergence of new applications that inevitably involve new categories of remote sensing image scenes, so how to make the deep learning model own the inference ability to recognize the remote sensing image scenes from unseen categories becomes incredibly important. By fully exploiting the remote sensing domain characteristic, this paper proposes a novel remote sensing knowledge graph-guided deep alignment network to address zero-shot remote sensing image scene classification. To improve the semantic representation ability of remote sensing-oriented scene categories, this paper, for the first time, tries to generate the semantic representations of remote sensing scene categories by representation learning of remote sensing knowledge graph (SR-RSKG). In addition, this paper proposes a novel deep alignment network with a series of constraints (DAN) to conduct robust cross-modal alignment between visual features and semantic representations. Extensive experiments on one merged remote sensing image scene dataset, which is the integration of multiple publicly open remote sensing image scene datasets, show that the presented SR-RSKG obviously outperforms the existing semantic representation methods (e.g., the natural language processing models and manually annotated attribute vectors), and our proposed DAN shows better performance compared with the state-of- the-art methods under different kinds of semantic representations.
A Novel Multi-Scan Joint Method for Slow-Moving Target Detection in the Strong Clutter via RPCA

Slow-moving target detection in strong clutter background is a critical issue for the ground-based radar system. To detect the slow-moving target effectively, a novel multi-scan joint target detection method via principal component analysis (RPCA) is proposed. For radar echoes, there are two useful properties: 1) Stationary ground clutters have low-rank property, since the clutters in adjacent scan intervals are almost similar; 2) Moving targets have the sparse characteristic, due to their variation of position and sparsely distributed. Thanks to these two properties, moving targets can be separated from the stationary clutters via RPCA. Compared with the moving target indicator (MTI) method, the experimental results demonstrate that the proposed algorithm not only can suppress clutters effectively, but also preserve the moving target as much as possible.
Tracking Water Limitation in Photosynthesis with Sun-Induced Chlorophyll Fluorescence

Sun-induced chlorophyll fluorescence has a mechanistic link to photosynthesis. It is therefore sensitive to subtle, stress-induced changes in photosynthetic activity. This study shows the evolution of the emission of sun-induced chlorophyll fluorescence (SIF) by a yellow mustard stand grown in plant boxes under varying water supply and under varying meteorological conditions, causing changes in the evaporative demand. Affected by both leaf biochemical processes and by changes in the plant canopy structure, we combined the fluorescence measurements with reflectance measurements to assess the effect of both components. A first result of this study shows that the biochemical component of the fluorescence emission decreases because of either a reduction in the water supply or an increase in the evaporative demand by the atmosphere.
Spectral Unmixing Using Autoencoder with Spatial and Spectral Regularizations

In this paper, we propose a novel approach for spectral unmixing based on unsupervised learning using autoencoder with Inhomogeneous Gaussian Markov random field (IGMRF) as prior for regularization. The decoder part of our autoencoder has linear weights making it a linear mixture model (LMM). The weights represent the endmember matrix that makes the hidden unit of autoencoder as abundances. IGMRF is used to apply spatial regularization on abundances that also preserves the discontinuities. To incorporate the spectral regularization, we use IGMRF priors on endmembers. In addition, we also apply the spatial and spectral regularizations on the given hyperspectral images (HSI). IGMRF parameters at every pixel locations are calculated using initial estimates of endmembers and abundances. We obtain both endmembers and their abundances by optimizing the loss function that consists of a data term and IGMRF prior terms. Experiments are performed on different noise level synthetic data and two real data (Jasper ridge and Urban). The results of the proposed approach are better when compared to existing state-of-the-art approaches.
Adversarial Fine-Grained Adaptation Network for Cross-Scene Classification

Domain adaptation is widely used in the field of remote sensing, which can transfer the existing knowledge to new tasks and promote performance. When applied in the field of scene classification, it can be called cross-scene classification. Previous cross-scene classification methods mainly consider the coarse-grained alignment in the global aspect, which may ignore the structures behind the data and lose the local information with respect to specific categories. To implement fine-grained alignment, we present an adversarial fine-grained adaptation network (AFGAN) which simultaneously captures the complex structures behind the data distributions to improve the discriminability and reduce the local discrepancy of different domains to align the relevant category distributions. Experimental results based on three existing scene classification datasets demonstrate the effectiveness of AFGAN.
Wolfgang-OP: A Robust Humanoid Robot Platform for Research and Competitions

We present our open humanoid robot platform Wolfgang. The described hardware focuses on four aspects. Firstly, the robustness against falls is improved by integrating 3D printed elastic elements. Additionally, a high control loop frequency is achieved by using new custom control electronics. Furthermore, a torsion spring is applied to reduce the torque on the knee joints. Finally, computational power is provided through the combination of different processors. The paper also presents the ROS-based software stack that is used in RoboCup.
On-Board Intelligent Processing for Remote Sensing Images Based on 20KG Micro-Nano Satellite

Micro-nano satellite has developed rapidly in recent years. It shares the advantages of small size, low power consumption, short development cycle, and the ability to complete complex space missions at a lower cost, which is important for scientific research, national defense and commercial fields. The great capacity of imaging data brings heavy pressure to the satellite-earth data transmission, therefore it is necessary and significant to develop intelligent processing on the satellite to transform remote sensing images into concerned information. In this paper, we introduce an integration technology on micronano satellite with high performance of resolution, coverage, intelligence, applicable flexibility and low cost. The designed micro-nano satellite is deployed with a small optical payload. The imaging capability is 5k pixels x5k pixels and the resolution is 1.6m@500km. We apply Commercial Off-The-Shelf (COTS) component equipped with deep learning algorithm to extract the information of target, including position, size, course, etc. We evaluate the proposed method on the ship detection task. The experimental results have indicated a great prospect for on-board intelligent processing on the micro-nano satellite.
Global Scale IB AMSR2 Vegetation Optical Depth at X-Band

Vegetation Optical Depth (VOD) plays an increasingly important role in studying global carbon, water and energy transformation [1], [2]. This study explores the performance of the X-MEB (X-band microwave emission of the biosphere) model at global scale. Similar to the L-MEB model, the X-MEB model, built by INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) Bordeaux, aims to retrieve VOD (referred to as IB X-VOD) at X-band. To avoid the ill-posed problem caused by retrieving two parameters of interest (soil moisture (SM) and VOD) from mono-angular and dual-polarized observations (AMSR2), which are strongly correlated, we used the ERA5 SM product as an input to the X-MEB inversion. At a first step, we produced global IB X-VOD in year 2015 using the parameters (soil roughness and effective scattering albedo) calibrated in the African continent and evaluated the retrieved X-VOD with three vegetation parameters including Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). The evaluation results indicate X-MEB model has a great potential for global VOD retrievals from AMSR2 satellite data.
Revisiting the Spatial Scale Effects on Remotely Sensed Evaporation

In recent years, there has been a push to increase agricultural productivity together with water efficiency. The most viable means to achieve this goal is by employing remote sensing technologies. Currently, multiple satellite platforms can provide the spatial information to crop water use models, each of them having diverse spatial resolutions and radiometric characteristics. Here we analyze the spatial scale effects on crop water use (via evaporation) from three satellite platforms (Landsat 8, Sentinel 2, and CubeSats) finding that each estimate shows different spatial variability and that flux aggregation can result in relative mean absolute error up to 120%.
Transient Reactivation of Kara-Bogaz-Gol Coastal Landslide, Modulated by Hydrological Forces Captured Using InSAR (Turkmenistan)

This paper presents a hydrologically modulated transient motion of world's largest active landslide occurring along the western shore of the Kara-Bogaz-Gol (KBG) lagoon of the Caspian Sea (CS) using Interferometric Synthetic Aperture Radar (InSAR) data. Here a dataset of Sentinel 1A/B IW SAR imagery acquired on ascending and descending modes between 2014 and 2020 has been processed to analyze the present-day evolution of this giant coastal landslide. The results shows that the eastern bank of the KBG lagoon (25x5 km) is horizontally sliding toward the lake at a rate of 25 mm/yr. The time series analysis reveals week-long accelerating slip events when the KBG reservoir water level reached its seasonal maximum. This study presents new information on the potential roles of the hydrological forces modulated by sea-level rises on the ancient landslides which originated during successive CS transgressions in the Late Glacial.
Boosting Hyperspectral Image Unmixing Using Denoising: Four Scenarios

We present an analysis of the influence of noise on the unmixing of hyperspectral data. We propose four scenarios to 1) investigate the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing and 2) study the relation between noise and different endmembers selection strategies. Experiments are conducted on a simu-1ated and a real datasets with a wide range of signal to noise ratios (from 10 to 50 dB).
Hydrological Big Data Prediction Based on Shared Weight Long Short-Term Memory

Hydrological big data was characterized by complexity and comprehensibility, There are massive data association relationships to be mined in hydrological big data. Hydrological forecasting is primary to flood prevention in China, and how to use hydrological big data to make accurate and efficient prediction has become the primary study. This study starts with the analysis of hydrological big data association relationship, captures the characteristics of hydrological data, and proposes shared weight Long Short- Term Memory(SWLSTM) to reduce the number of optimized variables, shorten the training time of SWLSTM, and improve prediction accuracy of hydrological big data. Taking the daily water level data of Tunxi Hydrological Station of Tunxi Basin in China in 2016 as experimental data, experimental results demonstrate that our approach is efficient and accurate.
A Target-to-Mechanism Mapping Network for Polsar Data Interpretation

Model-based decompositions are powerful tools for scattering mechanism interpretation of polarimetric synthetic aperture radar (PolSAR) data. By incorporating their refined physical scattering models and utilizing the excellent nonlinear data fitting capability of neural networks, a target-to-mechanism mapping network is proposed. Inputting the polarimetric features defined in the normalized polarimetric feature space, the proposed network outputs the normalized powers of four scattering components. Experimental studies demonstrate that the trained network on Pi-SAR X-band PolSAR data shows good interpretation performance and generality on the cross-observation perspective Pi-SAR X-band PolSAR data and the cross-frequency Radardat-2 C-band PolSAR data. In addition, the proposed approach has a fast interpretation speed.
An Unsupervised Change Detection Approach for Dense Satellite Image Time Series Using 3D CNN

Recent satellite missions have initiated a new era in the area of Satellite Image Time Series (SITS) analysis by providing a huge number of High Resolution (HR) spectral-temporal images. The availability of HR images opens a door to an unprecedented wide range of possibilities to produce and develop high resolution Land Cover (LC) and Land Cover Change (LCC) maps. The goal of this paper is to effectively use high spatio-temporal resolution images to generate LCC maps by defining a novel automatic and unsupervised deep learning method based on three-dimensional (3D) Convolutional Neural Network (CNN). The method extracts spatio-temporal information from long SITS by using a pre-trained 3D CNN, detects changes and locates them in space and time. Experiments have provided promising results over both Amazonia and Saudi Arabia in the period 2013–2017, and has been compared to the other well-known LCC detection method.
An Efficient Transmit Waveforms Design Under Constant Modulus Constraint

In this paper, the Multiple-input Multiple-output(MIMO) radar waveform design under constant modulus constraint is considered. The process of current transmit beampattern design is conducted by approximating the desired transmit beampattern and minimizing the cross-correlation sidelobes. This leads to hard optimization problem due to the nonconvexity of the objective function and constant modulus constraint. In this work, an efficient method is proposed to address those problem based on Majorization-Minimization(MM) method. Finally, we evaluate the performance of the proposed MM-based algorithm via numerical simulations.
Modified Deep Transformers for GNSS Time Series Prediction

Highly accurate time series prediction in the field of geodesy is both important and a demanding task. For this problem we have investigated the potentiality of deep transformers, a deep learning approach. We have slightly modified the original network architecture and the optimization procedure of this model and thus have created a deep learning regression framework. We have applied the method for time series prediction of more than 18000 GNSS stations. We show that this approach performs better than traditional statistical methods by 21.5% in prediction accuracy. Furthermore, we show that it outperforms other machine learning algorithms by at least 2.7%. We demonstrate that millimeter accuracy can be expected for the prediction.
Multi-objective Optimization in Optical Networks

Optical networks use very effectively the available fiber bandwidth and thus provide the technological platform for core networks supporting various services, e.g. 4G and 5G mobile systems. From the network operator point of view, the most important element of the optical network design process is the minimization of network resources and estimate the moment of congestion in terms of maximum network capacity. Hence, the main objective of this study is firstly the minimization of optical nodal resources, i.e. transponders and amplifiers. Consequently, the optimization problem we consider is multi-objective. Heuristic methods based on the evolutionary algorithm were proposed to solve the problem, which was compared with the reference algorithm. The results obtained show that there is an advantage in using heuristic algorithms for optimizing large optical networks. The numerical experiments were carried out for the network, which is typical of a realistic network.
Impacts of Ionospheric Effects on Spaceborne Single-Pass SAR Imaging and Interferometry of LuTan-1

The ionosphere is a significant source of the phase dispersion on the low frequency radar signal for spaceborne synthesis aperture radar (SAR). LuTan-1 (LT-1) is an innovative spaceborne bistatic SAR (BiSAR) operated in L-band, which will be launch in 2021. Studying the impacts of ionospheric effects on spaceborne single-pass imaging and interferometry is a necessary work to support the ground processing system of LT-1. In this paper, The geometric distortions caused by the ionosphere, including the offsets of the SAR image and Digital Elevation Model, are analyzed firstly. Then the deterioration of the imaging performance caused by the quadratic phase error and the cubical phase error is analyzed, and simulation results of the point target are given to verify. Finally, a framework of ionospheric effects correction for BiSAR is proposed.
Towards Robust Autonomous Coverage Navigation for Carlike Robots

Thanks to their high carrying capacity and strong maneuverability, carlike robots which move with non-holonomic constraints, are frequently utilized in numerous coverage operation fields. In such fields, the robots need to complete the coverage task via autonomous path planning and tracking, which is referred to as a “Coverage Navigation for Carlike Robots” (CNCR) problem. When it comes to CNCR, two folds of challenges are put forward as follows. On one hand, the local path planner should not only consider the non-holonomic constraints of carlike robots but also should meet the high coverage rate required by the coverage task. On the other hand, during CNCR, there must be many U-turns in narrow areas. As a result, Reeds-Shepp-type paths will be planned inevitably. Tracking this sort of multi-segment paths stably is also an urgent problem to be solved in CNCR. To this end, this letter introduces a reference line constraint and a heuristic initialization strategy along with a cubic parametric path smoother to the time elastic band planner and designs a multi-segment-supported path tracker. Experimental results show that the proposed path planner largely reduces the average deviation off the reference line and obtains a high success rate even in complex environments. Besides, the designed path tracker achieves stable and high-precision tracking for multi-segment paths. Real-world experiments reveal that the proposed path planner and tracker are fully competent for CNCR, rendering our sweeping robot to attain an actual average coverage rate of 92.21%. To make our results fully reproducible, relevant codes and data are made available online at https://github.com/csLinZhang/cncr.git.
Convertible Sparse Convolution for Point Cloud Instace Segmentation

Instance segmentation based on 3D point cloud is a key step in scene understanding. It is widely used in indoor robot navigation, outdoor autonomous driving, and other fields. But research in this area is still in its infancy. Instance segmentation not only needs to predict the semantic label of each point but also the instance label of each point. Therefore, semantic segmentation can be considered the basis of instance segmentation to some extent. Based on this motivation, we designed a voxel-based branch based on convertible sparse convolution and residual optimization modules. We design a point-based branch so that the network can maintain high-resolution representation. Then the two branches are combined to optimize the semantic segmentation results. Breadth-first search (BFS) performs well in indoor point clouds and is simple to operate. Therefore, we use this clustering operation to group the points of the same instance to obtain the instance segmentation result. The proposed method was tested on the indoor dataset Scan-Net v2 and achieved relatively good instance segmentation precision.