Heterogeneous Satellite Research Articles

Big Remote Sensing Image Classification Based on Deep Learning Extraction Features and Distributed Spark Frameworks

Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods.

Collaborative mission optimization for ship rapid search by multiple heterogeneous remote sensing satellites

<p style='text-indent:20px;'>Multiple heterogeneous satellites mission optimization is a typical kind of non-deterministic polynomial-time hard (NP-hard) problem, and some complicated scenarios bring new challenges. A novel method of missing ship rapid search using multiple grouped heterogeneous satellites is introduced in this paper. The focus is on optimization of collaborative mission optimization for various satellites including low-earth orbit (LEO) satellite and geostationary orbit (GEO) satellites. A fast coverage of the wide sea area using imaging satellites with narrow coverage range has become the most important part to tackle this problem. However, due to different imaging mechanisms of heterogeneous satellites and other constraints, it brings a great challenge to construct the optimization model. A constrained optimization problem model considering the cooperation between LEO and GEO satellites is constructed. A solution strategy based on bi-level metaheuristic algorithm is designed. The time optimal solution of the collaborative task planning between LEO and GEO satellites can be obtained based on the optimal attitude maneuver path of GEO satellites. Thus, wide-area search for missing ships can be conducted in an effective way. The effectiveness of the proposed method is verified by an example.</p>

On the Generalization Ability of a Global Model for Rapid Building Mapping from Heterogeneous Satellite Images of Multiple Natural Disaster Scenarios

Post-classification comparison using pre- and post-event remote-sensing images is a common way to quickly assess the impacts of a natural disaster on buildings. Both the effectiveness and efficiency of post-classification comparison heavily depend on the classifier’s precision and generalization abilities. In practice, practitioners used to train a novel image classifier for an unexpected disaster from scratch in order to evaluate building damage. Recently, it has become feasible to train a deep learning model to recognize buildings from very high-resolution images from all over the world. In this paper, we first evaluate the generalization ability of a global model trained on aerial images using post-disaster satellite images. Then, we systemically analyse three kinds of method to promote its generalization ability for post-disaster satellite images, i.e., fine-tune the model using very few training samples randomly selected from each disaster, transfer the style of postdisaster satellite images using the CycleGAN, and perform feature transformation using domain adversarial training. The xBD satellite images used in our experiment consist of 14 different events from six kinds of frequently occurring disaster types around the world, i.e., hurricanes, tornadoes, earthquakes, tsunamis, floods and wildfires. The experimental results show that the three methods can significantly promote the accuracy of the global model in terms of building mapping, and it is promising to conduct post-classification comparison using an existing global model coupled with an advanced transfer-learning method to quickly extract the damage information of buildings.

Performance Evaluation Indicators of Space Dynamic Networks under Broadcast Mechanism

Large-scale heterogeneous constellations will be the major forms of future space-based systems, and the implementation of numerous derived applications depends mainly on intersatellite communication. The nodes representing heterogeneous satellites will form the networks with rapidly changing topology. However, few researches have been carried out for such networks. This paper studies the broadcast mechanism for space dynamic networks and establishes centralized and distributed routing framework. And then, performance evaluation indicators are proposed to evaluate both the connectivity of dynamic networks and the effectiveness of routing algorithms. Finally, we examine the performance of multigroup networks and verify the rationality of corresponding indicators. We also explore the impact of information survival time which directly affects the delivery ratio and, if unfortunately, may waste the communication resources. Empirical conclusion about the survival time is given in the final part. We believe the performance indicators and the routing algorithms proposed in this paper are great help to future space-based system and both the broadcast mechanism designing.

Computing Energy Efficiency for a Cognitive Heterogeneous Satellite Network Based on Interference Management

Computing Energy Efficiency for a Cognitive Heterogeneous Satellite Network Based on Interference Management

On Global Quantum Communication Networking.

Research in quantum communications networks (QCNs), where multiple users desire to generate or transmit common quantum-secured information, is still in its beginning stage. To solve for the problems of both discrete variable- and continuous variable-quantum key distribution (QKD) schemes in a simultaneous manner as well as to enable the next generation of quantum communication networking, in this Special Issue paper we describe a scenario where disconnected terrestrial QCNs are coupled through low Earth orbit (LEO) satellite quantum network forming heterogeneous satellite–terrestrial QCN. The proposed heterogeneous QCN is based on the cluster state approach and can be used for numerous applications, including: (i) to teleport arbitrary quantum states between any two nodes in the QCN; (ii) to enable the next generation of cyber security systems; (iii) to enable distributed quantum computing; and (iv) to enable the next generation of quantum sensing networks. The proposed QCNs will be robust against various channel impairments over heterogeneous links. Moreover, the proposed QCNs will provide an unprecedented security level for 5G+/6G wireless networks, Internet of Things (IoT), optical networks, and autonomous vehicles, to mention a few.

Reinforcement Learning Based Capacity Management in Multi-Layer Satellite Networks

The development of satellite networks is drawing much more attention in recent years due to the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, the satellite network is a three-layer heterogeneous network of high complexity, for which comprehensive theoretical analysis is still missing. In this paper, we investigate the problem of capacity management in the three-layer heterogeneous satellite network. We first construct the model of the network and propose a low-complexity method for calculating the capacity between satellites. Based on the time structure of the time expanded graph, the searching space is greatly reduced compared to traditional augmenting path searching strategies, which can significantly reduce the computing complexity. Then, based on Q-learning, we proposed a long-term optimal capacity allocation algorithm to optimize the long-term utility of the system. In order to reduce the storage and computing complexity, a learning framework with low-complexity is constructed while taking the properties of satellite systems into account. Finally, we analyze the capacity performance of the three-layer heterogeneous satellite network and also evaluate the proposed algorithms with numerical results.

Mission and system architecture for an operational network of earth observation satellite nodes

Nowadays, constellations and distributed networks of satellites are emerging as clear development trends in the space system market to enable augmentation, enhancement, and possibilities of new applications for future Earth Observation (EO) missions. While the adoption of these satellite architectures is gaining momentum for the attaining of ever more stringent application requirements and stakeholder needs, the efforts to analyze their benefits and suitability, and to assess their impact for future programmes remains as an open challenge to the EO community. In this context, this paper presents the mission and system architecture conceived during the Horizon 2020 ONION project, a European Union research activity that proposes a systematic approach to the optimization of EO space infrastructures. In particular, ONION addressed the design of complementary assets that progressively supplement current programs and took part in the exploration of needs and implementation of architectures for the Copernicus Space Component for EO.Among several use cases considered, the ONION project focused on proposing system architectures to provide improved revisit time, data latency and image resolution for a demanding application scenario of interest: Marine Weather Forecast (MWF).A set of promising system architectures has been subject of a comprehensive assessment, based on mission analysis expertise and detailed simulation for evaluating several key parameters such as revisit time and data latency of each measurement of interest, on-board memory evolution and power budget of each satellite of the constellation, ground station contacts and inter-satellite links. The architectures are built with several heterogeneous satellite nodes distributed in different orbital planes. Each platform can embark different instrument sets, which provide the required measurements for each use case.A detailed mission analysis has then been performed to the selected architecture for the MWF use case, including a refined data flow analysis to optimize system resources; a refined power budget analysis; a delta-V and a fuel budget analysis considering all the possible phases of the mission. This includes from the correction of launcher injection errors and acquisition of nominal satellite position inside the constellation, orbit maintenance to control altitude, collision avoidance to avoid collision with space debris objects and end-of-life (EOL) disposal to comply with EOL guidelines.The relevance of the system architecture selected for the MWF has been evaluated for three use cases of interest (Arctic sea-ice monitoring, maritime fishery pressure and aquaculture, agricultural hydric stress) to show the versatility and the feasibility of the chosen architecture to be adapted for other EO applications.

Using Heterogeneous Satellites for Passive Detection of Moving Aerial Target

Passive detection of a moving aerial target is critical for intelligent surveillance. Its implementation can use signals transmitted from satellites. Nowadays, various types of satellites co-exist which can be used for passive detection. As a result, a satellite signal receiver may receive signals from multiple heterogeneous satellites, causing difficult in echo signal detection. In this paper, a passive moving aerial target detection method leveraging signals from multiple heterogeneous satellites is proposed. In the proposed method, a plurality of direct wave signals is separated in a reference channel first. Then, an adaptive filter with normalized least-mean-square (NLMS) is adopted to suppress direct-path interference (DPI) and multi-path interference (MPI) in a surveillance channel. Next, the maximum values of the cross ambiguity function (CAF) and the fourth order cyclic cumulants cross ambiguity function (FOCCCAF) correspond into each separated direct wave signal and echo signal will be utilized as the detection statistic of each distributed sensor. Finally, final detection probabilities are calculated by decision fusion based on results from distributed sensors. To evaluate the performance of the proposed method, extensive simulation studies are conducted. The corresponding simulation results show that the proposed fusion detection method can significantly improve the reliability of moving aerial target detection using multiple heterogeneous satellites. Moveover, we also show that the proposed detection method is able to significantly improve the detection performance by using multiple collaborative heterogeneous satellites.

Analysis of heterogeneous satellite networks with multi‐user detectors

SummaryIn this work, a heterogeneous network, where both terrestrial‐ and multiple spot beam‐based geosynchronous satellite networks use the same sub‐channel, is investigated. Moreover, the satellite uses same sub‐channel to serve users across all spot beams. Apart from inter spot beam interference, satellite users also receive strong interfering signal from the terrestrial base station. The satellite receivers manage the inter system interference (ie, interference from terrestrial network) while the inter spot beam interference is mitigated through precoding at the gateway. To be specific, the following interference‐aware detectors are used to mitigate the inter system interference: successive interference canceller (SIC) and joint decoder (JD). The proposed decoders are shown to achieve better data rate and bit error rate (BER) than the interference ignorant decoder at moderate to strong interference levels. Although JD and SIC achieve same sum rate at moderate and strong interference levels, JD is shown to allow the terrestrial user achieve higher data rate than SIC.

Interference management for spectral coexistence in a heterogeneous satellite network

SummaryWith the advent of the fifth generation of mobile radio communication by 2020, there will be many challenges such as increasing service demand with low delay in providing billions of end users called the satellite mobile users. It is expected that terrestrial communication systems will be faced with a dense network having many small cells anywhere and anytime. Therefore, there are some remote regions in the world where terrestrial systems cannot provide any services to end users. Furthermore, because of lack of spectral resources, it is very important that the spectrum is shared between satellite systems and terrestrial equipment by a suitable solution to interference management. In this paper, a heterogeneous satellite network that includes low earth orbit (LEO) satellite constellation and terrestrial equipment is proposed to provide low delay services. In this type of structure, interference management based on transmission power control between LEO satellite systems and mobile users is very important for obtaining high throughput. Moreover, in order to mitigate interference, transmission power control is shown based on noncooperative Stackelberg game under many subgames through pricing‐based algorithm and convex optimization method. Finally, the simulation results show that the performance of this study's system model will be improved through the proposed algorithm.

Assessment of Satellite Contacts Using Predictive Algorithms for Autonomous Satellite Networks

Upcoming Low Earth Orbit Satellite Networks will provide low-latency and high downlink capacity necessary for future broadband communications and Earth Observation missions. This architecture was proposed at the beginning of the 90's, although it has just recently re-gained popularity thanks to the so-called Mega-Constellations. This network is composed of satellites that have Inter-Satellite Links (ISL) to communicate between them. Due to the satellite motion, an ISL is a temporal contact between two satellites characterized by a lifetime in which the communication remains feasible. The determination of a route between distant satellites is a challenging problem in this context. However, the satellite follows a well-known deterministic orbit trajectory, being feasible the prediction of its position by propagating a trajectory model over time. The Contact Graph Routing protocol uses this feature to determine the evolution of the routes by pre-computing on-ground a planning of the satellite contacts. This centralized ground-dependent solution cannot be directly applied in the Internet of Satellites paradigm, which proposes the autonomous deployment of heterogeneous satellite networks without pre-assuming any specific satellite system architecture. Following this concept, the present work proposes a distributed algorithm by which a satellite predicts neighbor contacts, and generates a global contact plan without trajectory propagation. To achieve this solution, an ISL has been modeled as a “close approach” between two satellites, which is characterized by their relative motion. The present work details the predictive algorithm, and evaluates its performance in two scenarios with a hybrid satellite constellation and a mega-constellation.

The Next Generation Heterogeneous Satellite Communication Networks: Integration of Resource Management and Deep Reinforcement Learning

This article proposes an innovative resource management framework for the next generation heterogeneous satellite networks (HSNs), which can achieve cooperation between independent satellite systems and maximizing resource utilization. The key points of the proposed design lie in the architecture that supports the intercommunication between different satellite systems, and the SDN/NFV-based management offering the matching between resources and services. Based on the framework, we then apply deep reinforcement learning (DRL) into the system due to its strong ability in optimal matching. The two problems of multiobjective reinforcement learning and multiagent reinforcement learning are studied to adapt the development of the HSN. The combination of the DRL and resource allocation achieves integrated resource management across different satellite systems and achieves resource allocation in the HSN which can be implemented more flexibly and efficiently.

A Reliable Mixed-Norm-Based Multiresolution Change Detector in Heterogeneous Remote Sensing Images

Analysis of heterogeneous remote sensing image is a challenging and complex problem due to the fact that the local statistics of the data to be processed can be radically different. In this article, we present a novel and reliable unsupervised change detection (CD) method to analyze heterogeneous remotely sensed image pairs. The proposed method is based on an imaging modality-invariant operator that detects at different scale levels the differences in terms of high-frequency pattern of each structural region existing in the two heterogeneous satellite images. First, this new detector is based upon a dual-norm formulation that makes our underlying CD estimation particularly robust in terms of a sensitivity/specificity tradeoff. Second, the detection process, embedded in a multiresolution framework, allows us to estimate a robust similarity or difference map that is then filtered out by a superpixel-based spatially adaptive filter to further increase its reliability against noise. Finally, changes are then identified from this similarity map by a simple binary clustering process that also takes into account the spatial contextual information around each pixel. Experimental results involving different types of heterogeneous remotely sensed image pairs confirm the robustness of the proposed approach.

Multimodal Change Detection in Remote Sensing Images Using an Unsupervised Pixel Pairwise Based Markov Random Field Model.

This work presents a Bayesian statistical approach to the multimodal change detection (CD) problem in remote sensing imagery. More precisely, we formulate the multimodal CD problem in the unsupervised Markovian framework. The main novelty of the proposed Markovian model lies in the use of an observation field built up from a pixel pairwise modeling and on the bitemporal heterogeneous satellite image pair. Such modeling allows us to rely instead on a robust visual cue, with the appealing property of being quasi-invariant to the imaging (multi-) modality. To use this observation cue as part of a stochastic likelihood model, we first rely on a preliminary iterative estimation technique that takes into account the variety of the laws in the distribution mixture and estimates the parameters of the Markovian mixture model. Once this estimation step is completed, the Maximum a posteriori (MAP) solution of the change detection map, based on the previously estimated parameters, is then computed with a stochastic optimization process. Experimental results and comparisons involving a mixture of different types of imaging modalities confirm the robustness of the proposed approach.

Caching in heterogeneous satellite networks with fountain codes

SummaryIn this paper, we investigate the performance of caching schemes based on fountain codes in a heterogeneous satellite network. We consider multiple cache‐aided hubs, which are connected to a geostationary satellite through backhaul links. With the aim of reducing the average number of transmissions over the satellite backhaul link, we propose the use of a caching scheme based on fountain codes. We derive a simple analytical expression of the average backhaul transmission rate and provide a tight upper bound on it. Furthermore, we show how the performance of the fountain code‐based caching scheme is similar to that of a caching scheme based on maximum distance separable codes.

A Traveling Salesman Problem-Based Approach to Observation Scheduling for Satellite Constellation

This paper addresses observation task scheduling of a heterogeneous satellite constellation in low-Earth-orbits. The goal of scheduling is to find a sequence and times for observing the ground objects to maximize the sum of values of the observed ground objects while satisfying all the constraints associated with complex mission specifications. The method develops an instance of the asymmetric traveling salesman problem (ATSP) for this scheduling, and solves the resulting ATSP using a well-established Lin–Kernighan–Helsgaun algorithm. Numerical experiments demonstrate the characteristics, efficiency, and scalability of the proposed scheduling approach, in particular, compared to the first-in first-out strategy-based greedy algorithm.

Interference mitigation for a heterogeneous satellite network based on spatial angle and transmission power

Nowadays, with the explosive growth of users and devices, it is expected that new generation satellite system will support different communication demand around the world. Therefore, by increasing demand, hybrid satellite-terrestrial networks have been developed for achieving more coverage and capacity. Thus, in this paper, a LEO satellite constellation is deployed which includes LEO satellite systems and terrestrial equipment. Also, in order to manage the interference that is one of the main challenges in this network, Stackelberg game theory is used based on power control between LEO satellite systems and mobile users. To mitigate interference, two sub-games are provided based on pricing-based algorithm and convex optimization method. Finally, simulation results show that the performance of heterogeneous satellite network will be enhanced by the presented assumptions.

Scale Matters: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Large and Heterogeneous Satellite Images

To classify Very-High-Resolution (VHR) imagery, Geographic Object Based Image Analysis (GEOBIA) is the most popular method used to produce high quality Land-Use/Land-Cover maps. A crucial step in GEOBIA is the appropriate parametrization of the segmentation algorithm prior to the classification. However, little effort has been made to automatically optimize GEOBIA algorithms in an unsupervised and spatially meaningful manner. So far, most Unsupervised Segmentation Parameter Optimization (USPO) techniques, assume spatial stationarity for the whole study area extent. This can be questionable, particularly for applications in geographically large and heterogeneous urban areas. In this study, we employed a novel framework named Spatially Partitioned Unsupervised Segmentation Parameter Optimization (SPUSPO), which optimizes segmentation parameters locally rather than globally, for the Sub-Saharan African city of Ouagadougou, Burkina Faso, using WorldView-3 imagery (607 km2). The results showed that there exists significant spatial variation in the optimal segmentation parameters suggested by USPO across the whole scene, which follows landscape patterns—mainly of the various built-up and vegetation types. The most appropriate automatic spatial partitioning method from the investigated techniques, was an edge-detection cutline algorithm, which achieved higher classification accuracy than a global optimization, better predicted built-up regions, and did not suffer from edge effects. The overall classification accuracy using SPUSPO was 90.5%, whilst the accuracy from undertaking a traditional USPO approach was 89.5%. The differences between them were statistically significant (p < 0.05) based on a McNemar’s test of similarity. Our methods were validated further by employing a segmentation goodness metric, Area Fit Index (AFI)on building objects across Ouagadougou, which suggested that a global USPO was more over-segmented than our local approach. The mean AFI values for SPUSPO and USPO were 0.28 and 0.36, respectively. Finally, the processing was carried out using the open-source software GRASS GIS, due to its efficiency in raster-based applications.

Exact and Heuristic Methods for Observing Task-Oriented Satellite Cluster Agent Team Formation

With the development of aerospace science and technology, Earth Observation Satellite cluster which consists of heterogeneous satellites with many kinds of payloads appears gradually. Compared with the traditional satellite systems, satellite cluster has some particular characteristics, such as large-scale, heterogeneous satellite platforms, various payloads, and the capacity of performing all the observation tasks. How to select a subset from satellite cluster to perform all observation tasks effectively with low cost is a new challenge arousing in the field of aerospace resource scheduling. This is the agent team formation problem for observation task-oriented satellite cluster. A mathematical scheduling model is built. Three novel algorithms, i.e., complete search algorithm, heuristic search algorithm, and swarm intelligence optimization algorithm, are proposed to solve the problem in different scales. Finally, some experiments are conducted to validate the effectiveness and practicability of our algorithms.