A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism

While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.

Advanced data handling architecture for earth observation satellites

In this paper we describe how commercial open standards for embedded systems could affect the architecture of future satellite data handling systems. Traditionally, satellite data handling systems are based on the principles of a federated architecture, i. e. one function is implemented as one box. Each box has its own housing and power supply. In the paper we describe the transition path from the traditional federated architecture to a centralized but modular architecture based on adapted industrial standards. In the presented approach functional modules like on-board computer, Global Navigation Satellite System receiver, interface boards, etc. are combined in a rack communicating via a standard backplane using standardized communication links. The analysis performed during the Advanced Data Handling Architecture study showed that this approach contributes significantly to mass and power reduction (approx. 20 %) of a typical satellite data handling system. Another major point highlighted in the Advanced Data Handling Architecture study is the simplification of Assembly, Integration and Test activities. All this will help space industry to handle increasing system complexity while keeping costs at an acceptable level.

THE DIGITAL ELEVATION MODEL INTERCOMPARISON EXPERIMENT DEMIX, A COMMUNITY-BASED APPROACH AT GLOBAL DEM BENCHMARKING

This paper presents an initiative recently launched under the auspices of the Committee on Earth Observation Satellites (CEOS) aiming at providing harmonised terminology and methods, as well as practical guidelines and results allowing the intercomparison of continental or global Digital Elevation Models (DEM). As the work is still ongoing the main purpose of this article is not the dissemination of the outcome but rather to inform the wider community about the initiative, communicate the chosen approach to raise awareness, and attract possible further participants. Nevertheless, some preliminary results are included and an outlook on planned next steps is provided.

ONBOARD/ON-GROUND IMAGE PROCESSING CHAIN FOR HIGH-RESOLUTION EARTH OBSERVATION SATELLITES

Over the last decade, the French space agency (CNES) has designed and successfully operated high-resolution satellites such as Pléiades. High-resolution satellites typically acquire panchromatic images with fine spatial resolutions and multispectral images with coarser samplings for downlink constraints. The multispectral image is reconstructed on the ground, using pan-sharpening techniques. Onboard compression and ground processing affect however the quality of the final product. In this paper, we describe our next-generation onboard/on-ground image processing chain for high-resolution satellites. This paper focuses on onboard compression, compression artefacts correction, denoising, deconvolution and pan-sharpening. In the first part, we detail our fixed-quality compression approach, which limits compression effects to a fraction of the noise, thus preserving the useful information in an image. This approach optimises the bitrate at the cost of image size, which depends on the scene complexity. This technique requires however pre- and post-processing steps. The noisy HR images obtained after decompression are suited for non-local denoising algorithms. We show in the second part of this paper that non-local denoising outperforms previous techniques by 15% in terms of root mean-squared error when tested on simulated noiseless references. Deconvolution is also detailed. In the final part of this paper, we put forward an adaptation of this chain to low-cost CMOS Bayer colour matrices. We demonstrate that the concept of our image chain remains valid, provided slight modifications (in particular dedicated transformations of the colour planes and demosaicing). A similar chain is under investigation for future missions.

PREDICTING THE INFRARED UAV IMAGERY OVER THE COAST

The infrared (IR) imagery provides additional information to the visible (red-green-blue, RGB) about vegetation, soil, water, mineral, or temperature, and has become essential for various disciplines, such as geology, hydrology, ecology, archeology, meteorology or geography. The integration of the IR sensors, ranging from near-IR (NIR) to thermal-IR through mid-IR, constitutes a baseline for Earth Observation satellites but not for unmanned airborne vehicles (UAV). Given the hyperspatial and hypertemporal characteristics associated with the UAV survey, it is relevant to benefit from the IR waveband in addition to the visible imagery for mapping purposes. This paper proposes to predict the NIR reflectance from RGB digital number predictors collected with a consumer-grade UAV over a structurally and compositionally complex coastal area. An array of 15 000 data, distributed into calibration, validation and test datasets across 15 representative coastal habitats, was used to build and compare the performance of the standard least squares, decision tree, boosted tree, bootstrap forest and fully connected neural network (NN) models. The NN family surpassed the four other ones, and the best NN model (R2 = 0.67) integrated two hidden layers provided, each, with five nodes of hyperbolic tangent and five nodes of Gaussian activation functions. This perceptron enabled to produce a NIR reflectance spatially-explicit model deprived of original artifacts due to the flight constraints. At the habitat scale, sedimentary and dry vegetation environments were satisfactorily predicted (R2 > 0.6), contrary to the healthy vegetation (R2 < 0.2). Those innovative findings will be useful for scientists and managers tasked with hyperspatial and hypertemporal mapping.

Editorial for Special Issue “UAV Photogrammetry and Remote Sensing”

The concept of Remote Sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of earth observation satellites [...]

Variational Fusion of Hyperspectral Data by Non-Local Filtering

The fusion of multisensor data has attracted a lot of attention in computer vision, particularly among the remote sensing community. Hyperspectral image fusion consists in merging the spectral information of a hyperspectral image with the geometry of a multispectral one in order to infer an image with high spatial and spectral resolutions. In this paper, we propose a variational fusion model with a nonlocal regularization term that encodes patch-based filtering conditioned to the geometry of the multispectral data. We further incorporate a radiometric constraint that injects the high frequencies of the scene into the fused product with a band per band modulation according to the energy levels of the multispectral and hyperspectral images. The proposed approach proved robust to noise and aliasing. The experimental results demonstrate the performance of our method with respect to the state-of-the-art techniques on data acquired by commercial hyperspectral cameras and Earth observation satellites.