ALBERT

Description: Commercial, industrial, and military aerospace designs are increasingly deploying MEMS micro inertial measurement unit (MIMU) for motion control, automation, and positioning applications, such as the unmanned aerial vehicle (UAV), robot, and smart phone. On the one hand, MIMU has the merit of low cost, small size, low-power consumption, and high shock resistance, but on the other hand, low-cost MIMU is affected by systematic error caused by the instability of the drift, scaling factors, and axes misalignment, which may lead to large errors in the position and attitude’s determination from time to time. That means calibration before use is an effective way to improve the practical precision of MIMU. However, many customers have no precise turntable to calibrate the MIMU before they use it. To address these problems, this paper presents an easy self-calibration method to implement calibration of the MIMU on a common table only with an inclined surface, no precise turntable is needed. The calibration method is based on the following principles. First, the module of the output vector of the orthogonal configured three-axis accelerometers is equal to unit gravity. Second, when IMU rotates to a known gesture with a stable axis, the angles can be calculated through integration. Third, when the accelerometers’ parameters are calculated, it can act as a level datum. Furthermore, the accelerometers on the inclined surface are used to determine the rotating heading datum. Finally, after a series static positions test and rotating test, the parameters can be extracted and estimated. To demonstrate the success and the convenience of the proposed method, comparison experiments with the precision turntable have been made on an ADI’s MIMU. The calibration results show that the accuracy and precision of this method is quite equivalent with the turntable-based calibration, and the scale factors error with an order of magnitude always equal or less than $10^{-5}$ . The observed static and dynamic yaw maximum angular error in a certain period is <0.8°, the pitch maximum angular error is <0.5°, and the roll maximum angular error is <0.3°.

Description: In this paper, the classification via sprepresentation and multitask learning is presented for target recognition in SAR image. To capture the characteristics of SAR image, a multidimensional generalization of the analytic signal, namely the monogenic signal, is employed. The original signal can be then orthogonally decomposed into three components: 1) local amplitude; 2) local phase; and 3) local orientation. Since the components represent the different kinds of information, it is beneficial by jointly considering them in a unifying framework. However, these components are infeasible to be directly utilized due to the high dimension and redundancy. To solve the problem, an intuitive idea is to define an augmented feature vector by concatenating the components. This strategy usually produces some information loss. To cover the shortage, this paper considers three components into different learning tasks, in which some common information can be shared. Specifically, the component-specific feature descriptor for each monogenic component is produced first. Inspired by the recent success of multitask learning, the resulting features are then fed into a joint sparse representation model to exploit the intercorrelation among multiple tasks. The inference is reached in terms of the total reconstruction error accumulated from all tasks. The novelty of this paper includes 1) the development of three component-specific feature descriptors; 2) the introduction of multitask learning into sparse representation model; 3) the numerical implementation of proposed method; and 4) extensive comparative experimental studies on MSTAR SAR dataset, including target recognition under standard operating conditions, as well as extended operating conditions, and the capability of outliers rejection.

Description: Offsets of synthetic aperture radar (SAR) images have played an important role in deriving complete three-dimensional (3-D) surface displacement fields in geoscientific applications. However, offset maps often suffer from multiple outliers and patch-like artifacts, because the standard offset-measurement method is a regular moving-window operation that does not consider the scattering characteristics of the ground. Here, we show that by focusing the offset measurements on predetected strong reflectors, the reliability and accuracy of SAR offsets can be significantly improved. Application to the 2011 Van (Turkey) earthquake reveals a clear deformation signal from an otherwise decorrelated interferogram, making derivation of the 3-D coseismic displacement field possible. Our proposed method can improve mapping of coseismic deformation and other ground displacements, such as glacier flow and landslide movement when strong reflectors exist.

Description: We present an accelerated probabilistic learning concept and its prototype implementation for mining heterogeneous Earth observation images, e.g., multispectral images, synthetic aperture radar (SAR) images, image time series, or geographical information systems (GIS) maps. The system prototype combines, at pixel level, the unsupervised clustering results of different features, extracted from heterogeneous satellite images and geographical information resources, with user-defined semantic annotations in order to calculate the posterior probabilities that allow the final probabilistic searches. The system is able to learn different semantic labels based on a newly developed Bayesian networks algorithm and allows different probabilistic retrieval methods of all semantically related images with only a few user interactions. The new algorithm reduces the computational cost, overperforming existing conventional systems, under certain conditions, by several orders of magnitude. The achieved speed-up allows the introduction of new feature models improving the learning capabilities of knowledge-driven image information mining systems and opening them to Big Data environments.

Description: The mineralogy and environmental history of Mars are being extensively studied through remote sensing observations paired with laboratory and in situ experiments. A significant portion of these experiments is being devoted to the identification and mapping of different iron oxides present in the Martian terrains. Among these compounds, goethite has been an object of great interest since its occurrence can be interpreted as mineralogical evidence of past aqueous activity on those landscapes. Although such experiments can provide valuable information regarding the presence of these minerals, the scope of the resulting observations may be hindered by logistics and cost-related constraints. We believe that predictive computer simulations can be employed to mitigate some of these constraints and contribute to the generation and validation of hypotheses in this area. Accordingly, we propose the use of SPLITS ( Sp ectral Li ght T ransport Model for S and) in investigations involving the spectral signatures of iron-bearing regions of Mars. In this paper, we initially demonstrate the predictive capabilities of the SPLITS model in this context through qualitative comparisons of modeled results with actual observations and measured data. Using the resulting modeled reflectance curves as our baseline data, we then perform a series of controlled computational experiments to investigate how variations on goethite and hematite content affect the spectral responses of Martian sand-textured soils.

Description: Thermal infrared (TIR) time series are exploited by many methods based on Earth observation (EO), for such applications as agriculture, forest management, and meteorology. However, due to physical limitations, data acquired by a single sensor are often unsatisfactory in terms of spatial or temporal resolution. This issue can be tackled by using remotely sensed data acquired by multiple sensors with complementary features. When nonreal-time functioning or at least near real-time functioning is admitted, the measurements can be profitably fed to a sequential Bayesian algorithm, which allows to account for the correlation embedded in the successive acquisitions. In this work, we focus on applications that allow the batch processing of the whole data sequences acquired in a fixed time interval. In this case, multiple options for improving the final product are offered by the Bayesian framework, based on both sequential and smoothing techniques. We consider several such Bayesian strategies and comparatively assess their performances in practical applications and through real thermal data acquired by the SEVIRI and MODIS sensors, encompassing the presence of multiple disturbance source, e.g., the cloud coverage of the illuminated scene.

Description: Land surface albedo, qualifying the ratio of the radiant flux reflected from the land surface to the incident flux, is a key forcing parameter controlling the Earth’s energy budget. Previously, several BRDF archetypes were distilled from high-quality MODIS BRDF/Albedo products. In this study, we propose a method that largely relies on matching observed multiangular reflectances with the most appropriate of these prior BRDF archetypes to determine the amplitude and shape of the actual surface BRDFs, when directional signatures are insufficient. This method is first evaluated using an assortment of multisource BRDF data sets to demonstrate its viability for surface albedo estimates, and then is applied to airborne wide-angle infrared dual-mode line/area array scanner (WIDAS) from the Watershed Allied Telemetry Experimental Research (WATER) campaign in the Heihe River Basin of China in 2008. This algorithm makes use of the linear MODIS BRDF model to determine the BRDF archetypes needed as prior knowledge for intrinsic spectral albedo estimates. The intrinsic spectral albedos are then used to estimate actual spectral albedos by considering the proportion of direct and diffuse solar radiation. A spectral-to-broadband conversion is performed to generate the broadband albedo at shortwave regimes through the use of conversion coefficients derived from extensive radiative transfer simulations. A further validation confirms that the estimated albedos are consistent with in situ field measured albedos over available corn crop sites. This method provides a major advantage on utilizing generalized BRDF information derived from MODIS in conjunction with other instrument data that are acquired with less angular variation.

Description: Ocean current is highly related to the interaction between ocean and atmosphere. By measuring the speed and direction of the ocean current from space, we can investigate the ocean–atmosphere interaction on a global scale. The ocean–atmosphere interaction helps to maintain the balance that is essential for planet habitability. However, the conventional scatterometer is unable to measure the ocean current vector. To achieve this, a potentially feasible approach is to use a bigger antenna, a higher PRF, and measure the interferometric phase of two successive echoes. This paper derives four decorrelation factors, and provides the phase error model first. Then, an end-to-end simulation model is established, and it is used to analyze the feasibility of ocean surface current measurement from space. Based on the simulation model, the system parameters are optimized. The simulation results show that the current speed standard deviation (Std), which means the measurement accuracy, in along-track and cross-track direction is smaller than 0.1 m/s when the wind speed is larger than 4 m/s. The swath can be used for current vector inversion that is greater than 70% when the wind speed is larger than 7 m/s. Meanwhile, ${{bf K}_{{bf pc}}}$ of the modified scatterometer is computed and the results show that ${{bf K}_{{bf pc}}}$ is better than the traditional pencil-beam rotating scatterometer when the wind speed is larger than 6 m/s.

Description: High-resolution three-dimensional (3-D) radar imaging of space targets in micromotion plays a significant role in space target recognition and space situation awareness; thus, it has attracted extensive attention in recent years. Because of the fast rotation, some scattering centers are occluded by others, i.e., the scattering centers cannot be continuously illuminated by radar in the imaging interval, and their radar echoes are discontinuous. In this paper, a nonparametric 3-D imaging method based on scattering center trajectory association is proposed. It deals with target occlusion using the Riemannian manifold optimization and obtains focused imaging of targets in complex micromotion. The effectiveness of the proposed method is validated using simulated data.