Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data

The Newton’s Polynomials Interpolation Based-Error Correction Method for Low-Cost Dive Altitude Sensor in Remotely Operated Underwater Vehicle (ROV)

Indonesia is the one of the countries with the largest of sea area. However, the water surveillance categorized as minimum. The human resource and the low level of infrastructure are the causal of the minimum level of water surveillance. The human involvement of water surveillance has many weaknesses, such as weak against the change of the nature condition, limitation in reaching location, weak against water turbidity levels and water pollution. The utilization of ROV (Remotely Operated Underwater Vehicle) could be a solution in water surveillance problem. The development of ROV still not significant in Indonesia. The development costs are also a problem in development of ROV. Many researcher using USBL (Ultra Short Base Line) sensor to sense the depth of the ROV. However, the cost of this sensor is relatively expensive. The usage of low-cost pressure sensor could be a solution to replace the USBL sensor. The low-cost pressure sensor has a significant deviation. The implementation of Newton’s polynomials interpolation algorithm has been used to decrease the deviation level of the sensors. The result shows the algorithm has succeeded to decrease the deviation level of the pressure sensor significantly. The MSE value of default sensor was 42956.2, which is significantly worst. The Newton interpolation algorithm has been succeeded to reducing the MSE value to 17.82. The result of this research was expected to reduce the cost of the ROVs development especially for sensors cost.Indonesia is the one of the countries with the largest of sea area. However, the water surveillance categorized as minimum. The human resource and the low level of infrastructure are the causal of the minimum level of water surveillance. The human involvement of water surveillance has many weaknesses, such as weak against the change of the nature condition, limitation in reaching location, weak against water turbidity levels and water pollution. The utilization of ROV (Remotely Operated Underwater Vehicle) could be a solution in water surveillance problem. The development of ROV still not significant in Indonesia. The development costs are also a problem in development of ROV. Many researcher using USBL (Ultra Short Base Line) sensor to sense the depth of the ROV. However, the cost of this sensor is relatively expensive. The usage of low-cost pressure sensor could be a solution to replace the USBL sensor. The low-cost pressure sensor has a significant deviation. The implementation of Newton’s polynomials interpolation algorithm has been used to decrease the deviation level of the sensors. The result shows the algorithm has succeeded to decrease the deviation level of the pressure sensor significantly. The MSE value of default sensor was 42956.2, which is significantly worst. The Newton interpolation algorithm has been succeeded to reducing the MSE value to 17.82. The result of this research was expected to reduce the cost of the ROVs development especially for sensors cost

The Performance Improvement of the Low-Cost Ultrasonic Range Finder (HC-SR04) Using Newton's Polynomial Interpolation Algorithm

The ultrasonic range finder sensors are widely used sensor in many applications such as computer applications, general purpose applications, medical applications, automotive applications and industrial grade applications. The ultrasonic range finder sensor has many advantages. The advantages are easy to use, fast in measuring process, non-contact measurement and suitable for air and underwater environment. However, the ultrasonic range finder has deviation especially for low-cost sensor. It affects the accuracy level of the measurement result that performed by its sensor directly. The HC-SR04 categorized as a low-cost ultrasonic range finder sensor. This sensor has significant error level. The improvement of the accuracy level of this low-cost ultrasonic sensor is expected to this research. The Newton's polynomial interpolation algorithm has been used in this research to reduce the error during the measurement process. The implementation of Newton's polynomial interpolation has succeeded to improve the sensor accuracy. The MSE level of 29,96 is obtained without the Newton's Polynomial Interpolation implementation. The implementation of the Newton's Polynomial Interpolation algorithm has succeeded to increase the accuracy level of the sensor by 55,54%. It has been proofed by the decrease of MSE level by 13,32.The ultrasonic range finder sensors are widely used sensor in many applications such as computer applications, general purpose applications, medical applications, automotive applications and industrial grade applications. The ultrasonic range finder sensor has many advantages. The advantages are easy to use, fast in measuring process, non-contact measurement and suitable for air and underwater environment. However, the ultrasonic range finder has deviation especially for low-cost sensor. It affects the accuracy level of the measurement result that performed by its sensor directly. The HC-SR04 categorized as a low-cost ultrasonic range finder sensor. This sensor has significant error level. The improvement of the accuracy level of this low-cost ultrasonic sensor is expected to this research. The Newton's polynomial interpolation algorithm has been used in this research to reduce the error during the measurement process. The implementation of Newton's polynomial interpolation has succeeded to improve the sensor accuracy. The MSE level of 29,96 is obtained without the Newton's Polynomial Interpolation implementation. The implementation of the Newton's Polynomial Interpolation algorithm has succeeded to increase the accuracy level of the sensor by 55,54%. It has been proofed by the decrease of MSE level by 13,32

Anisotropic osmosis filtering for shadow removal in images

We present an anisotropic extension of the isotropic osmosis model that has been introduced by Weickert et al.~(Weickert, 2013) for visual computing applications, and we adapt it specifically to shadow removal applications. We show that in the integrable setting, linear anisotropic osmosis minimises an energy that involves a suitable quadratic form which models local directional structures. In our shadow removal applications we estimate the local structure via a modified tensor voting approach (Moreno, 2012) and use this information within an anisotropic diffusion inpainting that resembles edge-enhancing anisotropic diffusion inpainting (Weickert, 2006, Gali\'c, 2008). Our numerical scheme combines the nonnegativity preserving stencil of Fehrenbach and Mirebeau (Fehrenbach, 2014) with an exact time stepping based on highly accurate polynomial approximations of the matrix exponential. The resulting anisotropic model is tested on several synthetic and natural images corrupted by constant shadows. We show that it outperforms isotropic osmosis, since it does not suffer from blurring artefacts at the shadow boundaries

Symmetric quadratic tetration interpolation using forward and backward operation combination

The existed interpolation method, based on the second-order tetration polynomial, has the asymmetric property. The interpolation results, for each considering region, give individual characteristics. Although the interpolation performance has been better than the conventional methods, the symmetric property for signal interpolation is also necessary. In this paper, we propose the symmetric interpolation formulas derived from the second-order tetration polynomial. The combination of the forward and backward operations was employed to construct two types of the symmetric interpolation. Several resolutions of the fundamental signals were used to evaluate the signal reconstruction performance. The results show that the proposed interpolations can be used to reconstruct the fundamental signal and its peak signal to noise ratio (PSNR) is superior to the conventional interpolation methods, except the cubic spline interpolation for the sine wave signal. However, the visual results show that it has a small difference. Moreover, our proposed interpolations converge to the steady-state faster than the cubic spline interpolation. In addition, the option number increasing will reinforce their sensitivity

Optimum constrained image restoration filters

The filter was developed in Hilbert space by minimizing the radius of gyration of the overall or composite system point-spread function subject to constraints on the radius of gyration of the restoration filter point-spread function, the total noise power in the restored image, and the shape of the composite system frequency spectrum. An iterative technique is introduced which alters the shape of the optimum composite system point-spread function, producing a suboptimal restoration filter which suppresses undesirable secondary oscillations. Finally this technique is applied to multispectral scanner data obtained from the Earth Resources Technology Satellite to provide resolution enhancement. An experimental approach to the problems involving estimation of the effective scanner aperture and matching the ERTS data to available restoration functions is presented

Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data

Electroencephalography (EEG), magnetoencephalography (MEG) and related techniques are prone to glitches, slow drift, steps, etc., that contaminate the data and interfere with the analysis and interpretation. These artifacts are usually addressed in a preprocessing phase that attempts to remove them or minimize their impact. This paper offers a set of useful techniques for this purpose: robust detrending, robust rereferencing, outlier detection, data interpolation (inpainting), step removal, and filter ringing artifact removal. These techniques provide a less wasteful alternative to discarding corrupted trials or channels, and they are relatively immune to artifacts that disrupt alternative approaches such as filtering. Robust detrending allows slow drifts and common mode signals to be factored out while avoiding the deleterious effects of glitches. Robust rereferencing reduces the impact of artifacts on the reference. Inpainting allows corrupt data to be interpolated from intact parts based on the correlation structure estimated over the intact parts. Outlier detection allows the corrupt parts to be identified. Step removal fixes the high-amplitude flux jump artifacts that are common with some MEG systems. Ringing removal allows the ringing response of the antialiasing filter to glitches (steps, pulses) to be suppressed. The performance of the methods is illustrated and evaluated using synthetic data and data from real EEG and MEG systems. These methods, which are mainly automatic and require little tuning, can greatly improve the quality of the data

At risk of being risky: The relationship between "brain age" under emotional states and risk preference.

Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N=212; 10-25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the "brain age" of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that "brain age" across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts) relates to a group mean difference in risk perception - a pattern exemplified greatest in young-adults (ages 18-21). The results are suggestive of a specified functional brain phenotype that relates to being at "risk to be risky.

Optimising Spatial and Tonal Data for PDE-based Inpainting

Some recent methods for lossy signal and image compression store only a few selected pixels and fill in the missing structures by inpainting with a partial differential equation (PDE). Suitable operators include the Laplacian, the biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The quality of such approaches depends substantially on the selection of the data that is kept. Optimising this data in the domain and codomain gives rise to challenging mathematical problems that shall be addressed in our work. In the 1D case, we prove results that provide insights into the difficulty of this problem, and we give evidence that a splitting into spatial and tonal (i.e. function value) optimisation does hardly deteriorate the results. In the 2D setting, we present generic algorithms that achieve a high reconstruction quality even if the specified data is very sparse. To optimise the spatial data, we use a probabilistic sparsification, followed by a nonlocal pixel exchange that avoids getting trapped in bad local optima. After this spatial optimisation we perform a tonal optimisation that modifies the function values in order to reduce the global reconstruction error. For homogeneous diffusion inpainting, this comes down to a least squares problem for which we prove that it has a unique solution. We demonstrate that it can be found efficiently with a gradient descent approach that is accelerated with fast explicit diffusion (FED) cycles. Our framework allows to specify the desired density of the inpainting mask a priori. Moreover, is more generic than other data optimisation approaches for the sparse inpainting problem, since it can also be extended to nonlinear inpainting operators such as EED. This is exploited to achieve reconstructions with state-of-the-art quality. We also give an extensive literature survey on PDE-based image compression methods