Low-resolution Sensor Research Articles

Remote sensing applications for prescribed burn research

Prescribed burning is a key management strategy within fire-adapted systems, and improved monitoring approaches are needed to evaluate its effectiveness in achieving social-ecological outcomes. Remote sensing provides opportunities to analyse the impacts of prescribed burning, yet a comprehensive understanding of the applications of remote sensing for prescribed burn research is lacking. We conduct a literature review of 120 peer-reviewed publications to synthesise the research aims, methodologies, limitations and future directions of remote sensing for the analysis of prescribed fire. Studies evaluating management outcomes found prescribed burning effective for wildfire risk reduction, yet few analysed co-benefits or trade-offs with other management goals. Most studies use passive, spaceborne, low spatial resolution sensors, characterised in the literature as consistent and accessible data sources but limited in detecting small, low-severity and short-duration fires characteristic of prescribed burns. In contrast, active remote sensing approaches including LiDAR are less frequently employed, but show promise for highly accurate, spatially explicit 3D vegetation and fuel load mapping. Remote sensing advances toward higher spatial resolution, more frequent revisit, denser spectral sampling and more data across the electromagnetic spectrum are critical to advancing prescribed fire research, addressing current methodological gaps, and improving fuels and fire management capacity.

Madagascar's burned area from Sentinel-2 imagery (2016–2022): Four times higher than from lower resolution sensors

Madagascar is one of the most burned regions in the world, to the point that it has been called the ‘Isle of fire’ or the ‘Burning Island’. An accurate characterization of the burned area (BA) is crucial for understanding the true situation and impacts of fires on this island, where there is an active scientific debate on how fire affects multiple environmental and socioeconomic aspects, and how fire regimes should be in a complex context with differing interests. Despite this, recent advances have revealed that BA in Madagascar is poorly characterised by the currently available global BA products. In this work, we present, validate, and explore a BA database at 20 m spatial resolution for Madagascar covering the period 2016–2022. The database was built based on 75,010 Sentinel-2 images using a two-phase BA detection algorithm. The validation with independent long-term reference units showed Dice coefficients ≥79 %, omission errors ≤24 %, commission errors ≤18 %, and a relative bias ≥ − 8 %. An intercomparison with other available global BA products (GABAM, FireCCI51, C3SBA11, or MCD64) demonstrated that our product (i) exhibits temporal consistency, (ii) represents a significant accuracy improvement, as it reduces BA underestimations by about eightfold, (iii) yields BA estimates four times higher, and (iv) shows enhanced capability in detecting fires of all sizes. The observed BA spatial patterns were heterogeneous across the island, with 32 % of the grasslands burning annually, in contrast to other land cover types such as the dense tropical forest where <2 % burned every year. We conclude that the BA characterization in Madagascar must be addressed using imagery at spatial resolution higher than MODIS or Sentinel-3 (≥250 m), and temporal resolution higher than Landsat (16 days) to deal with cloudiness, the rapid attenuation of burn scars signals, and small fire patches.

Integrated turbulence parameters' estimation from NAOMI adaptive optics telemetry data

Context.Monitoring turbulence parameters is crucial in high-angular resolution astronomy for various purposes, such as optimising adaptive optics systems or fringe trackers. The former systems are present at most modern observatories and will remain significant in the future. This makes them a valuable complementary tool for the estimation of turbulence parameters.Aims.The feasibility of estimating turbulence parameters from low-resolution sensors remains untested. We performed seeing estimates for both simulated and on-sky telemetry data sourced from the new adaptive optics module installed on the four Auxiliary Telescopes of the Very Large Telescope Interferometer.Methods.The seeing estimates were obtained from a modified and optimised algorithm that employs a chi-squared modal fitting approach to the theoretical von Kármán model variances. The algorithm was built to retrieve turbulence parameters while simultaneously estimating and accounting for the remaining and measurement error. A Monte Carlo method was proposed for the estimation of the statistical uncertainty of the algorithm.Results.The algorithm is shown to be able to achieve per-cent accuracy in the estimation of the seeing with a temporal horizon of 20 s on simulated data. A (0.76″ ± 1.2%|stat± 1.2%|sys) median seeing was estimated from on-sky data collected from 2018 to 2020. The spatial distribution of the Auxiliary Telescopes across the Paranal Observatory was found to not play a role in the value of the seeing.

Collaborative representation with hole filling techniques for Kinect RGB-D face recognition

We present in this paper, three different filtering techniques based on kernel function to fill missing information in depth map images obtained from low resolution sensor such as Kinect to improve the performance accuracy of RGB-D face recognition systems. We propose in this study, an RGB-D face recognition scheme that combines depth map and colour image using wavelet average fusion followed by collaborative representation classifier (CRC) for comparison of reference and probe images. We present the evaluation results based on our GU-RGBD face database and IIIT-D face database to present the significance of our three different filters employed in hole filling. Further, our investigation presents an extensive experimental analysis using eight different feature extraction techniques independently across three different filters to demonstrate the potential of our proposed approach. The proposed approach of hole filling, improves the performance accuracy of RGB-D face recognition as compared to without employing filtering operations.

Skip-Concatenated Image Super-Resolution Network for Mobile Devices

Single-image super-resolution technology has been widely studied in various applications to improve the quality and resolution of degraded images acquired from noise-sensitive low-resolution sensors. As most studies on single-image super-resolution focused on the development of deep learning networks operating on high-performance GPUs, this study proposed an efficient and lightweight super-resolution network that enables real-time performance on mobile devices. To replace the relatively slow element-wise addition layer on mobile devices, we introduced a skip connection layer by directly concatenating a low-resolution input image with an intermediate feature map. In addition, we introduced weighted clipping to reduce the quantization errors commonly encountered during float-to-int8 model conversion. Moreover, a reparameterization method was selectively applied without increasing the cost in terms of inference time and number of parameters. Based on the contributions, the proposed network has been recognized as the best solution in Mobile AI & AIM 2022 Real-Time Single-Image Super-Resolution Challenge with PSNR of 30.03 dB and NPU runtime of 19.20 ms.

Low Power Environmental Image Sensors for Remote Photogrammetry.

This paper aims to prove the feasibility of a 4D monitoring solution (3D modeling and temporal monitoring) for the sandbar and to characterize the species’ role in the landscape. The developed solution allows studying the interaction between the river dynamics and vegetation using a network of low resolution and low power sensors. The issues addressed concern the feasibility of implementing a photogrammetry solution using low-resolution sensors as well as the choice of the appropriate sensor and its testing according to different configurations (image capture and storage on the sensor and/or image transmission to a centralization node) and also the detailed analysis of the different phases of the process (camera initialization, image capture, network transmission and selection of the most appropriate standby mode). We reveal that the tiny, low-cost board (ESP32-Cam) can perform a 3D reconstruction and propose using the camera’s UXGA (1600, 1200) resolution because of the quality rendering and energy consumption. A multi-node scenario based on a combined Wi-Fi and GSM relay is proposed in the study showing several years of autonomy for the system. Finally, to illustrate the energy cost of the module, we have defined a study process, where we have identified and quantified one by one the different phases of operation of the card for better energy optimization (setup, camera configuration, shooting, saving on SD card, or sending by Wi-Fi). The device is now operational for deployment on the Allier River (France).

Predicting Vehicle Behavior Using Multi-task Ensemble Learning

Vehicle utilization analysis is an essential tool for manufacturers to understand customer needs, improve equipment uptime, and to collect information for future vehicle and service development. Typically today, this behavioral modeling is done on high-resolution time-resolved data with features such as GPS position and fuel consumption. However, high-resolution data is costly to transfer and sensitive from a privacy perspective. Therefore, such data is typically only collected when the customer pays for extra services relying on that data. This motivated us to develop a multi-task ensemble approach to transfer knowledge from the high-resolution data and enable vehicle behavior prediction from low-resolution but high dimensional data that is aggregated over time in the vehicles.This study proposes a multi-task snapshot-stacked ensemble (MTSSE) deep neural network for vehicle behavior prediction by considering vehicles’ low-resolution operational life records. The multi-task ensemble approach utilizes the measurements to map the low-frequency vehicle usage to the vehicle behaviors defined from the high-resolution time-resolved data. Two data sources are integrated and used: high-resolution data called Dynafleet, and low-resolution so-called Logged Vehicle Data (LVD). The experimental results demonstrate the proposed approach’s effectiveness in predicting the vehicle behavior from low frequency data. With the suggested multi-task snapshot-stacked ensemble deep network, it is shown how low-resolution sensor data can highly contribute to predicting multiple vehicle behaviors simultaneously while using only one single training process.

Resident space object (RSO) attitude and optical property estimation from space-based light curves

With the increase in the number of objects orbiting Earth, Space Situational Awareness (SSA) has becoming an important area of research in the space sector. Currently most sensors that contribute to SSA are large dedicated optical or radar stations, such as space fence (Pechkis, et al., 2015). With the increase in low resolution sensors in LEO there is a growing potential to utilize these to augment current SSA efforts. Star trackers are readily available and used in space for attitude determination, with recent work performed to demonstrate the benefit of using spaced-based optical measurements for Resident Space Object (RSO) detection. In this paper, we describe the interpretation of space-based measurement for light curve of an RSOs to estimate the RSOs shape, attitude and optical properties. In this model, two Bidirectional Reflectance Distribution Functions (BDRF’s) are compared, namely a defined facet model and an anthropic Phong model. From the initial results an RSOs shape, attitude, optical properties can be estimated with basic a-priory information on the shape of the RSO with both models.

Fast Reconstruction of Three-Quarter Sampling Measurements Using Recurrent Local Joint Sparse Deconvolution and Extrapolation

Recently, non-regular three-quarter sampling has shown to deliver an increased image quality of image sensors by using differently oriented L-shaped pixels compared to the same number of square pixels. A three-quarter sampling sensor can be understood as a conventional low-resolution sensor where one quadrant of each square pixel is opaque. Subsequent to the measurement, the data can be reconstructed on a regular grid with twice the resolution in both spatial dimensions using an appropriate reconstruction algorithm. For this reconstruction, local joint sparse deconvolution and extrapolation (L-JSDE) has shown to perform very well. As a disadvantage, L-JSDE requires long computation times of several dozen minutes per megapixel. In this paper, we propose a faster version of L-JSDE called recurrent L-JSDE (RL-JSDE) which is a reformulation of L-JSDE. For reasonable recurrent measurement patterns, RL-JSDE provides significant speedups on both CPU and GPU without sacrificing image quality. Compared to L-JSDE, 20-fold and 733-fold speedups are achieved on CPU and GPU, respectively.

Head Tracking in Automotive Environments for Driver Monitoring Using a Low Resolution Thermal Camera

The steady enhancement of driver assistance systems and the automation of driving functions are in need of advanced driver monitoring functionalities. To evaluate the driver state, several parameters must be acquired. A basic parameter is the position of the driver, which can be useful for comfort automation or medical applications. Acquiring the position through cameras can be used to provide multiple information at once. When using infrared cameras, not only the position information but also the thermal information is available. Head tracking in the infrared domain is still a challenging task. The low resolution of affordable sensors makes it especially difficult to achieve high robustness due the lack of detailed images. In this paper, we present a novel approach for robust head tracking based on template matching and optical flow. The method has been tested on various sets of subjects containing different head shapes. The evaluation does not only include the original sensor size, but also downscaled images to simulate low resolution sensors. A comparison with the ground truth is performed for X- and Y-coordinate separately for each downscaled resolution.

Boosting Image Super-Resolution via Fusion of Complementary Information Captured by Multi-Modal Sensors

Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution sensors for a wide range of optical applications. It is noted that the costs for capturing high-resolution images in various spectral ranges are significantly different, thus it is reasonable to utilize low-cost channel images (e.g., visible/depth images) as guidance to boost the accuracy of SR results of the expensive channel (e.g., thermal images) significantly. In this paper, we attempt to leverage complementary information from low-cost channels (visible/depth) to boost image quality of an expensive channel (thermal) using fewer parameters. To this end, we first build a multi-modal imaging system and present an effective method to generate pixel-wise aligned visible and thermal images via virtual 3D viewpoint rendering. Then, we design a feature-level multispectral fusion residual network model to perform high-accuracy SR of thermal images by adaptively integrating co-occurrence features presented in multispectral images. Experimental results demonstrate that this novel approach can effectively alleviate the ill-posed inverse problem of image SR by taking into account complementary information from an additional low-cost channel, significantly outperforming state-of-the-art SR approaches in terms of both accuracy and efficiency.

Multi-Agent Position Estimation in Modular Motor Drives Using Low-Resolution Sensors

Because a modular motor drive is composed of multiple identical pole drive units and controllers, it can be considered as a multi-agent system. This makes modular motor drives particularly interesting during faults, when the remaining healthy agents assure the continuity of operation. Nevertheless, for some crucial information such as the rotor position, these drives still rely on a single sensor which introduces a single point of failure. In this work, this single high-resolution position sensor is replaced by multiple binary, low-resolution position sensors. A vector tracking observer algorithm, implemented in the different controllers, processes this low-resolution sensor data into a position estimate. Subsequently, these position estimates of the different agents are exchanged with other agents using a distributed averaging algorithm. For this latter approach, it is shown in this work that it improves the position estimation under healthy conditions as well as during agent malfunctions. The concept is demonstrated on a modular axial-flux permanent magnet synchronous machine with fifteen pole drive units, each equipped with a low-resolution position sensor, that are assigned to five agents.

Distributed Bayesian vector estimation using noise-optimized low-resolution sensor observations

The distributed Bayesian vector parameter estimation problem based on low-resolution observations is investigated in a network, where each node represents an ensemble of estimates from a large number of sensors. A noise-enhanced Bayesian vector estimator that benefits from artificially added noise is proposed. For a network node composed of a sufficiently large number of identical low-resolution sensors, a lemma governing the weight coefficients is proven, and low-cost calculation expressions of the designed estimator and its Bayesian mean square error (MSE) are derived by avoiding costly computations due to high-dimensional matrix inversions. Experimental results show that by intentionally adding an appropriate amount of noise to networks of the low-resolution sensors, the MSE of the designed Bayesian vector estimator can be significantly reduced.

RESEARCH OF THE METHOD OF INCREASING THE OBJECT DETERMINATION ACCURACY ON THE LOW-RESOLUTION VIDEO STREAM

Study subject. The article proposes and investigates a method for increasing the accuracy of determination of the distance and the obstacle geometric parameters based on object contours determination using a computer vision system that uses low-resolution sensors. The goal is the effectiveness evaluation of the proposed method. Tasks: to conduct experimental researches of the quality indicators of the method of increasing the object contours determination accuracy; evaluate the effectiveness of this method. Used methods: statistical modeling, laboratory scale tests. The obtained results: the analysis of the proposed method efficiency was carried out and the influence of this method on the determination accuracy of the distance and object geometric parameters was evaluated. Conclusions: the considered method made it possible to achieve the increasing the determination accuracy of the distance and geometric object parameters by compensating for image blur using the Lucy-Richardson deconvolution algorithm. The obtained data showed a decrease in the maximum error in determining the distance from 8% to 4% and the error in the geometric object parameters from 7.7% to 5.8%. The implementation of this approach was carried out in the Python programming language.

Temperature measurement assisted modal tracking of an instrumented building

In Structural Health Monitoring (SHM), the instrumentation policies for buildings usually require large numbers of high-resolution sensors. The relatively high economic cost for the sensors and their interconnecting cables has discouraged the widespread application of SHM. For modal analysis, small number of sensors means low spatial resolution of mode shapes, which limits the possibility of differentiating between close frequency modes in a single measurement and between consecutive measurements on a continuous monitoring system. In this study, we try to overcome this limitation, particularly on the tracking component of SHM, with the aid of a predictive numerical model based on the effect of the ambient temperature on the modal frequency. To validate this hypothesis, we test it on a real building, monitored with a small number of low-resolution sensors. The methodology’s performance is evaluated during normal conditions, rainfall and also after the damaging Chile Maule earthquake (Mw of 8.8), proving to be effective for tracking purposes. For this building, the sensitivity of the mode shape and damping to the environment is also studied, showing a very low sensitivity as compared to the frequency. This validates the simpler temperature – frequency model.

Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures

The automation of typically intelligent and decision-making processes in the maritime industry leads to fewer accidents and more cost-effective operations. However, there are still lots of challenges to solve until fully autonomous systems can be employed. Artificial Intelligence (AI) has played a major role in this paradigm shift and shows great potential for solving some of these challenges, such as the docking process of an autonomous vessel. This work proposes a lightweight volumetric Convolutional Neural Network (vCNN) capable of recognizing different docking-based structures using 3D data in real-time. A synthetic-to-real domain adaptation approach is also proposed to accelerate the training process of the vCNN. This approach makes it possible to greatly decrease the cost of data acquisition and the need for advanced computational resources. Extensive experiments demonstrate an accuracy of over 90% in the recognition of different docking structures, using low resolution sensors. The inference time of the system was about 120ms on average. Results obtained using a real Autonomous Surface Vehicle (ASV) demonstrated that the vCNN trained with the synthetic-to-real domain adaptation approach is suitable for maritime mobile robots. This novel AI recognition method, combined with the utilization of 3D data, contributes to an increased robustness of the docking process regarding environmental constraints, such as rain and fog, as well as insufficient lighting in nighttime operations.

A Novel Approach Based on a Hierarchical Multiresolution Analysis of Optical Time Series to Reconstruct the Daily High-Resolution Snow Cover Area

High-resolution (HR) snow cover maps derived by remotely sensed images are an asset for data assimilation in hydrological models. However, the current satellite missions do not provide daily HR multispectral observations suitable for an accurate snow monitoring in alpine environments. On the contrary, low-resolution (LR) sensors acquire daily information of snow cover fraction (SCF) but at an inappropriate spatial scale. This article proposes a novel approach that combines multisource and multiscale acquisitions to infer the daily HR snow cover area (SCA) for mountainous basins. The approach builds on the assumption that interannual snow patterns are both affected by the local geomorphometry and meteorology. We derive these patterns through a hierarchical multistep approach based on historical statistical analyses on a long and sparse HR time-series. At each step, we obtain HR snow cover maps with higher number of reconstructed pixels but decreasing level of confidence. Historical data are used to estimate the probability that a HR pixel is covered by snow according to two possible multiscale strategies: 1) HR gap-filling, or 2) LR downscaling. These analyses lead to the identification of the patterns that regularly appear given certain conditions. When no systematic patterns are observed, we reinforce the inference of the pixel class by a generalized additive model that exploits not only the historical data, but also explicit geomorphometric, global snow, and multitemporal properties. The proposed approach has been validated on a catchment in the Sierra Nevada, USA, for three hydrological years (2017–2019) showing an average overall accuracy of 92%.

Variable Forgetting Factor-Based Adaptive Kalman Filter With Disturbance Estimation Considering Observation Noise Reduction

This paper addresses the influence reduction of quantization and observation noises in a disturbance observer (DOB) technique. DOB is a disturbance estimation method that makes control systems robust. However, in implementing low-resolution sensors, disturbance estimates from DOB are considerably influenced by observation and quantization noises. In this paper, a novel DOB design method for simultaneous estimation of state and unknown disturbances, including the reduction of noise influences, is proposed. The proposed method is divided into two components. The first component is a Kalman filter (KF)-based DOB for simultaneous estimation of state and unknown disturbances. To improve the estimation performance through the KF-based DOB, a forgetting factor-based adaptive KF (FAKF) was employed. The second component is an adaptive law for the forgetting factor in the FAKF. The adaptive law is used for balancing the estimation accuracy and observation noise reduction. Simulation results involving various types of noise environments demonstrate the effectiveness of the proposed method.

Wire-Mesh Sensor Super-Resolution Based on Statistical Reconstruction

A wire-mesh sensor (WMS) is a widely used instrument to visualize and estimate derived parameters of multiphase flows, e.g., gas void fraction or liquid hold-up. The spatial resolution of obtained flow images is associated with the number of crossing points formed by the transmitter and receiver wires of a given sensor. This may be a limitation for applications that require high spatial resolution since WMS is an intrusive device and the increase of electrodes may increase pressure drop and deform/fragment bubbles. In order to minimize such undesirable effects and maximize the sensor resolution, we employed a reconstruction algorithm based on the minimum mean-square error (MMSE) estimator to increase image resolution of WMS with fewer wires than commonly reported in the literature, i.e., here, we apply <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\times 6$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> sensors for 1-in pipe. Since standard regularization approaches may provide incorrect solutions for such configurations, a new methodology to obtain the prior model is presented. In our approach, the prior is assumed as a multivariate Gaussian model, which is extracted from experimental flow data of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> WMS (the most common resolution for 1-in pipe). Finally, the sensitivity matrix obtained by electric field simulation and the experimental prior model is incorporated into the MMSE algorithm to restore experimental flow data of the low-resolution sensors. The experiments were performed in a flow loop operating at slug flow. The experimental results suggest that the MMSE estimator combined with the experimental prior model has a high potential not only to improve image resolution but also to correct the average void fraction estimation.

Using deep neural networks to diagnose engine pre-ignition

Engine downsizing and boosting have been recognized as effective strategies for improving engine efficiency. However, operating the engines at high load promotes abnormal combustion events, such as pre-ignition and potential superknock. Currently the most effective method for detecting pre-ignition is by using in-cylinder pressure sensors that have high precision and sensitivity, but also high cost. Due to rapid advances in automotive technology such as autonomous driving, computer-aided designs and future connectivity, we propose to use a complimentary data-driven strategy for diagnosing abnormal combustion events. To this end, a data-driven diagnostics approach for pre-ignition detection with deep neural networks is proposed. The success of convolutional neural networks (CNNs) in object detection and recurrent neural networks (RNNs) in sequence forecasting inspired us to develop these models for pre-ignition detection. For a cost-effective strategy, we use data from less expensive sensors, such as lambda and low-resolution exhaust back pressure (EBP), instead of high resolution in-cylinder pressure measurements. The first deep learning model is combined with a commonly used dimensionality reduction tool–Principal Component Analysis (PCA). The second model eliminates this step and directly processes time-series data. Results indicate that the first model with reduced input dimensions, and correspondingly smaller size of the network, shows better performance in detecting pre-ignition cycles with an F1 score of 79%. Overall, the proposed deep learning approach is a promising alternative for abnormal combustion diagnostics using data from low resolution sensors.