scholarly journals Adaptive multi-threshold object selection in remote sensing images

2020 ◽  
pp. 12-24
Author(s):  
Vladimir Volkov
Keyword(s):  
Remote Sensing ◽  
A Priori ◽  
Detection Threshold ◽  
Medical Diagnostics ◽  
Morphological Processing ◽  
False Alarms ◽  
Material Research ◽  
Test Model ◽  
Object Selection ◽  
Geometric Characteristics

    Introduction: Detection, selection and analysis of objects of interest in digital images is a major problem for remote sensing and technical vision systems. The known methods of threshold detection and selection of objects avoid using the processing results, therefore not providing a low probability of false alarms, and not keeping the shape of the selected objects well enough. There are only few results from the studies about quantifying the quality of such algorithms on either model or real images. Purpose: Studying the effectiveness of algorithms for detecting, selecting, and localizing objects of interest using their geometric characteristics, when the object properties and background are a priori uncertain, and the shape of the selected objects is kept unchanged. Results: We have obtained and studied the characteristics of algorithms for detecting and selecting objects of interest on test models of monochrome images. These software-implemented algorithms use multi-threshold processing, providing a set of binary slices. This makes it possible to perform morphological processing of objects on each slice in order to analyze their geometric characteristics and then select them according to geometric criteria, taking into account the percolation effect which causes changes in the area, and fragmentation of the objects. As a result of analyzing these changes, an adaptive detection threshold is set for each of the selected objects. The selection allows you to significantly reduce the number of false positives during the detection and to use lower thresholds, increasing the correct detection probability. We present the detection characteristics and the results of test model processing, as well as the results of object selection on a real television and radar image, confirming the effectiveness of the considered algorithms. Practical relevance: The proposed algorithms can more effectively select objects on images of various nature obtained in remote sensing, material research or medical diagnostics systems. Their microprocessor implementation is much simpler than the implementation of universal trainable neural network algorithms.

scholarly journals Remote sensing detection enhancement

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tian J. Ma
Keyword(s):  
Remote Sensing ◽  
Moving Objects ◽  
Signal To Noise Ratio ◽  
Detection Threshold ◽  
False Alarms ◽  
Camera Motion ◽  
Low Contrast ◽  
Low Snr ◽  
Number Of False Alarms ◽  
Remote Sensor

AbstractBig Data in the area of Remote Sensing has been growing rapidly. Remote sensors are used in surveillance, security, traffic, environmental monitoring, and autonomous sensing. Real-time detection of small moving targets using a remote sensor is an ongoing, challenging problem. Since the object is located far away from the sensor, the object often appears too small. The object’s signal-to-noise-ratio (SNR) is often very low. Occurrences such as camera motion, moving backgrounds (e.g., rustling leaves), low contrast and resolution of foreground objects makes it difficult to segment out the targeted moving objects of interest. Due to the limited appearance of the target, it is tough to obtain the target’s characteristics such as its shape and texture. Without these characteristics, filtering out false detections can be a difficult task. Detecting these targets, would often require the detector to operate under a low detection threshold. However, lowering the detection threshold could lead to an increase of false alarms. In this paper, the author will introduce a new method that improves the probability to detect low SNR objects, while decreasing the number of false alarms as compared to using the traditional baseline detection technique.

Combining remote sensing and hydrological information for improving hydrological characterization of dehesas in Mediterranean mountain areas: a study case in Cardeña-Montoro Natural Park (Spain) 

2021 ◽  
Author(s):  
Rafael Pimentel ◽  
Pedro Torralbo ◽  
Javier Aparicio ◽  
María José Pérez-Palazón ◽  
Ana Andreu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Waves ◽  
A Priori ◽  
Regional Government ◽  
Livestock Farming ◽  
Spatiotemporal Resolution ◽  
Mountain Areas ◽  
Natural Park ◽  
Mediterranean Shrubs

<p>Mediterranean mountain areas are especially vulnerable to changes. Climatic trends observed in the last decades point out to an increasing number of extreme events (i.e., number of heat waves and droughts) and consequently, a direct alteration of the hydrological states of their associated ecosystems. The savanna type ecosystem called <em>dehesa</em> is one of them. This system is the result of a long-term co-evolution of indigenous ecosystems and human settlement in a sustainable balance, with high relevance from both the environmental (biodiversity) and socioeconomic (livestock farming, including Iberian pork food industry) point of view. <em>Dehesa </em>systems have a complex vegetation cover structure, where isolated trees, mainly holm oak, cork oak and oak, Mediterranean shrubs, and pastures coexist. Different problems have arisen in <em>dehesa</em> during last years, an example of them are seca episodes, a disease of oak trees that results in drying and final death. This condition is caused by a fungus, but very likely triggered by external hydrological related conditions like air temperature and soil water content.  Remote sensing techniques have been widely used as the best alternative to monitor vegetation patterns over these areas. However, the presence of clouds and the fixed spatiotemporal resolution of these sensors constitute a limitation in more local studies.</p><p>This work proposes the combined use of remote sensing by both terrestrial photography and satelital sensors, and hydrometeorological information as data sources for improving the hydrological characterization of vegetation in <em>dehesa</em> areas. The study was carried out in the Santa Clotilde experimental area, within the Cardeña-Montoro Natural Park (southern Spain). Three years of local sub-daily terrestrial photography and hydrometeorological information allowed us to define different hydrometeorological/ecohydrological indicators that are representative of key vegetation states. This local information is linked with vegetation indexes derived from high spatial resolution satellite information (i.e., Landsat TM, ETM+ and OLI (30 m x 30 m) and Sentinel-2 (10 m x 10 m) and distributed meteorological variables to extend the results from the local to the watershed scale. The promising results will be used in a short future as the basis of an advanced monitoring service where meteorological seasonal forecast information could be used to derive key indicators and help in a priori diagnosis of the system facilitating decisions making.</p><p>This work has been funded by project SIERRA Seguimiento hIdrológico de la vEgetación en montaña mediteRránea mediante fusión de sensores Remotos en Andalucía), with the economic collaboration of the European Funding for Rural Development (FEDER) and the Office for Economy, Knowledge, Enterprises and University of the Andalusian Regional Government.</p>

Algorithms for detecting broadband signals with a constant level of false alarms

2021 ◽  
Author(s):  
I.F. Lozovskiy
Keyword(s):  
Detection Threshold ◽  
Signal Amplitude ◽  
Rayleigh Distribution ◽  
Training Sample ◽  
Detection Algorithm ◽  
Constant Level ◽  
False Alarms ◽  
Interference Power ◽  
Number Of False Alarms ◽  
Distribution Type

The use of broadband souding signals in radars, which has become real in recent years, leads to a significant reduction in the size of resolution elements in range and, accordingly, in the size of the window in which the training sample is formed, which is used to adapt the detection threshold in signal detection algorithms with a constant level of false alarms. In existing radars, such a window would lead to huge losses. The purpose of the work was to study the most rational options for constructing detectors with a constant level of false alarms in radars with broadband sounding signals. The problem was solved for the Rayleigh distribution of the envelope of the noise and a number of non-Rayleigh laws — Weibull and the lognormal, the appearance of which is associated with a decrease in the number of reflecting elements in the resolution volume. For Rayleigh interference, an algorithm is proposed with a multi-channel in range incoherent signal amplitude storage and normalization to the larger of the two estimates of the interference power in the range segments. The detection threshold in it adapts not only to the interference power, but also to the magnitude of the «power jump» in range, which allows reducing the number of false alarms during sudden changes in the interference power – the increase in the probability of false alarms did not exceed one order of magnitude. In this algorithm, there is a certain increase in losses associated with incoherent accumulation of signals reflected from target elements, and losses can be reduced by certain increasing the size of the distance segments that make up the window. Algorithms for detecting broadband signals against interference with non-Rayleigh laws of distribution of the envelope – Weibull and lognormal, based on the addition of the algorithm for detecting signals by non-linear transformation of sample counts into counts with a Rayleigh distribution, are studied. The structure of the detection algorithm remains unchanged in practice. The options for detectors of narrowband and broadband signals are considered. It was found that, in contrast to algorithms designed for the Rayleigh distribution, these algorithms provide a stable level of false alarms regardless of the values of the parameters of non-Rayleigh interference. To reduce losses due to interference with the distribution of amplitudes according to the Rayleigh law, detectors consisting of two channels are used, in which one of the channels is tuned for interference with the Rayleigh distribution, and the other for lognormal or Weibull interference. Channels are switched according to special distribution type recognition algorithms. In such detectors, however, there is a certain increase in the probability of false alarms in a rather narrow range of non-Rayleigh interference parameters, where their distribution approaches the Rayleigh distribution. It is shown that when using broadband signals, there is a noticeable decrease in detection losses in non-Rayleigh noise due to lower detection thresholds for in range signal amplitudes incoherent storage.

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

scholarly journals IMAGE SEGMENTATION AND OBJECT SELECTION BASED ON MULTI-THRESHOLD PROCESSING

2019 ◽  
Vol 22 (3) ◽  
pp. 24-35 ◽  
Author(s):  
Vladimir Yu. Volkov ◽  
Oleg A. Markelov ◽  
Mikhail I. Bogachev
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Test Methods ◽  
Object Identification ◽  
Detection Methods ◽  
Clustering Methods ◽  
Object Selection ◽  
Advantages And Disadvantages ◽  
Infrared Cameras ◽  
Threshold Processing

Introduction. Detection, isolation, selection and localization of variously shaped objects in images are essential in a variety of applications. Computer vision systems utilizing television and infrared cameras, synthetic aperture surveillance radars as well as laser and acoustic remote sensing systems are prominent examples. Such problems as object identification, tracking and matching as well as combining information from images available from different sources are essential. Objective. Design of image segmentation and object selection methods based on multi-threshold processing. Materials and methods. The segmentation methods are classified according to the objects they deal with, including (i) pixel-level threshold estimation and clustering methods, (ii) boundary detection methods, (iii) regional level, and (iv) other classifiers, including many non-parametric methods, such as machine learning, neural networks, fuzzy sets, etc. The keynote feature of the proposed approach is that the choice of the optimal threshold for the image segmentation among a variety of test methods is carried out using a posteriori information about the selection results. Results. The results of the proposed approach is compared against the results obtained using the well-known binary integration method. The comparison is carried out both using simulated objects with known shapes with additive synthesized noise as well as using observational remote sensing imagery. Conclusion. The article discusses the advantages and disadvantages of the proposed approach for the selection of objects in images, and provides recommendations for their use.

Estimating local CH4 emissions in the Upper Silesian Coal Basin using inverse modelling

2021 ◽  
Author(s):  
Sebastian Wolff ◽  
Friedemann Reum ◽  
Christoph Kiemle ◽  
Gerhard Ehret ◽  
Mathieu Quatrevalet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
A Priori ◽  
Point Sources ◽  
Airborne Lidar ◽  
Inverse Modelling ◽  
Emission Rates ◽  
Coal Basin ◽  
Upper Silesian Coal Basin ◽  
The Individual ◽  
Modelling Approach

<p>Methane (CH<sub>4</sub>) is the second most important anthropogenic greenhouse gas (GHG) with respect to radiative forcing. Since pre-industrial times, the globally averaged CH<sub>4</sub> concentration in the atmosphere has risen by a factor of 2.5. A large fraction of global anthropogenic CH<sub>4</sub> emissions originates from localized point sources, e.g. coal mine ventilation shafts. International treaties foresee GHG emission reductions, entailing independent monitoring and verification support capacities. Considering the spatially widespread distribution of point sources, remote sensing approaches are favourable, in order to enable rapid survey of larger areas. In this respect, active remote sensing by airborne lidar is promising, such as provided by the integrated-path differential-absorption lidar CHARM-F operated by DLR. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for future satellite missions, e.g. MERLIN. CHARM-F simultaneously measures weighted vertical column mixing ratios of CO<sub>2</sub> and CH<sub>4</sub> below the aircraft. In spring 2018, during the CoMet field campaign, measurements were taken in the Upper Silesian Coal Basin (USCB) in Poland. The USCB is considered to be a European hotspot of CH<sub>4</sub> emissions, covering an area of approximately 50 km × 50 km. Due to the high number of coal mines and density of ventilation shafts in the USCB, individual CH<sub>4</sub> exhaust plumes can overlap. This makes simple approaches to determine the emission rates of single shafts, i.e. the cross-sectional flux method, difficult. Therefore, we use an inverse modelling approach to obtain an estimate of the individual emission rates. Specifically, we employ the Weather Research and Forecast Model (WRF) coupled to the CarbonTracker Data Assimilation Shell (CTDAS), an Ensemble Kalman Filter. CTDAS-WRF propagates an ensemble realization of the a priori CH<sub>4</sub> emissions forward in space and time, samples the simulated CH<sub>4</sub> concentrations along the measurement’s flight path, and scales the a priori emission rates to optimally fit the measured values, while remaining tied to the prior. Hereby, we obtain a regularized a posteriori best emission estimate for the individual ventilation shafts. Here, we report on the results of this inverse modelling approach, including individual and aggregated emission estimates, their uncertainties, and to which extent the data are able to constrain individual emitters independently.</p>

Ensuring the secrecy of the radar

2021 ◽  
Author(s):  
S.G. Vorona ◽  
S.N. Bulychev
Keyword(s):  
Likelihood Function ◽  
Radio Pulse ◽  
A Priori ◽  
Detection Threshold ◽  
Posteriori Probability ◽  
Echo Signal ◽  
A Posteriori Probability ◽  
Advantages And Disadvantages ◽  
Radio Electronic ◽  
Unknown Signal

The article deals with the issue of stealth of radio-electronic means, energy and structural, radio-electronic masking and ways of its implementation. The structure of the unknown signal for exploration and its parameters, as well as the a posteriori probability of each signal associated with the a priori likelihood function and the cases of its solution. The advantages and disadvantages of broadband signals and their characteristics used in modern radars are considered. On the basis of which conclusions are drawn: LFM radio pulse and a single FCM pulse, used in target tracking modes, has high resolution capabilities in range and radial velocity. The ACF of the FCM pulse has side lobes that raise the target detection threshold, as a result of which radar targets with a weak echo signal can be missed. The considered signals do not provide energy and structural stealth of the radar operation.

Optimal Detection and Estimation of Marine Oil Spills Through Coherent Pluralism

2013 ◽  
Vol 4 (1) ◽  
pp. 84-111
Author(s):  
Kufre Bassey ◽  
Polycarp Chigbu
Keyword(s):  
Remote Sensing ◽  
Environmental Science ◽  
Oil Spills ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Marine Oil ◽  
Detection And Estimation ◽  
Number Of False Alarms ◽  
Marine Oil Spills ◽  
Remote Sensing Techniques

An important area of environmental science involves the combination of information from diverse sources relating to a similar endpoint. Majority of optical remote sensing techniques used for marine oil spills detection have been reported lately of having high number of false alarms (oil slick look-a-likes) phenomena which give rise to signals which appear to be oil but are not. Suggestions for radar image as an operational tool has also been made. However, due to the inherent risk in these tools, this paper presents the possible research directions of combining statistical techniques with remote sensing in marine oil spill detection and estimation.

Sensorless Detection of Cavitation in Centrifugal Pumps

2006 ◽  
Author(s):  
Parasuram P. Harihara ◽  
Alexander G. Parlos
Keyword(s):  
Mechanical Load ◽  
A Priori ◽  
Detection Threshold ◽  
Cost Effective ◽  
Detection Algorithm ◽  
Design Parameters ◽  
Centrifugal Pumps ◽  
Detection Scheme ◽  
Pump Design ◽  
Electric Power Supply

Analysis of electrical signatures has been in use for some time for estimating the condition of induction motors, by extracting spectral indicators from motor current waveforms. In most applications, motors are used to drive dynamic loads, such as pumps, fans, and blowers, by means of power transmission devices, such as belts, couplers, gear-boxes. Failure of either the electric motors or the driven loads is associated with operational disruption. The large costs associated with the resulting idle equipment and personnel can often be avoided if the degradation is detected in its early stages prior to reaching failure conditions. Hence the need arises for cost-effective detection schemes not only for assessing the condition of the motor but also of the driven load. This prompts one to consider approaches that use no add-on sensors, in order to avoid any reduction in overall system reliability and increased costs. This paper presents an experimentally demonstrated sensorless approach to detecting varying levels of cavitation in centrifugal pumps. The proposed approach is sensorless in the sense that no mechanical sensors are required on either the pump or the motor driving the pump. Rather, onset of pump cavitation is detected using only the line voltages and phase currents of the electric motor driving the pump. Moreover, most industrial motor switchgear are equipped with potential transformers and current transformers which can be used to measure the motor voltages and currents. The developed fault detection scheme is insensitive to electric power supply and mechanical load variations. Furthermore, it does not require a priori knowledge of a motor or pump model or any detailed motor or pump design parameters; a model of the system is adaptively estimated on-line. The developed detection algorithm has been tested on data collected from a centrifugal pump connected to a 3 φ, 3 hp induction motor. Several cavitation levels are staged with increased severity. In addition to these staged pump faults, extensive experiments are also conducted to test the false alarm performance of the algorithm. Results from these experiments allow us to offer the conclusion that for the cases under consideration, the proposed model-based detection scheme reveals cavitation detection times that are comparable to those obtained from vibration analysis with a detection threshold that is significantly lower than used in industrial practice.

scholarly journals On the Use of Single-, Dual-, and Quad-Polarimetric SAR Observation for Landslide Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 384 ◽  
Author(s):  
Park ◽  
Lee
Keyword(s):  
Remote Sensing ◽  
Detection Rate ◽  
Detection Method ◽  
Incidence Angle ◽  
Accuracy Assessment ◽  
Temporal Variations ◽  
The Other ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Simple Change

Remote sensing technologies, particularly with Synthetic Aperture Radar (SAR) system, can provide timely and critical information to assess landslide distributions over large areas. Most space-borne SAR systems have been operating in different polarimetric modes to meet various operational requirements. This study aims to discuss how much detectability can be expected in the landslide map produced from the single-, dual-, and quad-polarization modes of observation. The experimental analysis of the characteristic changes of PALSAR-2 signals showed that quad-polarization parameters indicating signal depolarization properties revealed noticeable landslide-induced temporal changes for all local incidence angle ranges. To produce a landslide map, a simple change detection method based on characteristic scattering properties of landslide areas was proposed. The accuracy assessment results showed that the depolarization parameters, such as the co-pol coherence and polarizing contribution, can identify areas affected by landslides with a detection rate of 60%, and a false-alarm rate of 5%. On the other hand, the single- or dual-pol parameters can only be expected to provide half the accuracy with significant false-alarms in areas with temporal variations independent of landslides.

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