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IEEE Conference on Advanced Video and Signal Based Surveillance

作者： Angiati, D Gera, G Piva, S Regazzoni, CS Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

ISBN: (纸本)0780393856

In recent decades vandal acts and graffiti drawing problem have increased and have required a lot of public funding. To face this problem the communal administrations have invested in automatic video surveillance systems. To deal with this problem through image processing techniques, this paper presents a method for graffiti detection based on change detection algorithm and motion vector. The aim of this system is to detect the graffiti painting act while people tire going to draw, identify them and distinguish the drawer.

关键词： image motion analysis object detection surveillance video signal processing automatic video surveillance systems change detection algorithm graffiti detection graffiti drawing problem image processing techniques motion vector

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A change detection algorithm FOR SAR IMAGES BASED ON LOGISTIC REGRESSION 39

A CHANGE DETECTION ALGORITHM FOR SAR IMAGES BASED ON LOGISTI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Molin, Ricardo D., Jr. Rosa, Rafael A. S. Bayer, Fabio M. Pettersson, Mats I. Machado, Renato ITA BR-12228900 Sao Jose Dos Campos SP Brazil Visiona Tecnol Espacial SA BR-12247016 Sao Jose Dos Campos SP Brazil Univ Fed Santa Maria BR-97105900 Santa Maria RS Brazil Blekinge Inst Technol BTH SE-37179 Karlskrona Sweden

ISBN: (纸本)9781538691540

This paper presents an incoherent change detection algorithm (CDA) for synthetic aperture radar (SAR) images based on logistic regression. The input data consists of a set of 24 SAR images acquired in a test site in northern Sweden [1]. Subsets of these images are trained based on pixel amplitude, flight heading and neighboring features such as local mean, standard deviation and skewness. The proposed method intends to explore the advantadges from both pixel- and object-based approaches, while evaluating multiple features in amplitude only SAR images. Preliminary results based on K-fold cross validation have shown that the proposed CDA achieves good performance when compared to the results presented in [1].

关键词： change detection algorithm Logistic Regression SAR images

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Statistical Sequential Analysis for detection of Microcalcifications in Digital Mammograms

Statistical Sequential Analysis for Detection of Microcalcif...

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International Conference on Signal Processing and Communications

作者： Zielinski, Jerzy Bouaynaya, Nidhal Univ Arkansas Dept Appl Sci Little Rock AR 72204 USA Univ Arkansas Dept Syst Engn Little Rock AR 72204 USA

ISBN: (纸本)9781424471379

We formulate the problem of microcalcification detection in digital mammograms as a statistical change detection problem in the local properties of the image. First, we represent mammograms by two-dimensional autoregressive moving-average (2D ARMA) fields;thus uniquely characterizing the images by their reduced dimensionality 2D ARMA feature vectors. Texture changes in mammograms are then modeled as an additive change in the mean parameter of the PDF associated with the 2D ARMA feature vector sequence that describes the image. A generalized likelihood ratio test is used to detect theses changes and estimate the exact time (or space) where they occur. Our simulation results on the Digital Database for Screening Mammography hosted by the University of South Florida show that the decision functions of cancerous images present high peaks at the microcalcification locations, whereas they exhibit a uniform behavior for healthy mammograms. The proposed algorithm achieves a sensitivity and specificity of 96.9% and 97.8%, respectively.

关键词： Breast microcalcification mammogram 2D-ARMA model change detection algorithm

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A Temporal-BRDF model-based approach to change detection

A Temporal-BRDF model-based approach to change detection

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IEEE International Geoscience and Remote Sensing Symposium

作者： Rebelo, L Lewis, P Roy, DP UCL Dept Geog 26 Bedford Way London WC1H 0AP England

ISBN: (纸本)0780387422

Remote Sensing provides the only practical means to monitor changes over large areas. This paper describes the development of a generic algorithm designed to map the temporal occurrence and spatial extent of areas exhibiting sudden change. The algorithm is demonstrated here applied to the problem of mapping fire affected areas. The research further develops the work of [1], which implemented a bi-directional reflectance (BRDF) model-based change detection algorithm to map the approximate day and location of burning, using daily 500m MODIS surface reflectance data. An original algorithm assumption is that the surface state remains static prior to the changes of interest. This is problematic in file presence of underlying change (for example, due to vegetation phenology) especially when there are missing and/or cloudy data. In an attempt to deal with this issue, an additional kernel has been added to the BRDF model in the form of a cubic function of time. In addition, a step function kernel has been introduced in order to more robustly detect step-like changes. These modifications and preliminary results over southern Africa using daily MODIS land surface reflectance data are presented.

关键词： topography (Earth) fires vegetation mapping BRDF model bidirectional reflectance model change detection algorithm burning location surface state vegetation phenology cloudy data step function kernel southern Africa MODIS land surface reflectance data remote sensing fire affected areas

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Surface Soil Moisture Estimation from Time Series of RADARSAT Constellation Mission Compact Polarimetric Data for the Identification of Water-Saturated Areas

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REMOTE SENSING 2024年第14期16卷 2664页

作者： Zakharov, Igor Kohlsmith, Sarah Hornung, Jon Charbonneau, Francois Bobby, Pradeep Howell, Mark C CORE Ottawa ON K2K 2A4 Canada Suncor Energy Serv Inc Calgary AB T2P 3E3 Canada Canada Ctr Mapping & Earth Observat CCMEO Canada Ctr Remote Sensing CCRS 580 Booth St Ottawa ON K1A 0E4 Canada C CORE St John NF A1B 3X5 Canada

Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium resolution, multi-temporal synthetic aperture radar (SAR). Understanding the distribution and identification of water-saturated areas using SAR soil moisture can be important for wetland mapping. The SAR soil moisture retrieval algorithm provides a relative assessment and requires calibration over wet and dry periods. In this work, relative soil moisture indicators are derived from a time series of the RADARSAT Constellation Mission (RCM) SAR compact polarimetric (CP) data over reclaimed areas of an oil sands mine in Alberta, Canada. An evaluation of the soil moisture product is performed using in situ measurements showing agreement from June to September. The surface scattering component of m-chi CP decomposition and the RL SAR products demonstrated a good agreement with the field data (low RMSE values and a perfect alignment with field-identified wetlands).

关键词： soil moisture wetland synthetic aperture radar (SAR) RCM change detection algorithm oil sands reclamation

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Retrieval of Surface Soil Moisture From Sentinel-1 Time Series for Reclamation of Wetland Sites

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 3569-3578页

作者： Zakharov, Igor Kapfer, Mark Hornung, Jon Kohismith, Sarah Puestow, Thomas Howell, Mark Henschel, Michael D. C CORE Ottawa ON K2K 2A4 Canada Suncor Energy Serv Inc Calgary AB T2P 3E3 Canada C CORE St John NF A1B 3X5 Canada

Soil moisture is a key factor in the reclamation of wetland habitats. Understanding the distribution and relative amount of water can be critical in reintroducing trees and grasses to disturbed soils. Soil moisture is also one of the main factors affecting microwave radar backscatter from the ground;while there are other factors determining backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, high resolution, multitemporal synthetic aperture radar (SAR). In this work, relative soil moisture indicators are derived from a time series of Sentinel-1 SAR data over previously mined oil sands in Alberta, Canada. The algorithm provides a relative assessment of soil moisture and requires calibration over wet and dry periods. An evaluation of the soil moisture product is validated using in situ measurements at multiple sites with observations showing agreement from May to August. Comparisons with precipitation records show that SAR derived surface soil moisture is influenced by discreet precipitation events;that is, rainfall that is coincident with the satellite observation reduces the effectiveness of the measurement. The resulting algorithm controls for rain events by including local weather records to adjust estimates based on the known precipitation.

关键词： change detection algorithm oil sands reclamation Sentinel-1 soil moisture synthetic aperture radar (SAR)

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Passive/active microwave soil moisture change disaggregation using SMAPVEX12 data

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JOURNAL OF HYDROLOGY 2019年 574卷 1085-1098页

作者： Fang, Bin Lakshmi, Venkat Jackson, Thomas J. Bindlish, Rajat Colliander, Andreas Univ Virginia Dept Engn Syst & Environm Charlottesville VA 22904 USA ARS USDA Beltsville MD 20705 USA NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA

The SMAPVEX12 (Soil Moisture Active Passive (SMAP) Validation Experiment 2012) experiment was conducted during June-July 2012 in Manitoba, Canada with the goal of collecting remote sensing data and ground measurements for the development and testing of soil moisture retrieval algorithms under varying vegetation and soil conditions for the SMAP satellite. The aircraft based soil moisture data provided by the passive/active microwave sensor PALS (Passive and Active L-band System) has a nominal spatial resolution of 1600 m. However, this resolution is not compatible with agricultural, meteorological and hydrological studies that require high spatial resolutions and this issue can be solved by soil moisture disaggregation. The soil moisture disaggregation algorithm integrates radiometer soil moisture retrievals and high-resolution radar observations and it can provide soil moisture estimates at a finer scale than the radiometer data alone. In this study, a change detection algorithm was used for disaggregation of coarse resolution passive microwave soil moisture retrievals with radar backscatter coefficients obtained from the higher spatial resolution UAVSAR (Unmanned Air Vehicle Synthetic Aperture Radar) at crop field scale. The accuracy of the disaggregated change in soil moisture was evaluated using ground based soil moisture measurements collected during SMAPVEX12 campaign. The results showed that soil moisture spatial variabilities were better characterized by the disaggregated change in soil moisture estimates at 5 m/800 m resolution as well as a good agreement between disaggregated estimates and in situ measurements. It also showed that VWC (Vegetation Water Content) did not have a big impact on disaggregation algorithm performance, with R-2 of the disaggregated results ranging 0.628-0.794. The 5 m and 800 m resolution disaggregated soil moisture did no show significant difference in statistical performance variables.

关键词： SMAP Microwave soil moisture disaggregation Radar backscatter change detection algorithm

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Extracting Moving Objects More Accurately: A CDA Contour Optimizer

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第12期31卷 4840-4849页

作者： Gao, Fei Li, Yunyang Lu, Shufang Zhejiang Univ Technol Coll Comp Sci & Technol Lab Graph & Image Proc Hangzhou 310023 Peoples R China

In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing algorithm for change detection is proposed. We believe that some problems cannot be avoided in the area of change detection such as 1) region of moving object generated by change detection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the change detection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in *** are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github https://***/walty19950301/CDA-contour-optimizer.

关键词： change detection algorithms Image edge detection Feature extraction Unsupervised learning Supervised learning Semantics Optical filters Post-processing algorithm optimization method change detection algorithm edge detection motion detection

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Normalized algorithm for mapping and dating forest disturbances and regrowth for the United States

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2011年第2期13卷 236-245页

作者： He, Liming Chen, Jing M. Zhang, Shaoliang Gomez, Gustavo Pan, Yude McCullough, Kevin Birdsey, Richard Masek, Jeffrey G. Univ Toronto Dept Geog Toronto ON M5S 3G3 Canada Univ Toronto Program Planning Toronto ON M5S 3G3 Canada China Univ Min & Technol Xuzhou 221008 Jiangsu Peoples R China Ryerson Univ Toronto ON M5B 2K3 Canada US Forest Serv USDA Newtown Sq PA 19073 USA NASA Goddard Space Flight Ctr Biospher Sci Branch Code 614 4 Greenbelt MD 20771 USA

Forest disturbances such as harvesting, wildfire and insect infestation are critical ecosystem processes affecting the carbon cycle. Because carbon dynamics are related to time since disturbance, forest stand age that can be used as a surrogate for major clear-cut/fire disturbance information has recently been recognized as an important input to forest carbon cycle models for improving prediction accuracy. In this study, forest disturbances in the USA for the period of similar to 1990-2000 were mapped using 400+ pairs of re-sampled Landsat TM/ETM scenes in 500m resolution, which were provided by the Landsat Ecosystem Disturbance Adaptive Processing System project. The detected disturbances were then separated into two five-year age groups, facilitated by Forest Inventory and Analysis (FIA) data, which was used to calculate the area of forest regeneration for each county in the USA. In this study, a disturbance index (DI) was defined as the ratio of the short wave infrared (SWIR, band 5) to near-infrared (NIR, band 4) reflectance. Forest disturbances were identified through the Normalized Difference of Disturbance Index (NDDI) between circa 2000 and 1990, where a positive NDDI means disturbance and a negative NDDI means regrowth. Axis rotation was performed on the plot between DIs of the two matched Landsat scenes in order to reduce any difference of DIs caused by non-disturbance factors. The threshold of NDDI for each TM/ETM pair was determined by analysis of FIA data. Minor disturbances affecting small areas may be omitted due to the coarse resolution of the aggregated Landsat data, but the major stand-clearing disturbances (clear-cut harvest, fire) are captured. The spatial distribution of the detected disturbed areas was validated by Monitoring Trends in Burn Severity fire data in four States of the western USA (Washington, Oregon, Idaho, and California). Results indicate omission errors of 66.9%. An important application of this remote sensing-based disturbance

关键词： Forest disturbance The continental US Fire Logging Disturbance index change detection algorithm

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Testing the detection of large, secretive snakes using camera traps

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WILDLIFE SOCIETY BULLETIN 2023年第1期47卷

作者： Walkup, Danielle K. Ryberg, Wade A. Pierce, Josh B. Smith, Emlyn Childress, James East, Forrest Pierce, Brian L. Brown, Price Fielder, Corey M. Hibbitts, Toby J. Texas A&M Univ Nat Resources Inst AgriLife Water & Nat Resources Bldg1001 Holleman College Stn TX 77840 USA US Forest Serv USDA Southern Res Stn 506 Hayter St Nacogdoches TX 75965 USA US Forest Serv USDA 5325 Highway 8 Bentley LA 71407 USA 100 Renfro Dr Lufkin TX 75901 USA Texas A&M Univ Nat Resources Inst Dept Ecol & Conservat Biol Biodivers Res & Teaching Collect College Stn TX 77843 USA Texas A&M Univ Wildlife Fisheries & Ecol Sci Bldg2258 TAMU College Stn TX 77843 USA Texas A&M Univ Nat Resources Inst 1001 Holleman Dr East College Stn TX 77840 USA

Novel technologies, such as camera traps, have expanded the opportunities for species detection, especially for rare species. Corresponding changes in data processing must occur to handle the large volume of data gathered from technology like camera traps. Automated image data processing, usually by running images through different types of computer algorithms, is an overarching goal to reduce the number of images that researchers must manually review. However, differences in camera trap setups and species characteristics can make automatic processing a challenge. Here, we evaluated the detection accuracy and efficiency of a time-lapse triggered camera trapping technique combined with a pixel change detection algorithm as part of a monitoring program for a translocated population of the rare and federally threatened Louisiana Pinesnake (Pituophis ruthveni). We paired 5 cameras with automated pit tag readers to collect observations of P. ruthveni. We evaluated an image dataset of 1,500,187 images, collected over 7 months, both manually (i.e., researchers looking at each individual picture to determine snake presence) and automatically using a change detection algorithm. There were 18 P. ruthveni observations recorded by the tag readers, 7 of which occurred while a paired camera was not operational. Ten of the tag reader P. ruthveni observations were captured by the paired camera trap, with an additional P. ruthveni observation from a paired camera trap not recorded by the tag reader. There were 132 snake observations of 13 additional species and 18 observations of unknown snakes from the camera traps. The algorithm reduced the number of images reviewers evaluated by an average of 78.5% per camera (range = 37.3-98.7%) but had a 54.5% success rate at detecting observations of P. ruthveni (47.1% for individual images), and a slightly lower 48.9% success rate detecting other large snakes. Large snakes were 4 times more likely to be flagged by the algorithm than small sna

关键词： camera trap change detection algorithm conservation monitoring detection Louisiana Pinesnake Pituophis ruthveni

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