Change Detection Methodology Research Articles

Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images.

The ability to measure minute structural changes in neural circuits is essential for long-term in vivo imaging studies. Here, we propose a methodology for detection and measurement of structural changes in axonal boutons imaged with time-lapse two-photon laser scanning microscopy (2PLSM). Correlative 2PLSM and 3D electron microscopy (EM) analysis, performed in mouse barrel cortex, showed that the proposed method has low fractions of false positive/negative bouton detections (2/0 out of 18), and that 2PLSM-based bouton weights are correlated with their volumes measured in EM (r = 0.93). Next, the method was applied to a set of axons imaged in quick succession to characterize measurement uncertainty. The results were used to construct a statistical model in which bouton addition, elimination, and size changes are described probabilistically, rather than being treated as deterministic events. Finally, we demonstrate that the model can be used to quantify significant structural changes in boutons in long-term imaging experiments.

A three-class change detection methodology for SAR-data based on hypothesis testing and Markov Random field modelling

ABSTRACTThis study presents a new automatic change detection process chain based on bi-temporal co-registered and calibrated Sentinel-1 level-1 Interferometric Wide Ground Range Detected C-band synthetic aperture radar intensity imagery. The whole processor contains three main components: firstly, a preprocessing step is used to perform geometrical and radiometrical calibration. Secondly, an automatic coarse detection step is applied based on a statistical hypothesis test to obtain an initial classification. Thirdly, a post-classification step is introduced to optimize the initial classification result in the form of minimizing a global energy function defined on a Markov Random Field. In this study, a graph-cut algorithm is applied iteratively to solve the global optimization problem. At each iteration, the data energy function for the current classification is set by the logarithmic probability density function. The relevant parameters are estimated by the method of logarithmic cumulants. Supplemental data is presented to explain the formulae used in this study. Experiments are performed using a flood event which occurred in 2015 along the coastline of Greece near Kavala region and the Evros River at the border between Greece and Turkey. The proposed method shows a satisfying classification result with overall accuracy above 95% and kappa coefficient () above 0.87.

Multi‐temporal synthetic aperture radar flood mapping using change detection

A change detection and thresholding methodology has been adapted from previous studies to determine the extent of flooding for 13 Sentinel‐1 synthetic aperture radar images captured during the floods of winter 2015–2016 in Yorkshire, UK. Both available polarisations, VH and VV, have been processed to allow for a comparison of their respective accuracy for delineating surface water. Peak flood extents are found on 29 December 2015 during the aftermath of storms Eva and Frank. Results have been validated against a Sentinel‐2 optical image, with both polarisations producing a total accuracy of 97%. Of the two polarisations, VV produces fewer misclassifications, mirroring the similar results reported in previous research. Mapped results are compared to the Environment Agency Flood Maps for Planning (EA FMP), with good correlation observed for inundation on the floodplains. Differences occur away from the floodplains, with the satellite data identifying pluvial flooding not highlighted by the EA FMP.

Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are comparable to those of a supervised method, supported by low detection of false positives and false negatives, along with a high overall accuracy and a high kappa index. On the other hand, the execution times were comparable to those of unsupervised methods of low computational load.

Performance evaluation of random forest and support vector regressions in natural hazard change detection

Information on land use and land cover changes is considered as a foremost requirement for monitoring environmental change. Developing change detection methodology in the remote sensing community is an active research topic. However, to the best of our knowledge, no research has been conducted so far on the application of random forest regression (RFR) and support vector regression (SVR) for natural hazard change detection from high-resolution optical remote sensing observations. Hence, the objective of this study is to examine the use of RFR and SVR to discriminate between changed and unchanged areas after a tsunami. For this study, RFR and SVR were applied to two different pilot coastlines in Indonesia and Japan. Two different remotely sensed data sets acquired by Quickbird and Ikonos sensors were used for efficient evaluation of the proposed methodology. The results demonstrated better performance of SVM compared to random forest (RF) with an overall accuracy higher by 3% to 4% and kappa coefficient by 0.05 to 0.07. Using McNemar’s test, statistically significant differences (Z≥1.96), at the 5% significance level, between the confusion matrices of the RF classifier and the support vector classifier were observed in both study areas. The high accuracy of change detection obtained in this study confirms that these methods have the potential to be used for detecting changes due to natural hazards.

Disaggregation of Low-Resolution L-Band Radiometry Using C-Band Radar Data

For Earth observation data to be useful for a wide range of land surface applications, a kilometer or finer resolution is required. Unfortunately, passive microwave observations at low microwave frequencies (1–10 GHz), already providing important information on soil moisture and vegetation dynamics, are generally only available at a resolution of tens of kilometers. This letter presents a new downscaling method relating L-band radiometer and C-band radar observations for downscaling purposes. The data were obtained from two extensive airborne field experiments across a 80 000-km2 catchment in south-eastern Australia and coinciding Envisat Advanced Synthetic Aperture Radar acquisitions, performed during the Austral summer and spring of 2010. The novel approach of this study is in the downscaling of coarse-scale emissivities as observed by the radiometer with a new interpretation of the change detection methodology for the radar signal to relate the spatiotemporal changes of those two types of observations at 1 km. It is shown that, for most land surface conditions, a good spatial representation at high resolution is achieved, without considering land surface specific parameterizations, which is promising for using very high resolution radar data from the Sentinel-1 platform for downscaling of passive microwave data from current missions, such as National Aeronautics and Space Administration's Soil Moisture Active Passive and European Space Agency's SMOS.

Turning Point Analysis를 이용한 실시간 교통량 변화 검지 방법론 개발

Turning Point Analysis를 이용한 실시간 교통량 변화 검지 방법론 개발

UPDATING NATIONAL TOPOGRAPHIC DATA BASE USING CHANGE DETECTION METHODS

Abstract. The traditional method for updating a topographic database on a national scale is a complex process that requires human resources, time and the development of specialized procedures. In many National Mapping and Cadaster Agencies (NMCA), the updating cycle takes a few years. Today, the reality is dynamic and the changes occur every day, therefore, the users expect that the existing database will portray the current reality. Global mapping projects which are based on community volunteers, such as OSM, update their database every day based on crowdsourcing. In order to fulfil user's requirements for rapid updating, a new methodology that maps major interest areas while preserving associated decoding information, should be developed. Until recently, automated processes did not yield satisfactory results, and a typically process included comparing images from different periods. The success rates in identifying the objects were low, and most were accompanied by a high percentage of false alarms. As a result, the automatic process required significant editorial work that made it uneconomical. In the recent years, the development of technologies in mapping, advancement in image processing algorithms and computer vision, together with the development of digital aerial cameras with NIR band and Very High Resolution satellites, allow the implementation of a cost effective automated process. The automatic process is based on high-resolution Digital Surface Model analysis, Multi Spectral (MS) classification, MS segmentation, object analysis and shape forming algorithms. This article reviews the results of a novel change detection methodology as a first step for updating NTDB in the Survey of Israel.

UPDATING NATIONAL TOPOGRAPHIC DATA BASE USING CHANGE DETECTION METHODS

The traditional method for updating a topographic database on a national scale is a complex process that requires human resources, time and the development of specialized procedures. In many National Mapping and Cadaster Agencies (NMCA), the updating cycle takes a few years. Today, the reality is dynamic and the changes occur every day, therefore, the users expect that the existing database will portray the current reality. Global mapping projects which are based on community volunteers, such as OSM, update their database every day based on crowdsourcing. In order to fulfil user's requirements for rapid updating, a new methodology that maps major interest areas while preserving associated decoding information, should be developed. <br><br> Until recently, automated processes did not yield satisfactory results, and a typically process included comparing images from different periods. The success rates in identifying the objects were low, and most were accompanied by a high percentage of false alarms. As a result, the automatic process required significant editorial work that made it uneconomical. In the recent years, the development of technologies in mapping, advancement in image processing algorithms and computer vision, together with the development of digital aerial cameras with NIR band and Very High Resolution satellites, allow the implementation of a cost effective automated process. The automatic process is based on high-resolution Digital Surface Model analysis, Multi Spectral (MS) classification, MS segmentation, object analysis and shape forming algorithms. This article reviews the results of a novel change detection methodology as a first step for updating NTDB in the Survey of Israel.

A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images

In this paper a novel integrated hybrid methodology for unsupervised change detection between Unmanned Aerial Vehicle (UAV) and satellite images, which can be utilized in various fields like security applications (e.g. border surveillance) and damage assessment, is proposed. This is a challenging problem mainly due to the difference in geographic coverage and the spatial resolution of the two images, as well as to the acquisition modes which lead to misregistration errors. The methodology consists of the following steps: (a) pre-processing, where the part of the satellite image that corresponds to the UAV image is determined and the UAV image is ortho-rectified using information provided by a Digital Terrain Model, (b) the detection of potential changes, which is based exclusively on intensity and image gradient information, (c) the generation of the region map, where homogeneous regions are produced by the previous potential changes via a seeded region growing algorithm and placed on the region map, and (d) the evaluation of the above regions, in order to characterize them as true changes or not. The methodology has been applied on demanding real datasets with very encouraging results. Finally, its robustness to the misregistration errors is assessed via extensive experimentation.

EVALUATION OF ANNUAL MODIS PTC DATA FOR DEFORESTATION AND FOREST DEGRADATION ANALYSIS

Abstract. Anthropogenic land-cover change, e.g. deforestation and forest degradation cause carbon emissions. To estimate deforestation and forest degradation, it is important to have reliable data on forest cover. In this analysis, we evaluated annual MODIS Percent Tree Cover (PTC) data for the detection of forest change including deforestation, forest degradation, reforestation and revegetation. The annual MODIS PTC data (2000 – 2010) were pre-processed by applying quality layer. Based on the PTC values of the annual MODIS data, forest change maps were produced and assessed by comparing with the data from visual interpretation of SPOT-5 images. The assessment was applied to two case-studies: Ayuquila Basin and Monarch Reserve. Results show that the detected deforestation patches by visual interpretation are roughly 4 times in quantity more than those by MODIS PTC data, which can be partially due to the much higher spatial resolution of SPOT-5, being able to pick up small deforestation patches. This analysis found poor spatial overlapping for both case-studies. Possible reasons for the discrepancy in quantity and spatial coincidence were provided. It is necessary to refine the methodology for forest change detection by PTC images; also to refine the validation data in terms of data periods and forest change categories to ensure a better assessment.

EVALUATION OF ANNUAL MODIS PTC DATA FOR DEFORESTATION AND FOREST DEGRADATION ANALYSIS

Anthropogenic land-cover change, e.g. deforestation and forest degradation cause carbon emissions. To estimate deforestation and forest degradation, it is important to have reliable data on forest cover. In this analysis, we evaluated annual MODIS Percent Tree Cover (PTC) data for the detection of forest change including deforestation, forest degradation, reforestation and revegetation. The annual MODIS PTC data (2000 – 2010) were pre-processed by applying quality layer. Based on the PTC values of the annual MODIS data, forest change maps were produced and assessed by comparing with the data from visual interpretation of SPOT-5 images. The assessment was applied to two case-studies: Ayuquila Basin and Monarch Reserve. Results show that the detected deforestation patches by visual interpretation are roughly 4 times in quantity more than those by MODIS PTC data, which can be partially due to the much higher spatial resolution of SPOT-5, being able to pick up small deforestation patches. This analysis found poor spatial overlapping for both case-studies. Possible reasons for the discrepancy in quantity and spatial coincidence were provided. It is necessary to refine the methodology for forest change detection by PTC images; also to refine the validation data in terms of data periods and forest change categories to ensure a better assessment.

Spectral indices based object oriented classification for change detection using satellite data

Change detection, using remotely sensed data can be utilized in a diversified way such as, land use and cover analysis, forest or vegetation assessment, and flood monitoring. The aim of this study is to develop a methodology for change detection in highly urbanized areas, using time-series satellite imagery. This paper analyzes the effectiveness of the object oriented classification over unsupervised algorithms such as k-means for the purpose of change detection. The study area selected is, National Capital Territory of Delhi, which is a good representative of the urban agglomeration conditions in the Asian region. A time series of Landsat 5 (TM) and Landsat 8 (OLI) imageries for the duration of 1993–2014, have been acquired in order to represent the wide range of pattern variation. This study uses three spectral indices namely, Normalized Difference Built-up Index to characterize built-up area, Modified Normalized Difference Water Index to signify open water and Modified Soil Adjusted Vegetation Index to symbolize green vegetation. Further, this study employs object-based environment that separates similar pixels spatially and spectrally at different scales and assign information class to segmented objects. Results recommends the use of OBIA as a semi-automated tool for classification of remotely sensed satellite data. Moreover, using OBIA along with traditional spectral indices, for the classification, provides dimensionality reduction. Moreover, such a method produces classified map which have great correspondence with reality, reflects in high accuracies that were achieved for classes like built-up. The statistics indicated that the classification with object oriented paradigm achieved an overall accuracy of up to 90.1 %, which is far better as compared to automated unsupervised clustering algorithm, such as k-means.

Front of pack labels enhance attention to nutrition information in novel and commercial brands

Objectives(1) To assess whether Front-of-Pack (FOP) nutrition labels garner attention more readily than more complete, mandated nutrition information (the Nutrition Facts Panel (NFP), required in the US), and (2) To determine whether label design characteristics, specifically, color coding and/or coding with facial icons, increase attention to the FOP label. MethodsIn two experiments, we tracked the allocation of attention while participants (n=125) viewed novel and commercial packages with varied FOP designs using a change detection methodology. ResultsWe found empirical evidence that FOP labels are attended more often, and earlier, than the currently mandated NFP, and that this benefit is due both to its placement on the front of the package and to the design characteristics of the FOP. Specifically, the use of color in FOPs increased attention to the label, but there was no evidence that coding information via facial icons impacted attention. ConclusionsOur work supports a growing body of evidence supporting the use of FOP labels to attract attention to nutritional information. Findings may be relevant to inform policy decisions on labeling standards.

Land Cover Change Detection in the Nakdong River Basin Using LiDAR Data and Multi-Temporal Landsat Imagery

This research is carried out for the land cover change detection in the Nakdong River basin before and after the 4 major rivers restoration project using the LiDAR DEM(Digital Elevation Model) and the multi-temporal Landsat imagery. Firstly the river basin polygon is generated by using the levee boundaries extracted from the LiDAR DEM, and the four river basin imagery are generated from the multi-temporal Landsat-5 TM(Thematic Mapper) and Landsat-8 OLI(Operational Land Imager) imagery by using the generated river basin polygon. Then the main land covers such as river, grass and bare soil are separately generated from the generated river basin imagery by using the image classification method, and the ratio of each land cover in the entire area is calculated. The calculated land cover changes show that the areas of grass and bare soil in the entire area have been significantly changed because of the seasonal change, while the area of the river has been significantly increased because of the increase of the water storage. This paper contributes to proposing an efficient methodology for the land cover change detection in the Nakdong River basin using the LiDAR DEM and the multi-temporal satellite imagery taken before and after the 4 major rivers restoration project.

Change Detection Techniques - A Survey

The data obtained from remote sensing satellites furnish information about the land at varying resolutions and has been widely used for change detection studies. There exist a huge number of change detection methodologies and techniques with the continual emergence of new ones. This paper provides a review of pixel based and object-based change detection techniques in conjunction with the comparison of their merits and limitations. The advent of very-high-resolution remotely sensed images, exponentially increased image data volume and multiple sensors demand the potential use of data mining techniques in tandem with object-based methods for change detection.

Optimizing Panchromatic Image Change Detection Based on Change Index Multiband Image Analysis

This work proposes an optimization of a semisupervised Change Detection methodology based on a combination of Change Indices (CI) derived from an image multitemporal data set. For this purpose, SPOT 5 Panchromatic images with 2.5 m spatial resolution have been used, from which three Change Indices have been calculated. Two of them are usually known indices; however the third one has been derived considering the Kullbak-Leibler divergence. Then, these three indices have been combined forming a multiband image that has been used in as input for a Support Vector Machine (SVM) classifier where four different discriminant functions have been tested in order to differentiate between change and no_change categories. The performance of the suggested procedure has been assessed applying different quality measures, reaching in each case highly satisfactory values. These results have demonstrated that the simultaneous combination of basic change indices with others more sophisticated like the Kullback-Leibler distance, and the application of non-parametric discriminant functions like those employees in the SVM method, allows solving efficiently a change detection problem.

Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries

Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. However, in order to handle the volume of data, a high level of automation is required. The semi-automatic change detection methodology described in this paper was developed to annually update land cover maps prepared in the context of the Geoland2. The proposed algorithm was tailored to work with different very high spatial resolution images acquired over different European landscapes. The methodology is a fusion of various change detection methods ranging from: (1) layer arithmetic; (2) vegetation indices (NDVI) differentiating; (3) texture calculation; and methods based on (4) canonical correlation analysis (multivariate alteration detection (MAD)). User intervention during the production of the change map is limited to the selection of the input data, the size of initial segments and the threshold for texture classification (optionally). To achieve a high level of automation, statistical thresholds were applied in most of the processing steps. Tests showed an overall change recognition accuracy of 89%, and the change type classification methodology can accurately classify transitions between classes.

Quickest Detection of Changes in the Generating Mechanism of a Time Series via the ε-Complexity of Continuous Functions

A novel methodology for the quickest detection of abrupt changes in the generating mechanisms (stochastic, deterministic, or mixed) of a time series, without any prior knowledge about them, is developed. This methodology has two components: the first is a novel concept of the ε-complexity and the second is a method for the quickest change point detection (Darkhovsky, 2013). The ε-complexity of a continuous function given on a compact segment is defined. The expression for the ε-complexity of functions with the same modulus of continuity is derived. It is found that, for the Hölder class of functions, there exists an effective characterization of the ε-complexity. The conjecture that the ε-complexity of an individual function from the Hölder class has a similar characterization is formulated. The algorithm for the estimation of the ε-complexity coefficients via finite samples of function values is described. The second conjecture that a change of the generating mechanism of a time series leads to a change in the mean of the complexity coefficients, is formulated. Simulations to support our conjectures and verify the efficiency of our quickest change point detection algorithm are performed.

Supervised Change Detection in Satellite Imagery Using Super Pixels and Relevance Feedback

The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques utilizing remotely sensed data have been developed, and newer techniques are still emerging. In this paper, a novel supervised approach of change detection using Support Vector Machine (SVM) and super pixels is proposed. In the formulation of change detection, SVM is modeled as a binary classifier to get the final output as “Change” and “No-Change” information. A relevant feedback mechanism is also included in to the change detection strategy so that it adapts in accordance with user preferences. Both ground truth and relevance feedback are collected using the developed GUIs. Comparison of the proposed approach with three other techniques of change detection is done via the experiments conducted on three multitemporal datasets. It is observed that the supervised, super pixel based change detection strategy gives superior results compared to traditional approaches of change detection. It is also seen that the usage of relevance feedback fine-tunes the results of change detection and acts as a desirable post-change detection process.