Pixel-based and object-based change detection methods for assessing fuel break maintenance

2020 ◽  
Author(s):  
Joao E. Pereira-Pires ◽  
Valentine Aubard ◽  
Joao M. N. Silva ◽  
Rita A. Ribeiro ◽  
Jose M. C. Pereira ◽  
...  
Keyword(s):  
Change Detection ◽  
Detection Methods ◽  
Object Based

scholarly journals Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

2019 ◽  
Vol 11 (5) ◽  
pp. 570 ◽  
Author(s):  
Inacio Bueno ◽  
Fausto Acerbi Júnior ◽  
Eduarda Silveira ◽  
José Mello ◽  
Luís Carvalho ◽  
...  
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Seasonal Changes ◽  
Vegetation Indices ◽  
Detection Methods ◽  
Object Based Image Analysis ◽  
Spectral Bands ◽  
Object Based ◽  
Shortwave Infrared ◽  
Spectral Channels

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series.

scholarly journals An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

2015 ◽  
Vol 8 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Daniel Hölbling ◽  
Barbara Friedl ◽  
Clemens Eisank
Keyword(s):  
Change Detection ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Detection Methods ◽  
Detection And Attribution ◽  
Object Based ◽  
Wide Range ◽  
Northern Taiwan

Abstract Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

scholarly journals A supervised method for object-based 3D building change detection on aerial stereo images

2014 ◽  
Vol XL-3 ◽  
pp. 259-264 ◽  
Author(s):  
R. Qin ◽  
A. Gruen
Keyword(s):  
Change Detection ◽  
Current Trend ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Status Change ◽  
Aerial Photos ◽  
Object Based ◽  
The Status

There is a great demand for studying the changes of buildings over time. The current trend for building change detection combines the orthophoto and DSM (Digital Surface Models). The pixel-based change detection methods are very sensitive to the quality of the images and DSMs, while the object-based methods are more robust towards these problems. In this paper, we propose a supervised method for building change detection. After a segment-based SVM (Support Vector Machine) classification with features extracted from the orthophoto and DSM, we focus on the detection of the building changes of different periods by measuring their height and texture differences, as well as their shapes. A decision tree analysis is used to assess the probability of change for each building segment and the traffic lighting system is used to indicate the status "change", "non-change" and "uncertain change" for building segments. The proposed method is applied to scanned aerial photos of the city of Zurich in 2002 and 2007, and the results have demonstrated that our method is able to achieve high detection accuracy.

Land Cover Change Detection from Remotely Sensed IoT Data for Assessment of Land Degradation: A Survey

2021 ◽  
Vol 20 (Supp01) ◽  
pp. 2140011
Author(s):  
Rohini Selvaraj ◽  
Suresh Kumar Nagarajan
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Land Degradation ◽  
Vital Role ◽  
Detection Methods ◽  
Detection Techniques ◽  
The Earth ◽  
Object Based ◽  
Effective Manner ◽  
Land Changes

As the contamination over the surface of the earth is increasing exponentially, the land cover and land use detection techniques are considered as important elements in mapping and monitoring the land degradation. Remote sensing plays a vital role in identifying the land changes over the period of time. As land degradation occurs, resource demand will increase and reliable service to achieve land neutrality will increase. Connected device (IoT) could be used to achieve this neutrality in an intelligent and effective manner. Innumerable change detection methods have been developed for as far back as five decades. These studies deal in detail about the different satellite imagery data, image preprocessing techniques and the discussion of pixel-based and object-based change detection techniques. In addition, the dataset, preprocessing and change detection technique are interrelated with each other and their connection between the techniques are clarified dependent on the element of image analysis. The merits and limitation of different methods are also explained in detail.

scholarly journals Neyman-Pearson Criterion-Based Change Detection Methods for Wavelength-Resolution SAR Image Stacks

2021 ◽  
pp. 1-5
Author(s):  
Dimas I. Alves ◽  
Cristian Muller ◽  
Bruna G. Palm ◽  
Mats I. Pettersson ◽  
Viet T. Vu ◽  
...  
Keyword(s):  
Change Detection ◽  
Detection Methods ◽  
Sar Image ◽  
Pearson Criterion ◽  
Wavelength Resolution

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

scholarly journals Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

2021 ◽  
Vol 10 (6) ◽  
pp. 367
Author(s):  
Simoni Alexiou ◽  
Georgios Deligiannakis ◽  
Aggelos Pallikarakis ◽  
Ioannis Papanikolaou ◽  
Emmanouil Psomiadis ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Change Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Value ◽  
Detection Methods ◽  
Mountainous Catchments ◽  
Aerial Vehicle ◽  
Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

Navigational Data Change Detection Based on Rasterizing Features & Attributes

2012 ◽  
Vol 256-259 ◽  
pp. 2279-2284
Author(s):  
Lian Ying Li ◽  
Zhang Huang ◽  
Xiao Lan Xu
Keyword(s):  
Change Detection ◽  
Detection Methods ◽  
Vehicle Navigation ◽  
Digital Map ◽  
Navigation Data ◽  
Data Production ◽  
History Of ◽  
Map Data ◽  
Data Updating

A necessary updating degree is vital for the digital map data in a vehicle navigation system. Only when the digital map data are well updated, can the quality of the navigation be assured. Today the companies devoting to the production of digital map data for vehicle navigation have to cost much labor, material and capital to collect and update data in order to maintain a necessary updating degree. Throughout the history of electronic navigation data updating, they have made considerable progress both on the methods and processes of data production, and the way of map management. Updating from the CD to the network, from the wired to the wireless, from the replacing to the incremental way, each of the technical changes is a power source to enhance the data updating rate. As we all know, the change detection is a prerequisite and base for the electronic navigation data updating. By rapidly developing the area with changes and using the appropriate updating method, we can scientifically maintain the original database of navigation data and terminal physical data. In view of this, starting from application needs for dynamic data updating, this paper analyses change detection methods of navigation data in different versions used for generating incremental data, and focuses on that of rasterizing features and attributes, exploring a new approach to quickly get the incremental data between versions.

Sign in / Sign up

Export Citation Format

Share Document