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

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.

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An Object-Based Change Detection Approach Using Uncertainty Analysis for VHR Images

Journal of Sensors ◽

10.1155/2016/9078364 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 5

Author(s):

Ming Hao ◽

Wenzhong Shi ◽

Kazhong Deng ◽

Hua Zhang ◽

Pengfei He

Keyword(s):

Change Detection ◽

Uncertainty Analysis ◽

Support Vector ◽

Detection Accuracy ◽

Data Sets ◽

Object Based ◽

Detection Approach ◽

The One ◽

Spot 5 ◽

Change Map

This paper proposes an object-based approach to supervised change detection using uncertainty analysis for very high resolution (VHR) images. First, two temporal images are combined into one image by band stacking. Then, on the one hand, the stacked image is segmented by the statistical region merging (SRM) to generate segmentation maps; on the other hand, the stacked image is classified by the support vector machine (SVM) to produce a pixel-wise change detection map. Finally, the uncertainty analysis for segmented objects is implemented to integrate the segmentation map and pixel-wise change map at the appropriate scale and generate the final change map. Experiments were carried out with SPOT 5 and QuickBird data sets to evaluate the effectiveness of proposed approach. The results indicate that the proposed approach often generates more accurate change detection maps compared with some methods and reduces the effects of classification and segment scale on the change detection accuracy. The proposed method supplies an effective approach for the supervised change detection for VHR images.

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Navigational Data Change Detection Based on Rasterizing Features & Attributes

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.256-259.2279 ◽

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.

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Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

Remote Sensing ◽

10.3390/rs10081285 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1285 ◽

Cited By ~ 20

Author(s):

Reza Attarzadeh ◽

Jalal Amini ◽

Claudia Notarnicola ◽

Felix Greifeneder

Keyword(s):

Soil Moisture ◽

Optical Data ◽

Estimation Accuracy ◽

Support Vector ◽

Practical Applications ◽

Initial Stage ◽

Object Based ◽

Single Polarization ◽

Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

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Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7110441 ◽

2018 ◽

Vol 7 (11) ◽

pp. 441 ◽

Cited By ~ 11

Author(s):

Zhenjin Zhou ◽

Lei Ma ◽

Tengyu Fu ◽

Ge Zhang ◽

Mengru Yao ◽

...

Keyword(s):

Coral Reef ◽

Change Detection ◽

Accuracy Assessment ◽

Detection Accuracy ◽

Reef Environment ◽

Object Based ◽

Coral Reef Environment ◽

Study Sites ◽

Object Area ◽

Segmentation Image

Despite increases in the spatial resolution of satellite imagery prompting interest in object-based image analysis, few studies have used object-based methods for monitoring changes in coral reefs. This study proposes a high accuracy object-based change detection (OBCD) method intended for coral reef environment, which uses QuickBird and WorldView-2 images. The proposed methodological framework includes image fusion, multi-temporal image segmentation, image differencing, random forests models, and object-area-based accuracy assessment. For validation, we applied the method to images of four coral reef study sites in the South China Sea. We compared the proposed OBCD method with a conventional pixel-based change detection (PBCD) method by implementing both methods under the same conditions. The average overall accuracy of OBCD exceeded 90%, which was approximately 20% higher than PBCD. The OBCD method was free from salt-and-pepper effects and was less prone to images misregistration in terms of change detection accuracy and mapping results. The object-area-based accuracy assessment reached a higher overall accuracy and per-class accuracy than the object-number-based and pixel-number-based accuracy assessment.

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The Study on High Precision 3D Detection Method of Steel Plate Surface Based on Structured Light

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.572.338 ◽

2012 ◽

Vol 572 ◽

pp. 338-342 ◽

Cited By ~ 1

Author(s):

Zhi Guo Liang ◽

Quan Yang ◽

Ke Xu ◽

Fei He ◽

Xiao Chen Wang ◽

...

Keyword(s):

Surface Quality ◽

Steel Plate ◽

Detection Method ◽

Structured Light ◽

Detection Methods ◽

Measurement Technology ◽

Detection Accuracy ◽

Plate Surface ◽

Factors Affecting

Structured light 3D measurement technology with its simple structure, non-contact measurement, fast measurement speed and other advantages, has been widely used. Steel plate surface quality detection is not confined to the two-dimensional feature of gray detection, and local topography measurement for surface quality of steel plate detection becomes increasingly important. In this paper, steel plate surface 3D detection method based on structured light and the factors affecting the measurement accuracy are analyzed. Several effective methods of improving 3D detection accuracy are put forward. Compared with the traditional structured light 3D detection methods, the detection accuracy of new methods is remarkably improved, thus possessing better application values.

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A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

Computational and Mathematical Methods in Medicine ◽

10.1155/2016/6169249 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Jiang Wu ◽

Yanju Ji ◽

Ling Zhao ◽

Mengying Ji ◽

Zhuang Ye ◽

...

Keyword(s):

Ovarian Cancer ◽

Cancer Detection ◽

Detection Methods ◽

Support Vector ◽

Detection Accuracy ◽

Analysis Method ◽

Data Set ◽

Specificity And Sensitivity ◽

Svm Model ◽

Tof Ms

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data.Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity.Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively.Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

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Benchmarking Steganalysis

Multimedia Forensics and Security ◽

10.4018/978-1-59904-869-7.ch013 ◽

2011 ◽

pp. 266-290

Author(s):

Andrew D. Ker

Keyword(s):

Detection Methods ◽

Statistical Techniques ◽

Detection Accuracy ◽

Performance Differences ◽

Fair Comparison ◽

Performance Envelope

This chapter discusses how to evaluate the effectiveness of steganalysis techniques. In the steganalysis literature, numerous different methods are used to measure detection accuracy, with different authors using incompatible benchmarks. Thus it is difficult to make a fair comparison of competing steganalysis methods. This chapter argues that some of the choices for steganalysis benchmarks are demonstrably poor, either in statistical foundation or by over-valuing irrelevant areas of the performance envelope. Good choices of benchmark are highlighted, and simple statistical techniques demonstrated for evaluating the significance of observed performance differences. It is hoped that this chapter will make practitioners and steganalysis researchers better able to evaluate the quality of steganography detection methods.

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Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

Remote Sensing ◽

10.3390/rs11020142 ◽

2019 ◽

Vol 11 (2) ◽

pp. 142 ◽

Cited By ~ 10

Author(s):

Wenping Ma ◽

Hui Yang ◽

Yue Wu ◽

Yunta Xiong ◽

Tao Hu ◽

...

Keyword(s):

Change Detection ◽

Representation Learning ◽

Local Information ◽

Learning Ability ◽

Detection Methods ◽

Detection Accuracy ◽

Image Block ◽

Sar Images ◽

Difference Image ◽

Multi Scale

In this paper, a novel change detection approach based on multi-grained cascade forest(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detectsthe changed and unchanged areas of the images by using the well-trained gcForest. Most existingchange detection methods need to select the appropriate size of the image block. However, thesingle size image block only provides a part of the local information, and gcForest cannot achieve agood effect on the image representation learning ability. Therefore, the proposed approach choosesdifferent sizes of image blocks as the input of gcForest, which can learn more image characteristicsand reduce the influence of the local information of the image on the classification result as well.In addition, in order to improve the detection accuracy of those pixels whose gray value changesabruptly, the proposed approach combines gradient information of the difference image with theprobability map obtained from the well-trained gcForest. Therefore, the image edge information canbe enhanced and the accuracy of edge detection can be improved by extracting the image gradientinformation. Experiments on four data sets indicate that the proposed approach outperforms otherstate-of-the-art algorithms.

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Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

Remote Sensing ◽

10.3390/rs10121987 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1987 ◽

Cited By ~ 5

Author(s):

Rocío Ramos-Bernal ◽

René Vázquez-Jiménez ◽

Raúl Romero-Calcerrada ◽

Patricia Arrogante-Funes ◽

Carlos Novillo

Keyword(s):

Change Detection ◽

Vegetation Index ◽

Thermal Emission ◽

Principal Component ◽

Detection Methods ◽

Detection Accuracy ◽

Data Types ◽

Statistical Parameters ◽

Wide Range ◽

The World

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

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

Remote Sensing ◽

10.3390/rs11050570 ◽

2019 ◽

Vol 11 (5) ◽

pp. 570 ◽

Cited By ~ 6

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.

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