A comparative study on change vector analysis based change detection techniques

Singh, Sartajvir; Talwar, Rajneesh

doi:10.1007/s12046-014-0286-x

Cited by 25 publications

(14 citation statements)

References 37 publications

(48 reference statements)

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“…Compared with widely used methods, such as GWDM_EM and GWDM_FCM [33], CVA_EM [62], LSELUC [40], MRF_EM [14], and PCA_Kmeans [39], the proposed OBEM can more effectively refine the raw change detection results of the preprocessing change detection methods and obtain higher detection accuracies. In addition, the proposed OBEM approach can investigate the relationship between multi-scale segmentation parameter and refining change detection accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…To test the effectiveness of the proposed OBEM post-processing LCCD method, six classical preprocessing LCCD approaches are adopted for comparisons, namely, GWDM coupled with EM and fuzzy c-means clustering algorithm (FCM) (named GWDM_EM and GWDM_FCM, respectively) [33], CVA coupled with EM (CVA_EM) [62], LSELUC [40], MRF coupled with EM (MRF_EM) [14], and PCA_Kmeans clustering [39]. For the first and second data sets, the optimal parameters of each method are obtained by trial-and-error method.…”

Section: Experimental Setup and Parameter Settingmentioning

confidence: 99%

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Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

Lv¹,

Liu²,

Wan

et al. 2018

Remote Sensing

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Abstract:In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors' knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images.

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Section: Discussionmentioning

confidence: 99%

Section: Experimental Setup and Parameter Settingmentioning

confidence: 99%

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

Lv¹,

Liu²,

Wan

et al. 2018

Remote Sensing

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“…A few number of CVA based change detection procedures have proved good precision in detecting LULC multi-temporal changes [19][20][21][22][23]. Among the later methods, Principal Component Analysis (PCA), Change Vector Analysis (CVA), and Post Classification Comparison (PCC) are most promising techniques to accomplish change detection objectives.…”

Section: Introductionmentioning

confidence: 99%

A quantitative analysis of different change detection algorithms over rugged terrain MODIS sensor satellite imagery

Singh

Talwar²

2015

2015 International Conference on Signal Processing and Communication (ICSC)

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Remotely sensed data is only a key source of detection of Earth's surface changes or Land-Use/Land-Cover (LULC) monitoring. During past decades, a series of effective change detection techniques such as Principal Component Analysis (PCA), Change Vector Analysis (CVA) and Post Classification Comparison (PCC), have been developed to observe the LULC vicissitudes. All aforesaid techniques performed very well in some situations such as on horizontal land but very rarely experimented on rugged terrain satellite imagery because change detection procedures are very problematic for such study areas. From this perspective, this study comprises the quantitative analysis of different change detection techniques to study LULC changes linked with rugged terrain Moderate Resolution Imaging Spectroradiometer (MODIS) sensor satellite imagery. In addition to this, necessary pre-processing steps such as geometric correction, radiometric correction and topographic correction for flat surface as well as rugged terrain, have also been summarized to correct the estimated spectral reflectance value. Experiment outcomes confirms that CVA technique has greater potential (achieved accuracy assessment of 90% with Kappa coefficient of 0.8838) than PCC and PCA techniques (achieved accuracy assessment of 78-82% with Kappa coefficient of 0.6358-0.7537, respectively) to analyzed the overall transformed information over rugged terrain MODIS satellite imagery. It is projected that this will efficiently guide the natural hazard forecaster's or algorithm engineer's to precisely perceive the multi-temporal environment changes over Land-Use/Land-Cover rugged terrain.

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“…S 2 CVA is implemented iteratively, with multiple change information models and it is identified hierarchically in each iteration [13]. However, CVA-based methods have some disadvantages, such as difficulties in identifying multi-class changes and in selecting an appropriate threshold [14].Transformation-based methods can transform HSIs into other feature spaces to distinguish changes from non-changes, these methods assist in producing multivariate components based on the first few components [15]. Principal component analysis (PCA) exploits the variance in the principal components (PCs) of the combined multi-temporal HSIs [16].…”

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confidence: 99%

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Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

et al. 2018

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Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral-spatial and temporal modules. Particularly, a spectral-spatial module with a 3D convolutional layer extracts the spectral-spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral-spatial-temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM. from various platforms, such as aircraft, satellites, and unmanned aerial vehicles [1]. The spectral profiles obtained from HSIs help to achieve target detection, classification, as well as change detection (CD) because of the profiles ability to distinguish the spectrally similar materials and describe the finer spectral changes [2,3].CD is the process of identifying changes in land cover or land use that have occurred over time in the same geographical area [4]. Applications of CD techniques include assessing natural disasters, monitoring crops, and managing water resources [5][6][7]. Moreover, CD has offered the benefit of hyperspectral remote sensing over the past two decades. As HSIs offer more-detailed information about spectral changes than multispectral images, HSIs can improve the performance of CD [8]. Ho...

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A comparative study on change vector analysis based change detection techniques

Cited by 25 publications

References 37 publications

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

A quantitative analysis of different change detection algorithms over rugged terrain MODIS sensor satellite imagery

Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

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