A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images

Bovolo, Francesca; Marchesi, Silvia; Bruzzone, Lorenzo

doi:10.1109/tgrs.2011.2171493

Cited by 276 publications

(131 citation statements)

References 41 publications

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“…ELM resulted in the estimated number of Mopt equal to 13. By analyzing all obtained values in different considered approaches, we can briefly conclude that in this case, a reliable Mopt value for generating a comparable result with the baseline might be in the range of [8,13], and a higher Mopt value (e.g., [22,33]) might lead to higher CD performance. The evaluation of computational efficiency in the two proposed supervised approaches, i.e., SVM-based and RaF-based, in comparison with HSCVA and S 2 CVA, the two reference methods, is provided as shown in .…”

Section: Number Of Selected Bands (M) Estimated Number Of Changes (K)mentioning

confidence: 98%

Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images

Tong

Samat

et al. 2017

Remote Sensing

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This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD performance by selecting the most informative band subset from the original high-dimensional data space. In particular, for cases where ground reference data are available or unavailable, either supervised or unsupervised CD approaches are designed. The following sub-problems in HSI-CD are investigated, including: (1) the estimated number of multi-class changes; (2) the binary CD; (3) the multiple CD; (4) the estimated optimal number of selected bands; and (5) computational efficiency. The main contribution of this paper is to provide for the first time a thorough analysis of the impacts of band selection on the HSI-CD problem, thus to fix the gap in the state-of-the-art techniques either by simply utilizing the full dimensionality of the data or exploring a complex hierarchical change analysis. It is applicable to CD problems in multispectral or PolSAR images when the feature space is expanded for discriminant feature extraction. Two real multi-temporal hyperspectral Hyperion datasets are used to validate the proposed approaches. Quantitative and qualitative experimental results demonstrated that by selecting a subset of the most informative and distinct spectral bands, the proposed approaches offered better CD performance than the state-of-the-art techniques using original full bands, without losing the change representative and discriminable capabilities of a detector.

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Section: Number Of Selected Bands (M) Estimated Number Of Changes (K)mentioning

confidence: 98%

Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images

Tong

Samat

et al. 2017

Remote Sensing

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“…Because of the lack of observed data on the changed objects and the subjective interference of supervised threshold determination, we used the expectation-maximization (EM) algorithm [22,32] to define thresholds in an unsupervised way. This method has been widely used for change threshold determination [11,28,33]. Thus, changed objects are extracted based on threshold segmentation of the CVA magnitude image.…”

Section: Extraction Of Changed Objectsmentioning

confidence: 99%

Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis

Chen

2016

Remote Sensing

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Change detection in multi-temporal remote sensing images has usually been treated as a problem of explicitly detecting land cover transitions. To date, multi-dimensional change vector analysis has been an effective solution to such problems. However, using change vector analysis makes it hard to calculate multiple directions or kinds of change. Through combining multi-feature object-based image analysis and change vector analysis, this paper presents a novel method for object-based change detection of multiple changes. Our technique, named self-adaptive weight-change vector analysis, carries out: (1) change vector analysis to determine magnitude and direction of changes; and (2) self-adaptive weight-based analysis of the standard deviation of image objects. Furthermore, a polar representation has been adopted to acquire visual change information for image objects. This paper proposes an automatic technique that can be applied to the field of multi-feature object-based change detection for very high resolution remotely sensed images. The two-step automatic detection strategy includes extraction of changed objects using an expectation-maximization algorithm to estimate the threshold under a Gaussian assumption, and identification of different kinds of changes using a K-means clustering algorithm. The effectiveness of our approach has been tested on both multispectral and panchromatic fusion images. Results of these two experimental cases confirm that this approach can detect multiple kinds of change. We found that self-adaptive weight-change vector analysis had superior capabilities of object-based change detection compared with standard change vector analysis, yielding Kappa statistics of 0.7976 and 0.7508 for Cases 1 and 2, respectively.

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“…Otherwise, when two thematic maps (e.g., LC/LU or habitats maps) independently produced at time T 1 and time T 2 are available, the well-known Post Classification Comparison (PCC) approach [10][11][12][13][14] can be used. The degree of success of this technique depends upon the reliability of the input thematic maps [15,16] since the quality of the output change image is related to the product of the accuracies of the two maps being compared.…”

Section: Change Detection Techniquesmentioning

confidence: 99%

Towards Operational Detection of Forest Ecosystem Changes in Protected Areas

et al. 2016

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This paper discusses the application of the Cross-Correlation Analysis (CCA) technique to multi-spatial resolution Earth Observation (EO) data for detecting and quantifying changes in forest ecosystems in two different protected areas, located in Southern Italy and Southern India. The input data for CCA investigation were elaborated from the forest layer extracted from an existing Land Cover/Land Use (LC/LU) map (time T 1 ) and a more recent (T 2 , with T 2 > T 1 ) single date image. The latter consist of a High Resolution (HR) Landsat 8 OLI image and a Very High Resolution (VHR) Worldview-2 image, which were analysed separately. For the Italian site, the forest layer (1:5000) was first compared to the HR Landsat 8 OLI image and then to the VHR Worldview-2 image. For the Indian site, the forest layer (1:50,000) was compared to the Landsat 8 OLI image then the changes were interpreted using Worldview-2. The changes detected through CCA, at HR only, were compared against those detected by applying a traditional NDVI image differencing technique of two Landsat scenes at T 1 and T 2. The accuracy assessment, concerning the change maps of the multi-spatial resolution outputs, was based on stratified random sampling. The CCA technique allowed an increase in the value of the overall accuracy: from 52% to 68% for the Italian site and from 63% to 82% for the Indian site. In addition, a significant reduction of the error affecting the stratified changed area estimation for both sites was obtained. For the Italian site, the error reduction became significant at VHR (±2 ha) in respect to HR (±32 ha) even though both techniques had comparable overall accuracy (82%) and stratified changed area estimation. The findings obtained support the conclusions that CCA technique can be a useful tool to detect and quantify changes in forest areas due to both legal and illegal interventions, including relatively inaccessible sites (e.g., tropical forest) with costs remaining rather low. The data obtained through CCA intervention could not only support the commitments undertaken by the European Habitats Directive (92/43/EEC) and the Convention of Biological Diversity (CBD) but also satisfy UN Sustainable Development Goals (SDG).

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A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images

Cited by 276 publications

References 41 publications

Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images

Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images

Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis

Towards Operational Detection of Forest Ecosystem Changes in Protected Areas

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