Multi-class Change Research Articles

On Unsupervised Multiclass Change Detection Using Dual-Polarimetric SAR Data

Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the comprehensive use of dual-pol parameters and proposed a novel dual-pol-based change detection framework utilizing different dual-pol scatter-type indicators. To optimize the exploitation of dual-pol change information, we presented a two-step processing strategy that divides the multiclass change detection process into a binary detection step that identifies the presence of changes and the classification step that distinguishes the types of change. In the detection stage, each dual-pol parameter was considered as an independent information source. Assuming potential conflict between dual-pol parameters, a disjunctive combination of detection results from different dual-pol parameters was applied to obtain the final detection result. In the classification step, an unsupervised change classification strategy was proposed based on the change direction and magnitude of the dual-pol parameters within the change class. Experimental results exhibited significantly improved detectability across a wide change spectrum compared with previous dual-pol-based change detection approaches. They also demonstrated the possibility of distinguishing different semantic changes without in situ ground data.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

Position-Aware Graph-CNN Fusion Network: An Integrated Approach Combining Geospatial Information and Graph Attention Network for Multiclass Change Detection

Position-Aware Graph-CNN Fusion Network: An Integrated Approach Combining Geospatial Information and Graph Attention Network for Multiclass Change Detection

DC3DCD: Unsupervised learning for multiclass 3D point cloud change detection

In a constant evolving world, change detection is of prime importance to keep updated maps. To better sense areas with complex geometry (urban areas in particular), considering 3D data appears to be an interesting alternative to classical 2D images. In this context, 3D point clouds (PCs), whether obtained through LiDAR or photogrammetric techniques, provide valuable information. While recent studies showed the considerable benefit of using deep learning-based methods to detect and characterize changes into raw 3D PCs, these studies rely on large annotated training data to obtain accurate results. The collection of these annotations are tricky and time-consuming. The availability of unsupervised or weakly supervised approaches is then of prime interest. In this paper, we propose an unsupervised method, called DeepCluster 3D Change Detection (DC3DCD), to detect and categorize multiclass changes at point level. We classify our approach in the unsupervised family given the fact that we extract in a completely unsupervised way a number of clusters associated with potential changes. Let us precise that in the end of the process, the user has only to assign a label to each of these clusters to derive the final change map. Our method builds upon the DeepCluster approach, originally designed for image classification, to handle complex raw 3D PCs and perform change segmentation task. An assessment of the method on both simulated and real public dataset is provided. The proposed method allows to outperform fully-supervised traditional machine learning algorithm and to be competitive with fully-supervised deep learning networks applied on rasterization of 3D PCs with a mean of IoU over classes of change of 57.06% and 66.69% for the simulated and the real datasets, respectively. The code is available at https://github.com/idegelis/torch-points3d-dc3dcd.

A new change detection method for wetlands based on Bi-Temporal Semantic Reasoning UNet++ in Dongting Lake, China

The utility of semantic change detection in myriad change scenarios has garnered considerable attention in contemporary research; however, its applicability in monitoring alterations in wetland ecosystems remains incompletely elucidated. To surmount the constraints associated with binary change detection methodologies—chiefly their insufficiency in the extraction of bi-temporal attributes—we introduced the Bi-Temporal Semantic Reasoning UNet++ (Bi-SRUNet++) algorithm. This algorithm leverages the architectural strengths of UNet++ as its foundational network to precisely delineate features pertinent to multi-class change detection. As a preliminary step, the study focused on the Dongting Lake wetland in China and conducted an analysis of feature trends predicated upon the monthly Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), as derived from Landsat 8 data spanning 2021–2022. Subsequently, the optimal temporal phases for change detection were ascertained through differential analyses between NDWI and NDVI metrics. Implementing the Bi-SRUNet++ algorithm on a pair of Sentinel-2 images, captured during the optimal phases, yielded augmented change information. Comparative evaluations reveal that the Bi-SRUNet++ algorithm, conceptualized on the framework of Bi-Temporal Semantic Reasoning Network (Bi-SRNet), surpasses the performance indices of its counterpart Semantic Segmentation and Change Detection Late Fusion (SSCD-l). Furthermore, the incorporation of the UNet++ backbone network amplifies the algorithm's capacity for semantic feature extraction, thereby enhancing the efficacy of Bi-SRUNet++ in wetland change detection. The analysis divulges that the total altered area of Dongting Lake during the 2021–2022 period amounts to 1187.97 km2, comprising a water loss of 1186.16 km2, a 715.34 km2 transformation into vegetation, and a conversion of 469.96 km2 into mudflats. The codes and partial dataset in this paper are available at: https://github.com/vivianmiumiu/Bi-SRUNetplusplus-for-SCD.

SIGNet: A Siamese Graph Convolutional Network for Multi-Class Urban Change Detection

Detecting changes in urban areas presents many challenges, including complex features, fast-changing rates, and human-induced interference. At present, most of the research on change detection has focused on traditional binary change detection (BCD), which becomes increasingly unsuitable for the diverse urban change detection tasks as cities grow. Previous change detection networks often rely on convolutional operations, which struggle to capture global contextual information and underutilize category semantic information. In this paper, we propose SIGNet, a Siamese graph convolutional network, to solve the above problems and improve the accuracy of urban multi-class change detection (MCD) tasks. After maximizing the fusion of change differences at different scales using joint pyramidal upsampling (JPU), SIGNet uses a graph convolution-based graph reasoning (GR) method to construct static connections of urban features in space and a graph cross-attention method to couple the dynamic connections of different types of features during the change process. Experimental results show that SIGNet achieves state-of-the-art accuracy on different MCD datasets when capturing contextual relationships between different regions and semantic correlations between different categories. There are currently few pixel-level datasets in the MCD domain. We introduce a new well-labeled dataset, CNAM-CD, which is a large MCD dataset containing 2508 pairs of high-resolution images.

Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning

This study is concerned with urban change detection and categorization in point clouds. In such situations, objects are mainly characterized by their vertical axis, and the use of native 3D data such as 3D Point Clouds (PCs) is, in general, preferred to rasterized versions because of significant loss of information implied by any rasterization process. Yet, for obvious practical reasons, most existing studies only focus on 2D images for change detection purpose. In this paper, we propose a method capable of performing change detection directly within 3D data. Despite recent deep learning developments in remote sensing, to the best of our knowledge there is no such method to tackle multi-class change segmentation that directly processes raw 3D PCs. Thereby, based on advances in deep learning for change detection in 2D images and for analysis of 3D point clouds, we propose a deep Siamese KPConv network that deals with raw 3D PCs to perform change detection and categorization in a single step. Experimental results are conducted on synthetic and real data of various kinds (LiDAR, multi-sensors). Tests performed on simulated low density LiDAR and multi-sensor datasets show that our proposed method can obtain up to 80% of mean of IoU over classes of changes, leading to an improvement ranging from 10% to 30% over the state-of-the-art. A similar range of improvements is attainable on real data. Then, we show that pre-training Siamese KPConv on simulated PCs allows us to greatly reduce (more than 3,000×) the annotations required on real data. This is a highly significant result to deal with practical scenarios. Finally, an adaptation of Siamese KPConv is realized to deal with change classification at PC scale. Our network overtakes the current state-of-the-art deep learning method by 23% and 15% of mean of IoU when assessed on synthetic and public Change3D datasets, respectively. The code is available at the following link: https://github.com/IdeGelis/torch-points3d-SiameseKPConv.

A Small Sample-Based Multiclass Change Detection Method Using Change Vector Analysis With Adaptive Weight Gaussian Mixture Model

A Small Sample-Based Multiclass Change Detection Method Using Change Vector Analysis With Adaptive Weight Gaussian Mixture Model

A Hierarchical Decoder Architecture for Multilevel Fine-Grained Disaster Detection

As a cutting-edge challenge in the field of disaster evaluation, the detection of disasters in remote sensing images is crucial. However, most existing approaches to disaster detection simply solve the problem as a naive multi-class change detection, lacking accurate damage-level classification. In this paper, we propose a new approach to disaster detection called multi-level disaster detection (MLDD) that focuses on fine-grained damage-level classification. Our proposed approach tackles MLDD through hierarchical-correlation modeling and presents a universal disaster detection architecture. Specifically, we summarize two existing applicative methods, one-step training and pre-training, which are compatible with our proposed architecture. In addition, we propose two novel hierarchical approaches, namely the multi-task (MT) based and graph-encoding (GE) based approaches. The MT approach resolves MLDD through layer-wise learning in a progressive manner, building explicit multi-stage and implicit joint models to probe into the coarse-to-fine correlation for damage-level evaluation. The GE approach enhances hierarchical relationships by encoding multifold messaging directions and probabilities using a graph neural network. Furthermore, all four hierarchical paradigms can be embedded in our hierarchical MLDD architecture, which outperforms state-of-the-art methods on the xBD dataset, particularly in fine-grained damage-level classification. Overall, our proposed approach represents a significant improvement over existing disaster detection methods and has the potential to advance the field of disaster evaluation.

Binary Change Guided Hyperspectral Multiclass Change Detection.

Characterized by tremendous spectral information, hyperspectral image is able to detect subtle changes and discriminate various change classes for change detection. The recent research works dominated by hyperspectral binary change detection, however, cannot provide fine change classes information. And most methods incorporating spectral unmixing for hyperspectral multiclass change detection (HMCD), yet suffer from the neglection of temporal correlation and error accumulation. In this study, we proposed an unsupervised Binary Change Guided hyperspectral multiclass change detection Network (BCG-Net) for HMCD, which aims at boosting the multiclass change detection result and unmixing result with the mature binary change detection approaches. In BCG-Net, a novel partial-siamese united-unmixing module is designed for multi-temporal spectral unmixing, and a groundbreaking temporal correlation constraint directed by the pseudo-labels of binary change detection result is developed to guide the unmixing process from the perspective of change detection, encouraging the abundance of the unchanged pixels more coherent and that of the changed pixels more accurate. Moreover, an innovative binary change detection rule is put forward to deal with the problem that traditional rule is susceptible to numerical values. The iterative optimization of the spectral unmixing process and the change detection process is proposed to eliminate the accumulated errors and bias from unmixing result to change detection result. The experimental results demonstrate that our proposed BCG-Net could achieve comparative or even outstanding performance of multiclass change detection among the state-of-the-art approaches and gain better spectral unmixing results at the same time.

A review of multi-class change detection for satellite remote sensing imagery

ABSTRACT Change Detection (CD) provides a research basis for environmental monitoring, urban expansion and reconstruction as well as disaster assessment, by identifying the changes of ground objects in different time periods. Traditional CD focused on the Binary Change Detection (BCD), focusing solely on the change and no-change regions. Due to the dynamic progress of earth observation satellite techniques, the spatial resolution of remote sensing images continues to increase, Multi-class Change Detection (MCD) which can reflect more detailed land change has become a hot research direction in the field of CD. Although many scholars have reviewed change detection at present, most of the work still focuses on BCD. This paper focuses on the recent progress in MCD, which includes five major aspects: challenges, datasets, methods, applications and future research direction. Specifically, the background of MCD is first introduced. Then, the major difficulties and challenges in MCD are discussed and delineated. The benchmark datasets for MCD are described, and the available open datasets are listed. Moreover, MCD is further divided into three categories and the specific techniques are described, respectively. Subsequently, the common applications of MCD are described. Finally, the relevant literature in the main journals of remote sensing in the past five years are analyzed and the development and future research direction of MCD are discussed. This review will help researchers understand this field and provide a reference for the subsequent development of MCD. Our collections of MCD benchmark datasets are available at: https://zenodo.org/record/6809804#.YsfvxXZByUk

Multi-class change detection of remote sensing images based on class rebalancing

ABSTRACT Multi-class change detection can make various ground monitoring projects more efficient and convenient. With the development of deep learning, the multi-class change detection methods have introduced Deep Neural Network (DNN) to improve the accuracy and efficiency of traditional methods. The class imbalance in the image will affect the feature extraction effect of DNN. Existing deep learning methods rarely consider the impact of data on DNN. To solve this problem, this paper proposes a class rebalancing algorithm based on data distribution. The algorithm iteratively trains the SSL model, obtains the distribution of classes in the data, then expands the original dataset according to the distribution of classes, and finally trains the baseline SSL model using the expanded dataset. The trained semantic segmentation model is used to detect multi-class changes in two-phase images. This paper is the first time to introduce the image class balancing method in the multi-class change detection task, so a control experiment is designed to verify the effectiveness and superiority of this method for the unbalanced data. The mIoU of the class rebalancing algorithm in this paper reaches 0.4615, which indicates that the proposed method can effectively detect ground changes and accurately distinguish the types of ground changes.

Change Detection of Amazonian Alluvial Gold Mining Using Deep Learning and Sentinel-2 Imagery

Monitoring changes within the land surface and open water bodies is critical for natural resource management, conservation, and environmental policy. While the use of satellite imagery for these purposes is common, fine-scale change detection can be a technical challenge. Difficulties arise from variable atmospheric conditions and the problem of assigning pixels to individual objects. We examined the degree to which two machine learning approaches can better characterize change detection in the context of a current conservation challenge, artisanal small-scale gold mining (ASGM). We obtained Sentinel-2 imagery and consulted with domain experts to construct an open-source labeled land-cover change dataset. The focus of this dataset is the Madre de Dios (MDD) region in Peru, a hotspot of ASGM activity. We also generated datasets of active ASGM areas in other countries (Venezuela, Indonesia, and Myanmar) for out-of-sample testing. With these labeled data, we utilized a supervised (E-ReCNN) and semi-supervised (SVM-STV) approach to study binary and multi-class change within mining ponds in the MDD region. Additionally, we tested how the inclusion of multiple channels, histogram matching, and La*b* color metrics improved the performance of the models and reduced the influence of atmospheric effects. Empirical results show that the supervised E-ReCNN method on 6-Channel histogram-matched images generated the most accurate detection of change not only in the focal region (Kappa: 0.92 (± 0.04), Jaccard: 0.88 (± 0.07), F1: 0.88 (± 0.05)) but also in the out-of-sample prediction regions (Kappa: 0.90 (± 0.03), Jaccard: 0.84 (± 0.04), and F1: 0.77 (± 0.04)). While semi-supervised methods did not perform as accurately on 6- or 10-channel imagery, histogram matching and the inclusion of La*b* metrics generated accurate results with low memory and resource costs. These results show that E-ReCNN is capable of accurately detecting specific and object-oriented environmental changes related to ASGM. E-ReCNN is scalable to areas outside the focal area and is a method of change detection that can be extended to other forms of land-use modification.

Very high-resolution satellite image segmentation using variable-length multi-objective genetic clustering for multi-class change detection

The baseline approaches on satellite image segmentation problem suffer from issues like sensitivity towards initialization, local optima solutions, a predefined number of output clusters, single-objective optimization, etc. To solve these challenges, this study proposes a unique pixel-level Multi-Spectral (MS) very high resolution (VHR) image segmentation algorithm based on variable-length multi-objective genetic clustering. We propose a new approach to update solutions by retaining variable length property throughout the optimization process. The resulting clustering algorithm contains a set of near-Pareto-optimal solutions. A map that has a scale of less than 1/10000 is called a large-scale map. We propose a large-scale change detection technique as an application of the proposed image segmentation algorithm. Solving Land-use/Land-Cover (LULC) change detection problems in a congested area is a complex task. This study considers the dataset from Pleiades-HR 1B, and Landsat 5 TM sensors in the experimental study. An extensive quantitative and qualitative analysis is performed to validate the superior performance of the proposed method with different state-of-the-art techniques.

Y-Net: A Multiclass Change Detection Network for Bi-temporal Remote Sensing Images

ABSTRACT The multiclass change detection task aims to segment an area of land change and to identify the type of ground change exploiting bi-temporal remote-sensing images. In recent years, this technology has become an important means of analysing remote-sensing data and has shown great potential in accurately understanding changes on the earth’s surface. However, the existing methods are mainly for binary change detection tasks and cannot detect the specific information of the change category. Hence, in this paper, we propose an end-to-end densely connected network, entitled Y-Net, employing a Siamese structure for multiclass change detection. The proposed network uses a dual-stream DenseNet to extract bi-temporal change features during the encoding stage and introduces the attention fusion mechanism for the decoding stage to enhance the attention to changing features. Add the dice loss to reduce the impact of sample imbalance on multiclass change detection. The suggested network utilizes a deep supervision strategy for each decoding layer to achieve change graph reconstruction of different levels. Y-Net is evaluated on the challenging multiclass change detection data set SECOND, and the ablation study verified the effectiveness of the attention fusion module and dice loss for multiclass change detection, considering both visual interpretation and quantitative evaluation, confirming that the proposed method is superior to current state-of-the-art change detection methods.

Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images

Semantic change detection (SCD) extends the multiclass change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use (LCLU) categories before and after the observation intervals. This fine-grained semantic change information is very useful in many applications. Recent studies indicate that the SCD can be modeled through a triple-branch convolutional neural network (CNN), which contains two temporal branches and a change branch. However, in this architecture, the communications between the temporal branches and the change branch are insufficient. To overcome the limitations in existing methods, we propose a novel CNN architecture for the SCD, where the semantic temporal features are merged in a deep CD unit. Furthermore, we elaborate on this architecture to reason the bi-temporal semantic correlations. The resulting bi-temporal semantic reasoning network (Bi-SRNet) contains two types of semantic reasoning blocks to reason both single-temporal and cross-temporal semantic correlations, as well as a novel loss function to improve the semantic consistency of change detection results. Experimental results on a benchmark dataset show that the proposed architecture obtains significant accuracy improvements over the existing approaches, while the added designs in the Bi-SRNet further improve the segmentation of both semantic categories and the changed areas. The codes in this article are accessible at <uri>https://github.com/ggsDing/Bi-SRNet</uri>.

MMSRC: A Multidirection Multiscale Spectral–Spatial Residual Network for Hyperspectral Multiclass Change Detection

Recently, deep convolutional neural network (CNN) hyperspectral change detection methods have achieved significant improvement. However, most CNN hyperspectral change detection methods do not make full use of spectral-spatial feature information. In this paper, we propose a novel multi-direction and multi-scale spectral-spatial residual network for hyperspectral multi-class change detection. Specifically, multi-scale structure and a multi-direction mechanism are introduced to investigate feature variation of hyperspectral images and improve the accuracy of hyperspectral change detection. Experiments on multiple hyperspectral datasets show that the proposed method achieves improved performance in comparison with other advanced hyperspectral multi-class change detection methods.

Hyperspectral change detection based on modification of UNet neural networks

The Earth’s surface changes continuously due to several natural and humanmade factors. Efficient change detection (CD) is useful in monitoring and managing different situations. The recent rise in launched hyperspectral platforms provides a diversity of spectrum in addition to the spatial resolution required to meet recent civil applications requirements. Traditional multispectral CD algorithms hardly cope with the complex nature of hyperspectral images and their high dimensionality. To overcome these limitations, a CD deep convolutional neural network (CNN) semantic segmentation-based workflow was proposed. The proposed workflow is composed of four main stages, namely preprocessing, training, testing, and evaluation. Initially, preprocessing is performed to overcome hyperspectral image noise and the high dimensionality problem. Random oversampling (ROS), deep learning, and bagging ensemble were incorporated to handle imbalanced dataset. Also, we evaluated the generality and performance of the original UNet model and four variants of UNet, namely residual UNet, residual recurrent UNet, attention UNet, and attention residual recurrent UNet. Three hyperspectral CD datasets were employed in performance assessment for binary and multiclass change cases; all datasets suffer from class imbalance and small region of interest size. Recurrent residual UNet presented the best performance in both accuracy and inference time. Overall, the obtained results imply that deep CNN segmentation models can be utilized to implement efficient CD for hyperspectral imageries.

Change detection using deep learning approach with object-based image analysis

In their applications, both deep learning techniques and object-based image analysis (OBIA) have shown better performance separately than conventional methods on change detection tasks. However, efforts to investigate the effect of combining these two techniques for advancing change detection techniques are unexplored in current literature. This study proposes a novel change detection method implementing change feature extraction using convolutional neural networks under an OBIA framework. To demonstrate the effectiveness of our proposed method, we compare the proposed method against benchmark pixel-based counterparts on aerial images for the task of multi-class change detection. To thoroughly assess the performance of our proposed method, this study also for the first time compared three common feature fusion schemes for change detection architecture: concatenation, differencing, and Long Short-Term Memory (LSTM). The proposed method was also tested on simulated misregistered images to evaluate its robustness, a factor that plays an important role in compromising change detection accuracy but has not been investigated for supervised change detection methods in the literature. Finally, the proposed change detection method was also tested using very high resolution (VHR) satellite images for binary class change detection to map an impacted area caused by natural disaster and the result was evaluated using reference data from the Federal Emergency Management Agency (FEMA). With the experimental results from these two sets of experiments, we showed that (1) our proposed method achieved substantially higher accuracy and computational efficiency when compared to pixel-based methods, (2) three feature fusion schemes did not show a significant difference for overall accuracy, (3) our proposed method was robust in image misregistration in both testing and training data, (4) we demonstrate the potential impact of automation to decision making by deploying our method to map a large geographic area affected by a recent natural disaster.

A Novel Framework Based on Polarimetric Change Vectors for Unsupervised Multiclass Change Detection in Dual-Pol Intensity SAR Images

Change detection (CD) is a crucial topic in many remote sensing applications. In the recent years, satellite polarimetric synthetic aperture radar (PolSAR) systems (e.g., the Sentinel-1 constellation) became a suitable tool for multitemporal monitoring due to the regular acquisitions with a short revisit time in different polarimetric channels. Methods for CD in PolSAR data mainly focus on binary CD (i.e., they provide information about the presence/absence of change only), whereas the polarimetric enhanced information provides multiple features that can be exploited for performing multiclass CD. In this article, we introduce a novel framework for the characterization of multitemporal changes in dual-polarimetric data. The framework is based on the definition of polarimetric change vectors (PCVs) and their representation in a polar coordinate system. PCVs allow characterizing and, thus, to separate multiclass changes in terms of target properties of the single-time scenes and the scattering theory. The proposed model is used to: 1) derive the statistical behaviors of change and no change classes in PolSAR multitemporal images; 2) design an automatic and unsupervised strategy to estimate the optimal number of changes; and 3) distinguish no change from change classes and the kinds of change from each other. An experimental analysis has been conducted on three multitemporal PolSAR data sets having different complexities in terms of number and kinds of change classes. The results confirm the effectiveness of the proposed approach and the better performance with respect to both specific techniques for CD in dual-pol SAR data and a general multiclass CD method, not designed for PolSAR data.