Change Detection Method Research Articles

Multi-class remote sensing change detection based on model fusion

ABSTRACT Change detection in remote sensing images has an important impact in various application fields. In recent years, great progress has been made in the change detection methods of multiple types of ground objects, but there are still limited recognition capabilities of the extracted features, resulting in unclear boundaries, and the accuracy rate needs to be improved. To address these issues, we use a high-resolution network (HRNet) to generate high-resolution representations and add new data augmentation methods to improve its accuracy. Secondly, we introduce the model of Transformer structure —— CSWin and HRNet to fuse to improve the performance and effect of the model. In order to enhance the model’s ability to perceive ground objects at different scales, a feature fusion network suitable for multi-class semantic segmentation is designed, named A-FPN. This feature fusion network is introduced between the CSWin backbone network and the semantic segmentation network. The experimental results show that the fusion method greatly improves the accuracy to 89.31% on the SECOND dataset, significantly reduces false detections, and recognizes the edges of objects more clearly. And achieved good results in the three evaluation indicators of precision, recall, and F1-score.

Change detection using multi-scale convolutional feature maps of bi-temporal satellite high-resolution images

ABSTRACT Change detection in high-resolution satellite images is essential to understanding the land surface (e.g. agriculture and urban change) or maritime surface (e.g. oil spilling). Many deep-learning-based change detection methods have been proposed to enhance the performance of the classical techniques. However, the massive amount of satellite images and missing ground-truth images are still challenging concerns. In this paper, we propose a supervised deep network for change detection in bi-temporal remote sensing images. We feed multi-level features from convolutional networks of two images (feature-extraction) into one architecture (feature-difference) to have better shape and texture properties using a dual attention module We also utilize a multi-scale dice coefficient error function to decrease overlapping between changed and background pixel. The network is applied to public datasets (ACD, SYSU-CD and OSCD). We compare the proposed architecture with various attention modules and loss functions to verfiy the performance of the proposed method. We also compare the proposed method with the stateof-the-art methods in terms of three metrics: precision, recall and F1-score. The experimental outcomes confirm that the proposed method has good performance compared to benchmark methods.

TSCNet: Topological Structure Coupling Network for Change Detection of Heterogeneous Remote Sensing Images

With the development of deep learning, convolutional neural networks (CNNs) have been successfully applied in the field of change detection in heterogeneous remote sensing (RS) images and achieved remarkable results. However, most of the existing methods of heterogeneous RS image change detection only extract deep features to realize the whole image transformation and ignore the description of the topological structure composed of the image texture, edge, and direction information. The occurrence of change often means that the topological structure of the ground object has changed. As a result, these algorithms severely limit the performance of change detection. To solve these problems, this paper proposes a new topology-coupling-based heterogeneous RS image change detection network (TSCNet). TSCNet transforms the feature space of heterogeneous images using an encoder–decoder structure and introduces wavelet transform, channel, and spatial attention mechanisms. The wavelet transform can obtain the details of each direction of the image and effectively capture the image’s texture features. Unnecessary features are suppressed by allocating more weight to areas of interest via channels and spatial attention mechanisms. As a result of the organic combination of a wavelet, channel attention mechanism, and spatial attention mechanism, the network can focus on the texture information of interest while suppressing the difference of images from different domains. On this basis, a bitemporal heterogeneous RS image change detection method based on the TSCNet framework is proposed. The experimental results on three public heterogeneous RS image change detection datasets demonstrate that the proposed change detection framework achieves significant improvements over the state-of-the-art methods.

Dynamic Detection of Forest Change in Hunan Province Based on Sentinel-2 Images and Deep Learning

Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas. First, the performance of various deep learning models (U-Net++, U-Net, LinkNet, DeepLabV3+, and STANet) and various loss functions (CrossEntropyLoss(CELoss), DiceLoss, FocalLoss, and their combinations) are compared on a self-made dataset. Next, the best model and loss function is used to predict the annual forest change in Hunan Province from 2017 to 2021, and the detection results are evaluated in 12 sample areas. Finally, forest changes are detected in Sentinel-2 images for each quarter of 2017–2021. In addition, a dynamic detection map of forest change in Hunan Province from 2017 to 2021 is drawn. The results reveal that the U-Net++ model and the CELoss performed the best on the self-made dataset, with a Precision of 0.795, a Recall of 0.748, and an F1-score of 0.771. The results of annual and quarterly forest change detection were consistent with the changes in the Sentinel-2 images with accurate boundaries. This result demonstrates the high practicality and generalizability of the method used in this paper. This paper achieves a rapid and accurate extraction of multi-temporal Sentinel-2 image forest change areas based on the U-Net++ model, which can be used as a benchmark for future large territorial areas monitoring and management of forest resources.

Deep collaborative learning with class-rebalancing for semi-supervised change detection in SAR images

Deep learning reveals excellent potential for accomplishing change detection in SAR imagery. Yet, it suffers from the problem of requiring large amounts of labeled samples, whilst labeling SAR imagery for change detection requires experts to label individual images at the pixel level, which is extremely tedious and time-consuming. Also, sample imbalance continues to present a serious challenge for the existing change detection techniques. To tackle these problems, in this study, a Deep Collaborative semi-supervised learning Framework with Class-Rebalancing (DCF-CRe) is proposed for SAR imagery change detection, by exploiting Convolutional Neural Network (CNN) and deep clustering. In particular, a Siamese Difference Fusion Network (SDFNet) is devised to implement change detection while effectively reducing the information loss due to the generation of difference images and highlighting features of the changed regions.In so doing, only a tiny batch of labeled samples is utilized to train SDFNet in order to obtain predicted change map and deep features. In addition, the Approximate Rank-Order Clustering (AROC) algorithm is employed to cluster the deep features, generating pseudo-labels for abundant unlabeled samples. DCF-CRe is then applied to select appropriate pseudo-labels and to add labeled samples to train SDFNet. Experimental results evaluated on six challenging datasets show that this proposed approach can achieve performance superior to state-of-the-art change detection methods for SAR imagery.

Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data

Earth observation technology has improved the detection of land cover changes. However, current pixel-based change detection methods cannot adequately describe the evolutionary process and spatiotemporal association of geographic entities. Therefore, we developed a method for analyzing the processes and patterns of land cover evolution based on spatiotemporal graphs. First, a spatiotemporal graph was generated from a time series of land cover maps according to the spatial and temporal relationships between land cover objects, as defined by spatial adjacency and temporal transition, respectively. Subsequently, structural characteristics, such as the spatial roles, adjacency type, temporal transitions and evolution trajectories, were derived from the spatiotemporal graph to describe and analyze the evolution of land cover. Finally, this method was applied to analyze land cover evolution in Fujian Province, China, from 2001 to 2019. The proposed method not only completely preserves the spatial adjacency and temporal transition details among land cover objects in a spatiotemporally unified graph framework but also extracts evolution-related spatiotemporal structural characteristics. This study provides a reliable scientific basis for analyzing the consistency of long-term land cover dynamics and has practical value for other geographic applications.

Detecting Urban Floods with Small and Large Scale Analysis of ALOS-2/PALSAR-2 Data

When a large-scale flood disaster occurs, it is important to identify the flood areas in a short time in order to effectively support the affected areas afterwards. Synthetic Aperture Radar (SAR) is promising for flood detection. A number of change detection methods have been proposed to detect flooded areas with pre- and post-event SAR data. However, it remains difficult to detect flooded areas in built-up areas due to the complicated scattering of microwaves. To solve this issue, in this paper we propose the idea of analyzing the local changes in pre- and post-event SAR data as well as the larger-scale changes, which may improve accuracy for detecting floods in built-up areas. Therefore, we aimed at evaluating the effectiveness of multi-scale SAR analysis for flood detection in built-up areas using ALOS-2/PALSAR-2 data. First, several features were determined by calculating standard deviation images, difference images, and correlation coefficient images with several sizes of kernels. Then, segmentation on both small and large scales was applied to the correlation coefficient image and calculated explanatory variables with the features at each segment. Finally, machine learning models were tested for their flood detection performance in built-up areas by comparing a small-scale approach and multi-scale approach. Ten-fold cross-validation was used to validate the model, showing that highest accuracy was offered by the AdaBoost model, which improved the F1 Score from 0.89 in the small-scale analysis to 0.98 in the multi-scale analysis. The main contribution of this manuscript is that, from our results, it can be inferred that multi-scale analysis shows better performance in the quantitative detection of floods in built-up areas.

Diabetic Foot Assessment using Skin Impedance in a Custom Made Sensor-sock.

Diabetic peripheral neuropathy (DPN) may lead to several changes in the skin, and some of these may influence the skin impedance spectrum. In the present study we have developed a prototype solution for skin impedance spectroscopy at selected skin sites (big toe pulp, heel and toe ball) that was tested in a pilot study on five patients with DPN and five healthy controls. At the big toe, most of the controls had markedly lower impedance than the DPN group, especially in the range of 1-100 kHz. The separation between the groups seems to be weaker at the heel and weakest at the toeball. The results may indicate that monitoring of the skin impedance spectrum may be a method for detection of skin changes associated with DPN, encouraging further studies with the big toe sensor in particular.

STCD: efficient Siamese transformers-based change detection method for remote sensing images

ABSTRACT Remote sensing Change Detection (CD) involves identifying changing regions of interest in bi-temporal remote sensing images. CD technology has rapidly developed in recent years through the powerful learning ability of Convolutional Neural Networks (CNN), affording complex feature extraction. However, the local receptive fields in the CNN limit modeling long-range contextual relationships in semantic changes. Therefore, this work explores the great potential of Siamese Transformers in CD tasks and proposes a general CD model entitled STCD that relies on Swin Transformers. In the encoding process, pure Transformers without CNN are used to model the long-range context of semantic tokens, reducing computational overhead and improving model efficiency compared to current methods. During the decoding process, the 3D convolution block obtains the changing features in the time series and generates the predicted change map in the deconvolution layer with axial attention. Extensive experiments on three binary CD datasets and one semantic CD dataset demonstrate that the proposed STCD model outperforms several popular benchmark methods considering performance and the required parameters. Among the STCD variants, the F1-Score of the Base-STCD on the three binary CD datasets LEVIR, DSIFN, and SVCD reached 89.85%, 54.72%, and 93.75%, respectively, and the mF1-Score and mIoU on the semantic CD dataset SECOND were 75.60% and 66.19%.

Mapping abandoned cropland using Within-Year Sentinel-2 time series

Against the background of the COVID-19 pandemic and various armed conflicts, the world is experiencing an unprecedented food crisis. The reclamation of abandoned cropland with food production potential may increase the global food supply in a short period of time, ensuring food security. At present, the extraction of abandoned cropland is mainly based on low- and medium-resolution remote sensing image data, making it difficult to extract fragmented areas in mountainous regions and to distinguish between abandoned cropland and transitional classes (such as fallow cropland). We developed a change-detection method based on within-year Sentinel-2 time series to extract cropland abandoned from 2018 to 2021 and defined four types of croplands, namely spontaneously abandoned, induced abandoned, fallow, and lost cropland, using Linxia County in mountainous China as the study region. First, cropland objects were generated from multi-temporal Sentinel-2 images using the multi-resolution segmentation method, and the land use map of Linxia County from 2017 to 2021 was drawn using random forest classifier. Second, through defining and identifying different cropland types, the interannual dynamic changes in cropland from 2018 to 2021 were extracted by analyzing the annual land use change trajectory. Third, by analyzing the normalized difference vegetation index (NDVI) time series of cropland within-year, the active and cultivated cropland sites within-year were extracted by threshold segmentation. Finally, the changes in the four cropland types were extracted by intersecting the two result types. Our method captured the object level changes well (overall mapping accuracy = 93 ± 5 %), and the extraction accuracy of abandoned cropland reached 81 ± 2 %. Abandoned cropland was mostly located in areas of medium quality and with a moderate distance from rural settlements. Reclamation can potentially increase the grain production in Linxia County by at least 3.6 % and needs to be combined with the local natural geography and human activities. Our method is a robust method for extracting abandoned cropland and may be applied to other research related to land use change.

Assessment of continuity and efficiency of complemented cropland use in China for the past 20 years: A perspective of cropland abandonment

The process of urbanization and infrastructure construction has resulted in the occupation and loss of high-quality cropland. Complemented cropland with higher altitudes and slopes from either development or reclamation has become an important component of cropland resources and plays a vital role in ensuring food security. However, existing research mainly focuses on the dynamic balance of quantity, quality, productivity, and impact on the environment, and not enough attention is paid to whether the newly added cropland is efficiently used in the long term. Based on a cropping intensity dataset from 2001–2019, this study applied the sliding window and change detection methods to assess the use continuity and efficiency of complemented cropland from the perspective of cropland abandonment and explored the impact of the abandonment on food production. The results showed the following: (1) The total area of complemented cropland in 2002–2019 reached 279,416.4 km2, accounting for 13.1% of total cropland in the base year (2001). (2) 45.1% of the complemented cropland was abandoned at least once during the past 20 years, making only 65.6% of that land efficiently used, and this percentage was higher in the middle plains and downstream of the Yangtze River. (3) Abandonment had significant impact on the food production of complemented cropland—with a trend of increasing and then decreasing—leading to a food production reduction of 24.3% per year on average, which was more severe in the middle plains and downstream of the Yangtze River. Higher slope and a migrated population promoted the abandonment of complemented cropland. Therefore, the government should strengthen the monitoring of long-term utilization of complemented cropland and strategic reserves to maintain stable utilization.

FERA-Net: A Building Change Detection Method for High-Resolution Remote Sensing Imagery Based on Residual Attention and High-Frequency Features

Buildings can represent the process of urban development, and building change detection can support land use management and urban planning. However, existing building change detection models are unable to extract multi-scale building features effectively or fully utilize the local and global information of the feature maps, such as building edges. These defections affect the detection accuracy and may restrict further applications of the models. In this paper, we propose the feature-enhanced residual attention network (FERA-Net) to improve the performance of the ultrahigh-resolution remote sensing image change detection task. The FERA-Net is an end-to-end network with a U-shaped encoder–decoder structure. The Siamese network is used as the encoder with an attention-guided high-frequency feature extraction module (AGFM) extracting building features and enriching detail information, and the decoder applies a feature-enhanced skip connection module (FESCM) to aggregate the enhanced multi-level differential feature maps and gradually recover the change feature maps in this structure. The FERA-Net can generate predicted building change maps by the joint supervision of building change information and building edge information. The performance of the proposed model is tested on the WHU-CD dataset and the LEVIR-CD dataset. The experimental results show that our model outperforms the state-of-the-art models, with 93.51% precision and a 92.48% F1 score on the WHU-CD dataset, and 91.57% precision and an 89.58% F1 score on the LEVIR-CD dataset.

A Hybrid Algorithm for Urban LULC Change Detection for Building Smart-city by Using WorldView Images

Technological advancement in smart cities can have adverse effects on the environment. Timely monitoring of smart cities to preserve environmental sustainability is a thrust area of research. It can be done by using change detection with multi-temporal satellite data. The success of these methods solely depends on the calibre of the backend image segmentation and Land-use Land-cover classification technique. The limitation of using cutting-edge classification algorithms is the availability of a proper dataset and identification of the edge structure of different LULC classes. In contrast, a segmentation algorithm cannot detect LULC classes automatically. In this research, we eliminated these shortcomings by considering a hybrid approach. We proposed a multi-class Support Vector Machine (SVM) and ISODATA-embedded large-scale change detection method. This method can automatically segment, detect, and perform LULC change analysis. We have considered the multi-sensor dataset of Barasat, West Bengal, India, obtained from the WorldView satellite sensor for the experimental study. The proposed method is validated concerning three different cutting-edge methods.

Multiresolution-Based Rough Fuzzy Possibilistic C-Means Clustering Method for Land Cover Change Detection

Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. First, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Second, objects at the coarsest scale are classified into changed, unchanged, and uncertain categories by the proposed MRFPCM. Third, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged, and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three datasets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach.

Landslide Mapping Using Multilevel-Feature-Enhancement Change Detection Network

Landslide mapping (LM) from bitemporal remote sensing images is essential for disaster prevention and mitigation. Bitemporal change detection technology has been applied for LM, however, there remains room for improvement in its accuracy and automation. In this paper, a multi-level feature enhancement network (MFENet) is proposed for LM based on modules built in convolutional neural network (CNN) like CNN-Attention. MFENet mainly consists of three modules: post-event feature enhancement module (PFEM), bi-feature difference enhancement module (BFDEM) and flow direction calibration module (FDCM). Specifically, the main role of PFEM selectively fuses post-event multi-layer features to provide discriminative post-event features. BFDEM fuses the multi-layer differences of both pre-event and post-event features to generate high-quality change detection features, sufficiently powerful to distinguish foreground from background. FDCM uses a digital elevation model (DEM) to calibrate the flow direction of each pixel of the landslide detection results to complete the LM task. Experiments were conducted to test the effectiveness of MFENet on two real-world regions, Lantau Island and Sharp Peak, Hong Kong, in which landslides occurred after rainstorms. Compared with other state-of-the-art general change detection methods and landslide-specific change detection methods, the proposed method outperforms on all metrics with its intersection over union (IoU) reaching 87.23%. The availability of additional features and the generalization performance of MFENet are demonstrated experimentally. It is anticipated that the proposed network will further contribute to disaster prevention and mitigation.

Unsupervised SAR Image Change Detection Based on Feature Fusion of Information Transfer

Synthetic aperture radar (SAR) image change detection is a hot but challenging task due to SAR images’ complex contents and inherent speckle noises. The expected change detection methods should reduce the influence of speckle noises, obtain the discriminative feature representations, and generate accurate change maps simultaneously. To these ends, we propose a new SAR image change detection method named feature fusion of information transfer network (FFITN). First, we develop a hybrid convolution block to depress the speckle noise impacts and explore the valuable information from SAR images. Thus, the feature extraction module (FEM) is constructed to obtain the multi-level features. Then, an information transfer module (ITM) is proposed to capture the salient regions from various aspects. Also, the salient knowledge is transferred among features at different levels to enhance their discrimination. Next, a self-attention-based feature fusion module (SAFFM) is introduced to fuse various features. Finally, a change map generation module (CMGM) with the clustering algorithm and specific loss functions is designed to produce the pseudo labels and change maps. Experimental results on three public SAR data sets demonstrate the model’s effectiveness. Our source codes are available at https://github.com/TangXu-Group/FFITN.

Detection of Detached Ice-fragments at Martian Polar Scarps Using a Convolutional Neural Network

Repeated high-resolution imaging has revealed current mass wasting in the form of ice block falls at steep scarps of Mars. However, both the accuracy and efficiency of ice-fragments’ detection are limited when using conventional computer vision methods. Existing deep learning methods suffer from the problem of shadow interference and indistinguishability between classes. To address these issues, we proposed a deep learning-driven change detection model that focuses on regions of interest. A convolutional neural network simultaneously analyzed bitemporal images, i.e., pre- and postdetach images. An augmented attention module was integrated in order to suppress irrelevant regions such as shadows while highlighting the detached ice-fragments. A combination of dice loss and focal loss was introduced to deal with the issue of imbalanced classes and hard, misclassified samples. Our method showed a true positive rate of 84.2% and a false discovery rate of 16.9%. Regarding the shape of the detections, the pixel-based evaluation showed a balanced accuracy of 85% and an F1 score of 73.2% for the detached ice-fragments. This last score reflected the difficulty in delineating the exact boundaries of some events both by a human and the machine. Compared with five state-of-the-art change detection methods, our method can achieve a higher F1 score and surpass other methods in excluding the interference of the changed shadows. Assessing the detections of the detached ice-fragments with the help of previously detected corresponding shadow changes demonstrated the capability and robustness of our proposed model. Furthermore, the good performance and quick processing speed of our developed model allow us to efficiently study large-scale areas, which is an important step in estimating the ongoing mass wasting and studying the evolution of the martian polar scarps.

Remote-Sensing-Based Change Detection Using Change Vector Analysis in Posterior Probability Space: A Context-Sensitive Bayesian Network Approach

Change vector analysis (CVA) and post-classification change detection (PCC) have been the most widely used change-detection methods. However, CVA requires sound radiometric correction to achieve optimal performance, and PCC is susceptible to accumulated classification errors. Although change vector analysis in the posterior probability space (CVAPS) was developed to resolve the limitations of PCC and CVA, the uncertainty of remote sensing imagery limits the performance of CVAPS owing to three major problems: 1) mixed pixels, 2) identical ground cover type with different spectra, and 3) different ground cover types with the same spectrum. To address this problem, this study proposes the FCM-CSBN-CVAPS approach under the CVAPS framework. The proposed approach decomposes the mixed pixels into multiple signal classes using the fuzzy C means (FCM) algorithm. Although the mixed pixel problem is less severe in the high-resolution image, the change detection performance is still enhanced because, as a soft clustering algorithm, FCM is less susceptible to cumulative clustering error. Then, a context-sensitive Bayesian network (CSBN) is constructed to establish multiple-to-multiple stochastic linkages between signal pairs and ground cover types by incorporating spatial information to resolve problems 2 and 3 discussed above. Finally, change detection is performed using CVAPS in the posterior probability space. The effectiveness of the proposed approach is evaluated on three bi-temporal remote sensing datasets with different spatial sizes and resolutions. The experimental results confirm the effectiveness of FCM-CSBN-CVAPS in addressing the uncertainty problems of change detection and its superiority over other relevant change-detection techniques.

Recognition of whole-landscape changes due to extreme rain events in a hyperarid desert

Infrequent extreme rain events in extraordinarily dry environments can dramatically shape the landscape. Here, a change-detection method is demonstrated that uses Landsat 8 OLI scenes to document land surface modification across thousands of square kilometers due to two extreme rain events which impacted the Atacama Desert. Images for 4 dates, in 3 date-pairs, are analyzed using a Change Vector Analysis (CVA) technique which delivers a measure of the intensity of change (Intensity-CVA). In 2015, an extreme rain event affected the southern Atacama and one in 2019 affected the northern Atacama. In this work, we show with the 2015 event the efficacy of CVA to identify areas of mass transference by favorable comparison to published Interferometric Synthetic Aperture Radar (InSAR) permanent coherence loss for areas located in nearly flat terrains. The efficacy of Intensity-CVA was also demonstrated for the 2019 rain event, where it identified mass transference and mineral transformations. To clarify the physical processes behind the Intensity-CVA response, for specific areas with known types of surface materials we conducted Principal Component Analysis (PCA) on quadratic difference of Surface Reflection (SR) values of Landsat 8 OLI scenes for multiple dates and also analyzed Intensity-CVA trends. These two analyses reveal that, among locations with strong Intensity-CVA change, remotely identified changes are dominated by soil moisture variations associated with active alluvial deposits, playa lakes and evaporitic units verified by PCA even 60 Km away from the focus of the rain. Secondarily we detected salt crystals growth several months after the rain. This approach, validated in the Atacama, successfully identified the consequences of extreme rain events at the scale of the entire landscape. This result opens the possibility to use a long-lived sensor, such as Landsat, to examine geomorphological surface changes through time in arid environments in a context of climate change.

Progressive Learning for Unsupervised Change Detection on Aerial Images

This article focuses on unsupervised methods for optical aerial image change detection. Existing unsupervised change detection techniques are mainly categorized as patch-based methods and transfer-learning-based methods. However, the first type ignores the spatial information in the images, and the second type may introduce new errors due to knowledge extracted from additional datasets. To effectively tackle these problems, we propose an unsupervised progressive learning framework (UPLF). We first use original estimated change maps as the labeled samples and choose the reliable regions from samples to train the network. We then propose a progressive learning method to expand the reliable labeled region. Briefly, we apply a label selection filter to filter out incorrect change information from the regions to help rectify incorrect labeling in the regions. This leads to a more reliable labeled region and thus, in turn, more accurate detection results. Compared with the patch-based and transfer-learning-based unsupervised techniques, our method takes the entire map as the training sample to avoid the problem associated with using small patches; moreover, our iterative and progressive methods further enhance the change detection performance without involving external knowledge. Indeed, based on our experimental results on the real datasets, the proposed method demonstrates highly competitive performance compared with the state-of-the-art.