Multimodal change detection (MCD) has important applications in disaster assessment, but the nonlinear distortion of features and spatial misalignment caused by sensor imaging differences make it difficult to obtain changes through direct comparison. To overcome the above problems, this study aims to realize MCD by capturing the modality-independent structural commonality features between Multimodal Remote Sensing Images (MRSIs). To achieve this, we devise a basic Graph Kolmogorov-Arnold Network (GKAN) to excavate spatial structural relationships and cross-modal nonlinear mappings simultaneously. Based on this, we propose a Dual-branch GKAN (DGKAN) for unsupervised MCD, which can capture spatial-spectral structural commonality features and compare them directly to detect changes. Concretely, the GKAN is used within the DGKAN to build two autoencoders consisting of a Siamese encoder and two independent decoders to learn spatial-spectral structural commonality features through feature reconstruction. Besides, we introduce a Covariance Structural Commonality Loss (CSCL), which guides the network in extracting spatial-spectral structural commonality features between MRSIs by unsupervised constraints on the distributional consistency of cross-modal features. Experiments on several MCD datasets show that the proposed DGKAN can achieve convincing results, and ablation studies verify the effectiveness of the GKAN and CSCL.

Due to the inherent inductive bias of operations, convolutional neural networks (CNN) cannot model global information of remote sensing (RS) images. In contrast, Transformer-based methods can establish long-range dependencies of images through self-attention (SA) mechanism, but it faces the challenges of computational complexity and memory requirements, but also ignores the exploration on the feature redundancy removal of RS images. To address these two issues, we propose a network based on dynamic sparse encoding and cross-temporal collaborative attention (DSECTCA-Net) for RS image change detection (CD). First, we implement dynamic sparse encoding (DSE) by designing hierarchical sparse Transformer module (HSTM), which decreases the correlation calculation of the SA mechanism and effectively reduces the computational complexity and parameter amount of Transformer. Secondly, we propose cross-temporal collaborative attention (CTCA) to model RS images in time series and fully explore the interactivity between dual-temporal RS images, so as to better extract the global understanding of visual scenes. Extensive experiments on two large-scale public RS datasets show that the proposed method not only provides higher detection accuracy, but also achieves lower computational complexity and required storage space than most popular CD networks.

Contrastive learning plays an important role in the semi-supervised medical image segmentation. However, existing contrastive learning methods struggle to capture the correlation of global and local features and improve feature discrimination for complex medical scenes, resulting in poor segmentation performance in challenging polyp segmentation. To overcome these limitations, we propose a semi-supervised polyp segmentation method using Hierarchical Granularity Contrastive Learning (HGCL). HGCL has two advantages. First, we design a hierarchical spatial contrastive learning module to divide the feature maps into large and small regions and perform different region-level contrastive learning, which can effectively capture the correlation of global and local information and improve the intra-class cohesion and inter-class separation. Second, we design a fine-granularity contrastive learning module, which can perform finer pixel-level contrastive learning to capture finer subtle local features and improve the generalization capacity of HGCL for complex medical scenes. Extensive experiments on three publicly available polyp datasets demonstrate that HGCL can achieve the better segmentation performance than existing popular semi-supervised methods. The code is available at https://github.com/Milk-White/HGCL.

Underwater images suffer from light absorption and scattering, impairs their visibility and applications. Existing underwater image restoration (UIR) methods based on generative models struggle are difficult to adapt to the complex and dynamic underwater environments characterized by illumination interference, low-light conditions, and non-uniform turbidity. To address these issues, we propose Water-CDM, a novel Adaptive Double-Branch Fusion Conditional Diffusion Model for underwater image restoration. Specifically, an adaptive double-branch fusion conditional diffusion model is presented utilizing a U-shaped full-attention network and Guided Multi-Scale Retinex with Brightness Correction (GMSRBC) to restore the challenging regions within underwater images. More precisely, to correct color casts and enhance the sharpness of underwater images, a U-shaped full-attention network incorporating Attention Blocks is designed for noise estimation during the reverse process of the conditional diffusion model. Concurrently, to mitigate overexposure during the enhancement of low-light underwater images under illumination interference, the GMSRBC method, featuring an Adaptive Brightness Correction Module, is proposed to efficiently adjust the brightness of underwater images. Experimental results demonstrate that the proposed Water-CDM significantly improves the quality of underwater images in challenging scenarios. Encouragingly, our proposed Water-CDM yields superior restoration outcomes compared to current state-of-the-art methods on three challenging publicly available datasets. Our codes will be released at: https://github.com/HKandWJJ/Water-CDM.

Road change detection (RCD) is crucial for intelligent transportation, disaster assessment, and urban planning. However, current general change detection (CD) methods focus on various targets, such as buildings, while less attention is paid to the CD of narrow and elongated roads. Compared with general CD, RCD may still be limited by the following two aspects: On the one hand, the road usually occupies a small proportion of pixels in remote sensing images (RSIs) and is often easily blocked by buildings, trees, etc., making it difficult to ensure the integrity and connectivity of road structural features in RCD. On the other hand, RCD may easily be confused with the semantic information of similar material backgrounds (such as parking lots and building roofs) due to the lack of salient road features. To overcome the above limitations, we propose a skeleton-guided large-kernel attention Transformer (SLAFormer) for RCD, which can focus on salient road structural and semantic features to enhance its performance. In the proposed SLAFormer, we construct a novel skeleton-guided large-kernel attention module (SLKAM) and a frequency-guided cross spatial-channel difference module (FSCDM) to achieve the above goals. The SLKAM is used to make the model focus on road-specific skeleton features, which preserve the overall structure and morphology of roads to enhance the continuity and integrity of road features. In addition, the FSCDM is devised to better capture small-scale road changes and reduce semantic confusion with similar backgrounds, thereby enhancing change regions and extracting highly discriminative road difference information. Extensive experiments on two public RCD datasets show that ours achieves better RCD accuracy compared with several state-of-the-art (SOTA) approaches. The code will be available at https://github.com/TongfeiLiu/SLAFormer-for-RCD.

Multimodal change detection (MCD) has garnered significant interest due to its capacity to address a variety of emergencies in a timely and effective manner. However, discrepancies in sensors and imaging techniques often hinder the direct comparison of heterogeneous remote sensing images (HRSIs), making it difficult to extract change information. To overcome this challenge, we propose a novel superpixel-based AutoEncoder Kolmogorov-Arnold Network (AEKAN) for unsupervised MCD. The primary objective of AEKAN is to excavate the latent commonality features between HRSIs. Notably, commonality features in unchanged regions are generally more pronounced than those in changed regions, which can be leveraged to assess change magnitude. To achieve this, the proposed method utilizes the Kolmogorov-Arnold Network (KAN), renowned for its capability to model data distributions, to extract these commonality features between HRSIs. Concretely, the proposed AEKAN consists of a Siamese KAN encoder and dual KAN decoders. The Siamese encoder aims to map HRSIs and extract latent commonality features, while the dual decoders reconstruct original bitemporal images from these features. In addition, we incorporate a hierarchical commonality loss function within the Siamese encoder to train AEKAN. This loss function is designed to intentionally guide the network in capturing commonality features by minimizing the discrepancies in features extracted from HRSIs at each layer of the Siamese encoder. The extracted commonality features are then adopted to quantify the change magnitude between images through mean square error (MSE). Extensive experiments on five MCD datasets demonstrate that the proposed AEKAN outperforms existing methods. The source code is available at:https://github.com/TongfeiLiu/AEKAN-for-MCD.

Multimodal change detection (MCD), as an emerging task, aims at recognizing change regions from bi-temporal remote sensing images (RSI) of different modalities. Inspired by the success of the self-attention mechanism in transformer, attempts have been made to solve MCD through the transformer variants. However, transformer-based network optimization requires high-quality training samples. In addition, due to the significant differences in the data distribution, semantic information, and feature representation of multimodal data, transformer-based methods have obvious deficiencies in local feature representation and spatial consistency, especially when dealing with heterogeneous images. To address the above challenges, we propose a collaborative frequency-aware transformer for MCD (CFAT-MCD). As an unsupervised framework, CFAT-MCD is capable of learning more fine-grained patterns of land cover change through a few pseudo-labels. The CFAT is designed to enhance spatial consistency and align the features on a multi-scale basis, which can effectively mitigate the effects of modal differences. In addition, we propose a window-based spatial-frequency collaborative representation (SFCR) module to introduce frequency information into the spatial domain and improve the discriminability of spatial features. Extensive experiments on public datasets and quantitative analyses have validated the superior detection performance of our approach and the effectiveness of each module.

The main challenge of multimodal change detection (MCD) is that multimodal bitemporal images (MBIs) cannot be compared directly to identify changes. To overcome this problem, this paper proposes a novel commonality feature representation learning (CFRL) and constructs a CFRL-based unsupervised MCD framework. The CFRL is composed of a Siamese-based encoder and two decoders. First, the Siamese-based encoder can map original MBIs in the same feature space for extracting the representative features of each modality. Then, the two decoders are used to reconstruct the original MBIs by regressing themselves, respectively. Meanwhile, we swap the decoders to reconstruct the pseudo-MBIs to conduct modality alignment. Subsequently, all reconstructed images are input to the Siamese-based encoder again to map them in a same feature space, by which representative features are obtained. On this basis, latent commonality features between MBIs can be extracted by minimizing the distance between these representative features. These latent commonality features are comparable and can be used to identify changes. Notably, the proposed CFRL can be performed simultaneously in two modalities corresponding to MBIs. Therefore, two change magnitude images (CMIs) can be generated simultaneously by measuring the difference between the commonality features of MBIs. Finally, a simple threshold algorithm or a clustering algorithm can be employed to divide CMIs into binary change maps. Extensive experiments on six publicly available MCD datasets show that the proposed CFRL-based framework can achieve superior performance compared with other state-of-the-art approaches.

Change detection (CD) methods have been crucial in exploring geo-environmental science. With the advancement of remote sensing (RS) technology, multimodal images acquired from different platforms and sensors are widely used for CD tasks. As an emerging task, multimodal CD (MCD) aims to achieve more comprehensive and precise detection of land cover changes through complementary information in multimodal images. However, there are significant differences between modalities, particularly in heterogeneous images. How to deal with modal differences while effectively integrating change information remains a challenge in MCD. In this article, we propose a novel adversarial feature equilibrium network (AFENet), which establishes an additional adversarial optimization to solve the equilibrium problem between modal differences and land cover changes. Our AFENet aligns the features and reduces the modal gap through a multiscale adversarial domain adaptation (MADA) approach. Meanwhile, a divergence-aware contrastive module (DCM) is designed as a regularization term for adversarial optimization. DCM affects the sensitivity of feature extractors by constraining the mutual information between changed and unchanged pixels. In this case, AFENet can maintain the consistency of feature representation while maximizing the discriminability of change targets. The features extracted from AFENet will then be integrated by our multistream feature fusion (MFF) module and utilized to generate change maps. The effectiveness of our approach is demonstrated on two scene-level multimodal RS datasets. Compared with existing methods, our AFENet achieves state-of-the-art (SOTA) performance on both datasets and outperforms the second-best$F1$score by 4.64% and 1.1%, respectively.

Change detection (CD) in multispectral remote sensing (RS) imagery suffers from low spectral resolution which can lead to degraded recognition of change information from land cover objects. Considering that natural hyperspectral imagery (HSI) is much higher in spectral resolution and more accessible, using it to enhance the spectral information of RS multispectral imagery for CD can improve performance. To achieve this, we propose a spectral knowledge transfer (SKT) framework to allow the creation of pseudo-hyperspectral RS images from the available RS multispectral ones without the need for the real pairs of RS multispectral and hyperspectral images, typically required by existing RS spectral enhancement methods. Specifically, an autoencoder is first trained based on the available pairs of natural HSI and its multispectral counterparts and then calibrated via the available RS multispectral images. The finally obtained decoder module is used to generate the pseudo-hyperspectral image from an input RS multispectral image. We further propose a multispectrum collaborative CD (MCCD) framework that leverages both the real multispectral images and the pseudo hyperspectral images generated from them in a collaborative way to achieve performance improvement. Extensive experiments on two large-scale RS CD datasets and eight existing deep learning-based CD methods demonstrate the stronger efficacy of the proposed method.

To meet the low latency demands of numerous Internet of Things (IoT) applications, edge computing (EC) has been proposed to migrate computation from the cloud to the network's edge. This paper investigates the resource allocation problem in edge computing with soft deadline tasks and designs an efficient online bargaining scheme for resource allocation at edge nodes. We introduce task value functions to model the sensitivities of different tasks to latency. Then, we model the resource allocation problem as an online bargaining problem base on a three-layer edge computing model. We propose a specific two-period bargaining scheme. Corresponding pricing strategies are formulated for different periods to achieve optimal resource allocation and maximization of average utility(AU). Experimental results demonstrate that our algorithm outperforms other bargaining strategies and effectively improves system response speed.

Self-supervised contrastive learning (CL) can learn high-quality feature representations that are beneficial to downstream tasks without labeled data. However, most CL methods are for image-level tasks. For the fine-grained change detection (FCD) tasks, such as change or change trend detection of some specific ground objects, it is usually necessary to perform pixel-level discriminative analysis. Therefore, feature representations learned by image-level CL may have limited effects on FCD. To address this problem, we propose a self-supervised global–local contrastive learning (GLCL) framework, which extends the instance discrimination task to the pixel level. GLCL follows the current mainstream CL paradigm and consists of four parts, including data augmentation to generate different views of the input, an encoder network for feature extraction, a global CL head, and a local CL head to perform image-level and pixel-level instance discrimination tasks, respectively. Through GLCL, features belonging to different perspectives of the same instance will be pulled closer, while features of different instances will be alienated, which can enhance the discriminativeness of feature representations from both global and local perspectives, thereby facilitating downstream FCD tasks. In addition, GLCL makes a targeted structural adaptation to FCD, i.e., the encoder network is undertaken by the common backbone networks of FCD, which can accelerate the deployment on downstream FCD tasks. Experimental results on several real datasets show that compared with other parameter initialization methods, the FCD models pretrained by GLCL can obtain better detection performance.

Although deep learning-based change detection (CD) methods achieve great success in remote sensing images, they still suffer from two main challenges. First, popular Convolutional Neural Networks (CNNs) are weak in extracting discriminated features focusing on changed regions, since most methods ignore the multi-frequency components of bi-temporal images. Second, although existing CD methods employ the Transformer structure to capture long-range dependency for global feature representation, it is difficult for them to simultaneously take into account the long-range dependency of changed objects at various scales. To address the above issues, we propose a triple change detection network (TCD-Net) via joint multi-frequency and full-scale Swin-Transformer. The proposed TCD-Net has two main advantages. First, we propose a multi-frequency channel attention (MFCA) module to boost the ability of modeling the channel correlation, which can compensate for the problem of insufficient feature representation caused by only performing global average pooling (GAP). Furthermore, a joint multi-frequency difference feature enhancement (JM-DFE) guiding block is proposed to improve the boundary quality and the position awareness of truly changed objects, which can effectively extract channel features of multi-frequency information and thus improve the discriminative ability of features. Second, unlike Siamese-based structures, we propose a full-scale Swin-Transformer (FST) module as the third branch to model and aggregate the long-range dependency of multi-scale changed objects, which can alleviate the missed detections of small objects and achieve more compact changed regions effectively. Experiments on three public CD datasets exhibit that the proposed TCD-Net achieves better CD accuracy with smaller model complexity than state-of-the-art methods. The code is publicly available at https://github.com/RSCD-mz/TCD-Net.

In the federated learning (FL) paradigm, edge devices use local datasets to participate in machine learning model training, and servers are responsible for aggregating and maintaining public models. FL cannot only solve the bandwidth limitation problem of centralized training, but also protect data privacy. However, it is difficult for heterogeneous edge devices to obtain optimal learning performance due to limited computing and communication resources. Specifically, in each round of the global aggregation process by the FL, clients in a ‘strong group’ have a greater chance to contribute their own local training results, while those clients in a ‘weak group’ have a low opportunity to participate, resulting in a negative impact on the final training result. In this paper, we consider a federated learning multi-client selection (FL-MCS) problem, which is an NP-hard problem. To find the optimal solution, we model the FL global aggregation process for clients participation as a potential game. In this game, each client will selfishly decide whether to participate in the FL global aggregation process based on its efforts and rewards. By the potential game, we prove that the competition among clients eventually reaches a stationary state, i.e. the Nash equilibrium point. We also design a distributed heuristic FL multi-client selection algorithm to achieve the maximum reward for the client in a finite number of iterations. Extensive numerical experiments prove the effectiveness of the algorithm.

Landslide inventory mapping (LIM) on the basis of change detection techniques has potential significance for landslide disaster analysis. In this letter, a novel LIM approach based on the adaptive histogram-mean distance (AHMD) is proposed, which adaptively considers spatial contextual information of different landslide regions to improve the detection performance. First, to adapt the shape, size, and distribution of various landslides, an adaptive region around a pixel is extracted by a novel adaptive region extension algorithm without parameter setting. Second, the pixels within the adaptive region are taken to construct the spectral frequency histograms, and then, the adaptive histogram mean (AHM) is developed as the feature of a histogram. Third, the AHMD is defined based on the bin-to-bin (B2B) distance to measure change magnitude between the pairwise AHMs. Finally, LIM can be obtained by a supervised threshold method called double-window flexible pace search (DFPS). Experimental results tested on two real datasets with a very high spatial resolution (VHR) demonstrate the outperformance of the proposed AHMD approach with seven comparative methods.

Landslide inventory mapping (LIM) plays an important role in landslide susceptibility analysis. Many LIM approaches based on change detection techniques have been proposed, but with various drawbacks. For example, existing approaches have limited capability to capture the objects of varying shapes/sizes present in an area impacted by landslide. Many existing approaches are supervised and require parameter tuning. Moreover, some methods are prone to salt-and-pepper noise. To overcome these limitations, in this letter, an algorithm based on automatic adaptive region extension using very-high-resolution remote sensing images is developed. First, a simple yet effective k-means clustering method is used to generate training samples for landslide and nonlandslide classes, which refer to changed and unchanged areas, respectively. Second, an automatic adaptive region extension algorithm is developed and applied to each pixel of the postevent image, and the label of an extended region around a pixel is determined by the nearest distance between the central pixel and the changed or unchanged samples. Finally, the labels of a pixel are recorded because a pixel in different adaptive regions may be reassigned dissimilar labels, and the final label of the pixel is consistent with its maximum assigned label. To verify the performance of the proposed approach, we conducted experiments on two different landslide sites with VHR remote sensing images in Lantau Island, Hong Kong, China. Experimental results clearly demonstrate that the proposed approach has several advantages in improving the performance of LIM with VHR remote sensing images.

Many land cover change detection (LCCD) approaches applied on very high resolution (VHR) remote sensing images utilize spatial information by using a regular window or strict mathematical model. However, regular shape or strict models cannot fit the various shapes and sizes of the ground targets. In this article, a novel LCCD approach without the parameter is proposed to detect land cover change with VHR remote sensing images. First, an adaptive spatial-context extraction algorithm is applied to explore contextual information around a pixel. Second, the change magnitude between pairwise pixels is quantitatively measured by computing the band-to-band distance which is defined by the pairwise adaptive regions around the corresponding pixels. Finally, after the generation of a change magnitude image (CMI), a binary threshold method called double-window flexible pace search (DFPS) is adopted to divide CMI into a binary change detection map. The performance of the proposed approach is verified by comparing it with five state-of-the-art methods with three pairs of VHR images. The comparisons demonstrated that the proposed approach achieved the improved detected results comparing with state-of-the-art LCCD methods. The code of the proposed approach is available athttps://github.com/TongfeiLiu/ASEA-CD.

Hyperspectral images (HSIs) contain rich spectral signatures that reveal more image details and, thus, enable the detection of less noticeable changes on the ground. However, HSI-based change detection (CD) is susceptible to a large amount of irrelevant or noisy spectral and spatial information due to massive spectral bands. To address these issues, we propose a novel spectral and spatial attention network (S2AN) for HSI-based CD, which is capable to suppress CD-irrelevant spectral and spatial information via adaptive spectral and spatial attention mechanisms. S2AN takes as input the image patch from the difference map between two HSIs and outputs the status of change for the patch. Specifically, S2AN is composed of several repeated attention blocks, each of which contains the spectral attention (SpeA) module for directly calculating the attention score for each input channel, the Gaussian spatial attention (GSpaA) module that first constructs an adaptive Gaussian distribution and then samples it to derive the attention scores for each spatial position, and the convolutional feature extraction (CFE) module for extracting features from the attention-weighted input. It is worth mentioning that, in addition to the advantage of the attention, GSpaA also reduces the sensitivity of patch size for patch-based methods. To effectively train S2AN when facing insufficient labeled data, a semisupervised strategy that combines supervised and unsupervised methods to augment labeled training data is proposed. Experiments on several HSI datasets in comparison to existing methods show the superiority of S2AN.

Building change detection (BCD) for very-high-spatial-resolution (VHR) remote sensing images is very important and challenging in the field of remote sensing, as the building is one of the most significant and valuable man-made ground targets. This article proposes a local–global pyramid network (LGPNet) that combines a local feature pyramid module (LFPM) and a global spatial pyramid module (GSPM) for various building feature extraction. The LFPM is constructed using the convolutional kernel with three different pyramid scales, and then, the local pyramid features are obtained by adding features of each scale. In the GSPM, the global spatial pyramid features are extracted by adaptive average pooling to acquire global contextual information from different fields of view on deep features. The LFPM and the GSPM work in a parallel and complementary manner to capture discriminative features of various buildings. In addition to the LFPM and the GSPM, the proposed LGPNet also employs two general attention mechanisms, i.e., the position attention module and the channel attention module, which can select and emphasize adaptively some building features with high semantic responses. Besides, in order to mitigate the influence of other ground targets to a certain extent, a cross-task transfer learning strategy is introduced to make the LGPNet focus on the building, which significantly improves the performance of our method. Extensive experiments on two public available BCD datasets show that the proposed LGPNet can achieve significant improvement compared with eight other state-of-the-art methods. The source code and the pretrained model will be released athttps://github.com/TongfeiLiu/LGPNet.

The majority of the change detection (CD) methods consider spatial information by using a regular window or strict mathematical model. Moreover, these methods use the spectra directly to measure the change magnitude between bitemporal images. To solve this problem, local histogram-based analysis (LHBA) is proposed for detecting a land cover change in this letter. This new approach aims to inhibit the pseudo change by defining the local histogram trend (LHT) in an adaptive manner instead of using spectral values to measure change magnitude directly. In the proposed approach, the spatial information around each pixel is first exploited by defining an adaptive local histogram. The LHT distance between the pairwise local histograms is then developed to measure the change magnitude between the pairwise pixels of bitemporal images. Finally, the change magnitude image is generated, and a binary CD is achieved by a threshold method. Experiments based on two pairs of very high-resolution remote sensing images, which refer to land use change and landslides events, demonstrate the advantages and performance of the proposed approach.

Very high-resolution (VHR) remote sensing images can geometrically depict ground targets in detail but are usually insufficient in the spectral domain. This characteristic leads to a considerable amount of noise and pseudo change in the produced binary change detection maps (BCDMs) when VHR remote sensing images are used for change detection. Here, to solve the aforementioned problem, an object-oriented key point vector distance (KPVD) is proposed to measure the change magnitude between bitemporal VHR images when land cover changes are detected. The proposed KPVD-based change detection approach comprises the following major steps. First, multiscale objects based on a postevent image are extracted by the fractional net evaluation segmentation approach, and then, the segments are taken as the unit for measuring the change magnitude between bitemporal images. Second, key points and the corresponding vector are defined to describe the object feature instead of using the total pixels within the object. Finally, KPVD is proposed to measure the change magnitude between the local areas referenced to the object in the bitemporal images. The change magnitude image (CMI) between the bitemporal images is generated while the entire images are scanned and processed object by object. A well-known automatic binary method, the Otsu approach, is employed in this article to divide CMI into a BCDM. Experimental results conducted on four real data sets demonstrate the feasibility and outperformance of the proposed KPVD-based change detection approach compared with five state-of-the-art methods in terms of visual performance and quantitative measurements.

Detecting land cover change through very-high-resolution (VHR) remote sensing images is helpful in supporting urban sustainable development, natural disaster evaluation, and environmental assessment. However, the intraclass spectral variance in VHR remote sensing images is usually larger than that of median-low remote sensing images. Furthermore, the bitemporal images are usually acquired under different atmospheric conditions, sun height, soil moisture, and other factors. Consequently, in practical applications, many pseudo changes are presented in the detected map. In this paper, an adaptive histogram trend (AHT) similarity approach is promoted to quantitatively measure the magnitude between the corresponding pixels in bitemporal images in terms of change semantic. In the proposed approach, to reduce the phenological effect on the bitemporal images of land cover change detection (LCCD), we first define the quantitative description of AHT. Second, the change magnitudes between pairwise pixels are quantitatively measured by an improved bin-to-bin (B2B) distance between the corresponding AHTs. Then, the change magnitudes between two entire bitemporal images are measured AHT-by-AHT. Finally, binary threshold methods, such as the Otsu method or the double-window flexible pace search (DFPS) method, are used to divide the change magnitude image into binary change detection maps and obtain the final change detection map. The performance of the AHT-based LCCD approach is verified by four pairs of VHR remote-sensing images that correspond to two types of real land cover change cases. The detected results based on the four pairs of bitemporal VHR images outperformed the compared state-of-the-art LCCD methods.