Image Vector Research Articles

Japanese historical character recognition by focusing on character parts

Japanese historical documents provide valuable information. Character recognition is a critical technology for the digitalization of historical documents. Sample imbalance is a significant obstacle in recognizing Japanese historical characters, kuzushiji. Thousands of kuzushiji only have less than a few samples. Thus, recognition performance deteriorates greatly in kuzushiji with a few samples. In this study, we propose a framework for transferring knowledge of character parts from font to kuzushiji. The pretraining learns character parts from synthesized font images. However, fine-tuning to kuzushiji is more complex. We propose calculating a mean squared error loss between feature vectors of kuzushiji and font images, resulting in consistent feature vectors in kuzushiji and font. Consequently, we can perform zero-shot recognition for kuzushiji using the font images of zero-sampled kuzushiji. The experimental results show that the proposed method recognized zero-sampled kuzushiji at approximately 48% accuracy. Consequently, we significantly expand the number of recognizable kuzushiji.

Visual Communication Design Features Based on Traditional Animal and Plant Patterns

Abstract This paper contributes to the visual communication design of traditional animal and plant patterns by recreating the traditional pattern graphics based on the specific skeletal laws of traditional patterns. Based on the demand for vectorization of traditional animal and plant tattoos, a vectorization algorithm based on edge structure extraction is proposed to pre-process the tattoos using the gradient minimization model, and the tattoos are processed by image vectorization using the edge structure extraction algorithm of clustered segmentation and the Bézier curve fitting algorithm of the least squares method. Based on the image vectorization rendering of traditional animal and plant patterns, the visual communication design is carried out, and the color matching effect is analyzed in terms of single-factor color matching and comprehensive color matching. 10 extracted colors are analyzed in terms of the hue angle difference on the color ring, and the two comparative colors are the contrasting colors when the angle difference is 120~180°. The contrast height is highest when the difference value is 180°.

Classification of Alzheimer’s Disease Based on White Matter Connectivity Network

Alzheimer’s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuroimaging differences between MCI and normal control (NC), the clinical diagnosis is subjective and easy to misdiagnose. Machine learning can extract depth features from neural images, and analyze and label them to assist the diagnosis of diseases. This paper combines diffusion tensor imaging (DTI) and support vector machine (SVM) to classify AD, MCI, and NC. First, the white matter connectivity network was constructed based on DTI. Second, the nodes with significant differences between groups were screened out by the two-sample t-test. Third, the optimal feature subset was selected as the classification feature by recursive feature elimination (RFE). Finally, the Gaussian kernel support vector machine was used for classification. The experiment tested and verified the data downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the area under the curve (AUC) of AD/MCI and MCI/NC are 0.94 and 0.95, respectively, which have certain competitive advantages compared with other methods.

Study on Remote Sensing Image Coverage Frequency Based on Spatial Grid

The coverage frequency analysis of remote sensing images is helpful to analyse the data acquisition capability of satellites or the vigorous users’ demand. In the current research, thermal values are mostly calculated using an individual point or surface target. In this paper, an index RSICF is proposed, which is calculated based on two shapes, image vectors, and spatial grid, to measure the frequency of remote sensing image coverage. The study uses GF-1 satellite image vectors to calculate RSICF around China to verify its availability. The result shows RSICF can reflect the distribution of historical image frequency respectively and can be introduced by creating a heatmap of remote sensing image coverage.

Machine learning methods on quantized vectors

Vector quantization is one of the important issues in digital images. There are many studies conducted on quantized vectors or images. On the other hand, machine-learning approaches are a popular issue today. In this study, the classification performances of machine learning approaches on reduced image vectors are examined. Firstly, Corel 1K data set were reduced to 64 colors with octree and histogram feature vectors extracted. Classification was carried out using various machine learning approaches on the relevant vectors. As a result of the classification, the success of the methods was examined.

Information extraction and knowledge linkage of geological profiles and related contextual texts from mineral exploration reports for geological knowledge graphs construction

Geological profiles serve as invaluable repositories of data for knowledge extraction and the identification of geological entities, constituting a fundamental resource in scientific investigation. By encapsulating the stratigraphic arrangement and rock characteristics, these profiles offer a visually compelling and efficacious representation of stratigraphy along the vertical axis. Within geological profiles, intricate semantic associations frequently emerge, connecting various objects such as strata, rocks, and minerals across two or more profiles. These relationships often unveil inherent correlations or evolutionary attributes among geological entities. Understanding the general semantic relations between geological objects by manually scrutinizing vast quantities of profiles proves arduous, if not impracticable. In response, this study proposes an innovative framework capable of automated comprehension of extensive geological profiles and their associated contextual textual information. Built upon a natural language processing model and employing image vectorization techniques, this framework enables extraction of semantic information encompassing geological profiles, semantic relationships, and properties. In this article, we propose the comprehensive architecture of the framework, delineating its individual modules and subsequently applying it within a case study. Moreover, we conduct exploratory visualizations utilizing a knowledge graph to depict the extracted semantic information concerning diverse geological objects. The proposed framework presents a promising approach for supporting large-scale content analysis in the realm of stratigraphic research, facilitating advancements in our understanding of the evolution of stratigraphy.

Research on intelligent clearing of weeds in wheat fields using spectral imaging and machine learning

The intelligent management of weed clearing can improve wheat yield and reduce the use of pesticides. In this paper, our contribution is to propose a method for wheat and weed recognition based on multispectral imaging and Membrane Search Algorithm Support Vector Machine (MSA-SVM), which is the basis for intelligent weed clearing. A spectral data acquisition system is set up in the laboratory. Then, a total of 700 groups of wheat and weed spectral data are collected. The classical SVM based on spectral data is applied to identify weeds to study the classification performance of wheat and weeds in different bands. The Membrane Search Algorithm (MSA) algorithm proposed by us was used for SVM parameters optimization, and the dimension reduction algorithm was adopted to reduce the interference of redundant information. After local linear embedding (LLE) dimensionality reduction of spectral data, MSA-SVM has the highest average recognition accuracy for weeds and wheat, which is 91.00%. The experimental results show that our research can distinguish wheat from weeds, which is of great significance for the development of smart agriculture.

FontFusionGAN: Refinement of Handwritten Fonts by Font Fusion

Handwritten fonts possess unique expressive qualities; however, their clarity often suffers because of inconsistent handwriting. This study introduces FontFusionGAN (FFGAN), a novel method that enhances handwritten fonts by mixing them with printed fonts. The proposed approach leverages a generative adversarial network (GAN) to synthesize fonts that combine the desirable features of both handwritten and printed font styles. Training a GAN on a comprehensive dataset of handwritten and printed fonts enables it to produce legible and visually appealing font samples. The methodology was applied to a dataset of handwriting fonts, showing substantial enhancements in the legibility of the original fonts, while retaining their unique aesthetic essence. Unlike the original GAN setting where a single noise vector is used to generate a sample image, we randomly selected two noise vectors, z1 and z2, from a Gaussian distribution to train the generator. Simultaneously, we input a real image into the fusion encoder for exact reconstruction. This technique ensured the learning of style mixing during training. During inference, we provided the encoder with two font images, one handwritten and the other printed font, to obtain their respective latent vectors. Subsequently, the latent vector of the handwritten font image was injected into the first five layers of the generator, whereas the latent vector of the printed font image was injected into the last two layers to obtain a refined handwritten font image. The proposed method has the potential to improve the readability of handwritten fonts, offering benefits across diverse applications, such as document composition, letter writing, and assisting individuals with reading and writing difficulties.

Image compression based on vector quantization and optimized code-book design using Genetic Mating Influenced Slime Mould (GMISM) algorithm

Large amounts of storage are required to store the recent massive influx of fresh photographs that are uploaded to the internet. Many analysts created expert image compression techniques during the preceding decades to increase compression rates and visual quality. In this research work, a unique image compression technique is established for Vector Quantization (VQ) with the K-means Linde–Buzo–Gary (KLBG) model. As a contribution, the codebooks are optimized with the aid of hybrid optimization algorithm. The projected KLBG model included three major phases: an encoder for image compression, a channel for transitions of the compressed image, and a decoder for image reconstruction. In the encoder section, the image vector creation, optimal codebook generation, and indexing mechanism are carried out. The input image enters the encoder stage, wherein it’s split into immediate and non-overlapping blocks. The proposed GMISM model hybridizes the concepts of the Genetic Algorithm (GA) and Slime Mould Optimization (SMO), respectively. Once, the optimal codebook is generated successfully, the indexing of the every vector with index number from index table takes place. These index numbers are sent through the channel to the receiver. The index table, optimal codebook and reconstructed picture are all included in the decoder portion. The received index table decodes the received indexed numbers. The optimally produced codebook at the receiver is identical to the codebook at the transmitter. The matching code words are allocated to the received index numbers, and the code words are organized so that the reconstructed picture is the same size as the input image. Eventually, a comparative assessment is performed to evaluate the proposed model. Especially, the computation time of the proposed model is 69.11%, 27.64%, 62.07%, 87.67%, 35.73%, 62.35%, and 14.11% better than the extant CSA, BFU-ROA, PSO, ROA, LA, SMO, and GA algorithms, respectively.

Radiolabeled NGR-Based Heterodimers for Angiogenesis Imaging: A Review of Preclinical Studies.

Since angiogenesis/neoangiogenesis has a major role in tumor development, progression and metastatic spread, the establishment of angiogenesis-targeting imaging and therapeutic vectors is of utmost significance. Aminopeptidase N (APN/CD13) is a pivotal biomarker of angiogenic processes abundantly expressed on the cell surface of active vascular endothelial and various neoplastic cells, constituting a valuable target for cancer diagnostics and therapy. Since the asparagine-glycine-arginine (NGR) sequence has been shown to colocalize with APN/CD13, the research interest in NGR-peptide-mediated vascular targeting is steadily growing. Earlier preclinical experiments have already demonstrated the imaging and therapeutic feasibility of NGR-based probes labeled with different positron emission tomography (PET) and single-photon emission computed tomography (SPECT) radionuclides, including Gallium-68 (68Ga), Copper-64 (64Cu), Technetium-99m (99mTc), Lutetium-177 (177Lu), Rhenium-188 (188Re) or Bismuth-213 (213Bi). To improve the tumor binding affinity and the retention time of single-receptor targeting peptides, NGR motifs containing heterodimers have been introduced to identify multi-receptor overexpressing malignancies. Preclinical studies with various tumor-bearing experimental animals provide useful tools for the investigation of the in vivo imaging behavior of NGR-based heterobivalent ligands. Herein, we review the reported preclinical achievements on NGR heterodimers that could be highly relevant for the development of further target-specific multivalent compounds in diagnostic and therapeutic settings.

Generating embedding spaces for re-identifying pallets from their chipwood patterns

Modern logistics rely on continuous tracking of even most basic entities like pallets. While tracking is nowadays achieved via additional markers (e.g., barcodes), pallets have a unique woodchip pattern in their blocks that can be exploited for re-identification if suitable methods are employed. This paper shows how this task can be accomplished in an open-set scenario. In this work, images of pallet blocks are transformed into vector representations using neural networks based on the ResNet-50 architecture. The networks are trained such that the Euclidean distance between image vectors of the same palette block is small. Several approaches, i.e., cross entropy, contrastive and triplet loss, to create the embedding space are compared. It is shown that Triplet Loss has advantages over other methods with a top-1 accuracy of 0.86 on the pallet-block-32965 dataset. In addition, a neighborhood-based approach to novelty detection called NSAND is presented, which outperforms a purely distance-based approach by up to 20%.

Novel in-house knowledge-based automated planning system for lung cancer treated with intensity-modulated radiotherapy.

The goal of this study was to propose aknowledge-based planning system which could automatically design plans for lung cancer patients treated with intensity-modulated radiotherapy (IMRT). From May 2018 to June 2020, 612 IMRT treatment plans of lung cancer patients were retrospectively selected to construct aplanning database. Knowledge-based planning (KBP) architecture named αDiar was proposed in this study. It consisted of two parts separated by afirewall. One was the in-hospital workstation, and the other was the search engine in the cloud. Based on our previous study, A‑Net in the in-hospital workstation was used to generate predicted virtual dose images. Asearch engine including athree-dimensional convolutional neural network (3D CNN) was constructed to derive the feature vectors of dose images. By comparing the similarity of the features between virtual dose images and the clinical dose images in the database, the most similar feature was found. The optimization parameters (OPs) of the treatment plan corresponding to the most similar feature were assigned to the new plan, and the design of anew treatment plan was automatically completed. After αDiar was developed, we performed two studies. The first retrospective study was conducted to validate whether this architecture was qualified for clinical practice and involved 96patients. The second comparative study was performed to investigate whether αDiar could assist dosimetrists in improving the quality of planning for the patients. Two dosimetrists were involved and designed plans for only one trial with and without αDiar; 26patients were involved in this study. The first study showed that about 54% (52/96) of the automatically generated plans would achieve the dosimetric constraints of the Radiation Therapy Oncology Group (RTOG) and about 93% (89/96) of the automatically generated plans would achieve the dosimetric constraints of the National Comprehensive Cancer Network (NCCN). The second study showed that the quality of treatment planning designed by junior dosimetrists was improved with the help of αDiar. Our results showed that αDiar was an effective tool to improve planning quality. Over half of the patients' plans could be designed automatically. For the remaining patients, although the automatically designed plans did not fully meet the clinical requirements, their quality was also better than that of manual plans.

Reconnection-generated Plasma Flows in the Quasi-separatrix Layer in Localized Solar Corona

Multiwavelength observations of the propagating disturbances (PDs), discovered by Atmospheric Imaging Assembly (AIA) on board Solar Dynamics Observatory (SDO), are analyzed to determine their driving mechanism and physical nature. Two magnetic strands in the localized corona are observed to approach and merge with each other, followed by the generation of brightening, which further propagates in a cusp-shaped magnetic channel. Differential emission measure analysis shows an occurrence of heating in this region of interest. We extrapolate potential magnetic field lines at coronal heights from the observed Helioseismic and Magnetic Imager vector magnetogram via Green’s function method using MPI-AMRVAC. We analyze the field to locate magnetic nulls and quasi-separatrix layers (QSLs), which are preferential locations for magnetic reconnection. Dominant QSLs including a magnetic null are found to exist and match the geometry followed by PDs; therefore, this provides conclusive evidence of magnetic reconnection. In addition, spectroscopic analysis of Interface Region Imaging Spectrograph Si iv λ1393.77 line profiles show a rise of line width in the same time range depicting the presence of mass motion in the observed cusp-shaped region. PDs are observed to exhibit periodicities of around 4 minutes. The speeds of PDs measured by the surfing transform technique are close to each other in four different SDO/AIA bandpasses, i.e., 304, 171, 193, and 131 Å, excluding the interpretation of PDs in terms of slow magnetoacoustic waves. We describe comprehensively the observed PDs as quasiperiodic plasma flows generated as a result of periodic reconnection in the vicinity of a coronal magnetic null.

T2-fuzzy multi-fused facial image fusion (T2FMFImgF): An efficient face recognition

This paper presents a novel decision-making method for face recognition where the features were extracted from the original image fused with its corresponding true and partial diagonal images. To extract features, we adopted the generalized two-dimensional FLD (G2DFLD) feature extraction technique. The feature vectors from a test image are given as input to neural network-based classifier. It is trained with the feature vectors of original image and diagonally fused images and thereby the merit weights with respect to different classes were generated. To address the factors that affect the face recognition accuracy and uncertainty related to raw biometric data, a fuzzy score for each of the classes is generated by treating a type-2 fuzzy set. This type-2 fuzzy set is formed by the feature vectors of both the diagonally fused training samples and the test image of the respective classes. A concluding score for each of the classes under consideration is computed by fusing complemented merit weight with the complemented fuzzy score. These class-wise concluding scores are considered in the face recognition process. In this study, the well-known face databases (AT&T, UMIST and CMU-PIE) are used to evaluate the performance of the proposed method. The experimental results illustrate the fact that the proposed method has exhibited superior classification precision as compared with other state-of-art methods. Our T2FMFImgF method achieves highest face recognition accuracies of 99.41%, 98.36% and 89.80% in case of AT&T, UMIST and CMU-PIE (with expression), respectively while for CMU-PIE (with Light) the highest recognition accuracy is 97.957%. In addition to it, the presented method is quite successful in fusing and classifying textural information from the original and partial diagonal images by integrating them with type-2 fuzzy set-based treatment.

Ultrasound Speckle Decorrelation-Based Blood Flow Measurements.

Ultrasound imaging is the preferred noninvasive technique to measure blood flow to diagnose cardiovascular disease such as heart failure, carotid stenosis, and renal failure. Conventional ultrasound techniques such as Doppler ultrasound, ultrasound imaging velocimetry, vector Doppler and transverse oscillation beamforming have been used for blood flow velocity profile measurement. However, these techniques were limited to measuring blood flow velocities within the 2-D lateral (across the ultrasound beam) plane of a vessel, and the blood flow velocity profile was derived by assuming that blood vessels have a circular cross-section with axis symmetry. This assumption is incorrect because most vessels have complex geometries, such as tortuosity and branches, and an asymmetric flow profile in the presence of vascular plaque. Consequently, ultrasound speckle decorrelation has been proposed to measure blood flow from transverse views of blood vessels wherein the ultrasound beam is perpendicular to the vessel axis. In this review, we present a summary of recent progress in ultrasound speckle decorrelation-based blood flow measurement techniques.

Memorial GAN With Joint Semantic Optimization for Unpaired Image Captioning.

Most works of image captioning are implemented under the full supervision of paired image-caption data. Limited to expensive cost of data collection, the task of unpaired image captioning has attracted researchers' attention. In this article, we propose a novel memorial GAN (MemGAN) with the joint semantic optimization for unpaired image captioning. The core idea is to explore implicit semantic correlation between disjointed images and sentences through building a multimodal semantic-aware space (SAS). Concretely, each modality is mapped into a unified multimodal SAS, where SAS includes the semantic vectors of image I, visual concepts O, unpaired sentence S, and the generated caption C. We adopt the memory unit based on multihead attention and relational gate as a backbone to preserve and transit crucial multimodal semantics in the SAS for image caption generation and sentence reconstruction. Then, the memory unit is embedded into a GAN framework to exploit the semantic similarity and relevance in SAS, that is, imposing a joint semantic-aware optimization on SAS without supervision clues. To summarize, the proposed MemGAN learns the latent semantic relevance of SAS's multimodalities in an adversarial manner. Extensive experiments and qualitative results demonstrate the effectiveness of MemGAN, achieving improvements over state of the arts on unpaired image captioning benchmarks.

Analysis of Results of Digital Electroencephalography and Digital Vectors of Coronavirus Images upon Applying the Theory of Covariance Functions

This paper analyses the structures of covariance functions of digital electroencephalography measurement vectors and digital vectors of two coronavirus images. For this research, we used the measurement results of 30-channel electroencephalography (E1–E30) and digital vectors of images of two SARS-CoV-2 variants (cor2 and cor4), where the magnitudes of intensity of the electroencephalography parameters and the parameters of the digital images of coronaviruses were encoded. The estimators of cross-covariance functions of the digital electroencephalography measurements’ vectors and the digital vectors of the coronavirus images and the estimators of auto-covariance functions of separate vectors were derived by applying random functions constructed according to the vectors’ parameter measurement data files. The estimators of covariance functions were derived by changing the values of the quantised interval k on the time and image pixel scales. The symmetric matrices of correlation coefficients were calculated to estimate the level of dependencies between the electroencephalography measurement results’ vectors and the digital vectors of the coronavirus images. The graphical images of the normalised cross-covariance functions for the electroencephalography measurement results’ vectors and the digital vectors of the coronavirus images within the period of all measurements are asymmetric. For all calculations, a computer program was developed by applying a package of Matlab procedures. A probabilistic interdependence between the results of the electroencephalography measurements and the parameters of the coronavirus vectors, as well as their variation on the time and image pixel scales, was established.

An encoded histogram of ridge bifurcations and contours for fingerprint presentation attack detection

In recent years, the exponential growth of internet technologies has made personal authentication an integral part of security applications. The fingerprint-based biometric systems are essentially used to safeguard the users' privacy and confidentiality. However, such systems are prone to spoof attacks by artificial replicas of the fingerprints. This paper presents an improved feature extractor called BiRi-PAD (Encoded Histogram of Ridge Bifurcations and Contours for fingerprint Presentation Attack Detection) that aims to enhance the accuracy of live fingerprint detection. The feature extraction process of the proposed BiRi-PAD consists of four steps. First, the fingerprint image undergoes a process of extracting 2-channel ridge contour maps (2-RC maps). The first channel of 2-RC maps consists of ridge contours extracted by a set of derivative filters in the spatial domain whereas the second channel of 2-RC maps consists of the ridge contours extracted by the maximum moments based on phase congruency in the frequency domain. Further, minutiae-based feature information i.e., ridge bifurcations are extracted by the minimum moments based on phase congruency covariance. Moreover, a fusion equation is proposed to integrate 2-RC maps and bifurcations into a single feature map. Second, an improved Comprehensive Local Phase Quantization (CLPQ) based on the well-known feature descriptor Rotation Invariant Local Phase Quantization (LPQri) is proposed to extract the phase information of ridges. CLPQ extracts the orientation of the ridges by using the complex parts of the significant frequency components of LPQri and monogenic filters. Third, the proposed BiRi-PAD quantizes the 2-RC maps into pre-determined intervals. Finally, both 2-RC maps and CLPQ features are integrated to generate a feature vector of a single fingerprint image. Performance evaluations of BiRi-PAD are conducted on three publicly available benchmarks from the LivDet competition, namely LivDet 2013, 2011, and 2015. Experimental evaluations demonstrate that the proposed BiRi-PAD achieves significant reductions in average rates compared to state-of-the-art techniques of fingerprint liveness detection. Specifically, on LivDet 2013, LivDet 2011, and LivDet 2015, the average rates are reduced to 1.92%, 4.39%, and 4.55%, respectively.

ED2IF2-Net: Learning Disentangled Deformed Implicit Fields and Enhanced Displacement Fields from Single Images Using Pyramid Vision Transformer

There has emerged substantial research in addressing single-view 3D reconstruction and the majority of the state-of-the-art implicit methods employ CNNs as the backbone network. On the other hand, transformers have shown remarkable performance in many vision tasks. However, it is still unknown whether transformers are suitable for single-view implicit 3D reconstruction. In this paper, we propose the first end-to-end single-view 3D reconstruction network based on the Pyramid Vision Transformer (PVT), called ED2IF2-Net, which disentangles the reconstruction of an implicit field into the reconstruction of topological structures and the recovery of surface details to achieve high-fidelity shape reconstruction. ED2IF2-Net uses a Pyramid Vision Transformer encoder to extract multi-scale hierarchical local features and a global vector of the input single image, which are fed into three separate decoders. A coarse shape decoder reconstructs a coarse implicit field based on the global vector, a deformation decoder iteratively refines the coarse implicit field using the pixel-aligned local features to obtain a deformed implicit field through multiple implicit field deformation blocks (IFDBs), and a surface detail decoder predicts an enhanced displacement field using the local features with hybrid attention modules (HAMs). The final output is a fusion of the deformed implicit field and the enhanced displacement field, with four loss terms applied to reconstruct the coarse implicit field, structure details through a novel deformation loss, overall shape after fusion, and surface details via a Laplacian loss. The quantitative results obtained from the ShapeNet dataset validate the exceptional performance of ED2IF2-Net. Notably, ED2IF2-Net-L stands out as the top-performing variant, exhibiting the highest mean IoU, CD, EMD, ECD-3D, and ECD-2D scores, reaching impressive values of 61.1, 7.26, 2.51, 6.08, and 1.84, respectively. The extensive experimental evaluations consistently demonstrate the state-of-the-art capabilities of ED2IF2-Net in terms of reconstructing topological structures and recovering surface details, all while maintaining competitive inference time.

Building Change Detection in Remote Sensing Imagery with Focal Self-Attention and Multi-Level Feature Fusion

Accurate and intelligent building change detection greatly contributes to effective urban development, optimized resource management, and informed decision-making in domains such as urban planning, land management, and environmental monitoring. Existing methodologies face challenges in effectively integrating local and global features for accurate building change detection. To address these challenges, we propose a novel method that uses focal self-attention to process the feature vector of input images, which uses a “focusing” mechanism to guide the calculation of the self-attention mechanism. By focusing more on critical areas when processing image features in different regions, focal self-attention can better handle both local and global information, and is more flexible and adaptive than other methods, improving detection accuracy. In addition, our multi-level feature fusion module groups the features and then constructs a hierarchical residual structure to fuse the grouped features. On the LEVIR-CD and WHU-CD datasets, our proposed method achieved F1-scores of 91.62% and 89.45%, respectively. Compared with existing methods, ours performed better on building change detection tasks. Our method therefore provides a framework for solving problems related to building change detection, with some reference value and guiding significance.