Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Li, Liangliang; Ma, Hongbing; Jia, Zhenhong

doi:10.3390/e24020291

Cited by 14 publications

(13 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The field of homogeneous change detection has seen significant advancements with the application of deep-learning techniques. These innovations have led to more sophisticated and efficient methods for analyzing changes in both optical remote sensing images and hyperspectral images, as well as SAR images [11][12][13][14][15][16][17][18][19][20][21][22][23]. For instance, Wu et al [14] proposed a spatial-temporal association-enhanced mobile-friendly vision transformer specifically designed for the change detection of high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the impressive strides made by deep learning in the realm of SAR change detection, traditional algorithms continue to play a vital role, showcasing their enduring strengths. These traditional methods primarily involve steps such as image registration, image preprocessing (for instance, speckle noise reduction), computation of difference images (DIs), and the application of thresholds or classification techniques to produce change detection maps [21]. Notably, operators like the log-ratio (LR), mean-ratio (MR), and neighbor-ratio (NR) are extensively utilized to generate difference images [22], while methods such as the Otsu algorithm, fuzzy C-means clustering (FCM), and fuzzy local information C-means clustering are employed to derive the change maps [23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Li,

Ma,

Zhang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Li,

Ma,

Zhang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…(2) Second, DL-based models have inadequate feature fusion at different levels during the decoding process. While many works have explored the concatenation and fusion of low-level features in the encoding stage through skip connections, only a limited number of studies have addressed the incorporation of deep features in the decision-making layer [36]. However, low-level features can introduce significant noise.…”

Section: Introductionmentioning

confidence: 99%

A Heterogeneity-Enhancement and Homogeneity-Restraint Network (HEHRNet) for Change Detection from Very High-Resolution Remote Sensing Imagery

Wang,

He,

Wang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Change detection (CD), a crucial technique for observing ground-level changes over time, is a challenging research area in the remote sensing field. Deep learning methods for CD have made significant progress in remote sensing intelligent interpretation. However, with very high-resolution (VHR) satellite imagery, technical challenges such as insufficient mining of shallow-level features, complex transmission of deep-level features, and difficulties in identifying change information features have led to severe fragmentation and low completeness issues of CD targets. To reduce costs and enhance efficiency in monitoring tasks such as changes in national resources, it is crucial to promote the practical implementation of automatic change detection technology. Therefore, we propose a deep learning approach utilizing heterogeneity enhancement and homogeneity restraint for CD. In addition to comprehensively extracting multilevel features from multitemporal images, we introduce a cosine similarity-based module and a module for progressive fusion enhancement of multilevel features to enhance deep feature extraction and the change information utilization within feature associations. This ensures that the change target completeness and the independence between change targets can be further improved. Comparative experiments with six CD models on two benchmark datasets demonstrate that the proposed approach outperforms conventional CD models in various metrics, including recall (0.6868, 0.6756), precision (0.7050, 0.7570), F1 score (0.6958, 0.7140), and MIoU (0.7013, 0.7000), on the SECOND and the HRSCD datasets, respectively. According to the core principles of change detection, the proposed deep learning network effectively enhances the completeness of target vectors and the separation of individual targets in change detection with VHR remote sensing images, which has significant research and practical value.

show abstract

“…The transform domain method is to reconstruct the decomposed bands by fusing them with relevant rules after transforming the image with multi-scale decomposition. Contourlet Transform (CT) method in reference [13], through the directional anisotropy and local information property of contour wave, solves the problem of poor performance of sub-band directional information and contour structure in image fusion; the reference [14] proposes Non-Subsampled Contourlet Transform (NSCT) based on CT solves the translation invariance while inheriting the advantages of CT; the reference [15] completes the fusion of multimodal medical images by NSCT; the reference [16] fuses two difference remote sensing images by NSCT for natural disaster change detection. Meanwhile, artificial neural networks [17] and convolutional neural networks [18] are also widely used in image fusion to improve image fusion quality.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Reconstruction Method Based on Parameter Adaptive Dual-Channel Pulse-Coupled Neural Network to Optimize Multiscale Decomposition

Tang

Zhang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Existing image processing methods usually divide image denoising and image fusion into two directions for research, and even the best current image denoising methods such as DnCNN can cause information loss during image processing, and the image fusion method mainly considers the fusion between multiple source images to complement the image information, but does not take into account the degradation of the fusion quality due to the noise in the source images. Therefore, aiming at the problem that various existing image denoising methods cannot reduce noise efficiently in complex noise situations while performing multi-source image fusion, preserving texture details, highlighting edge contour structures, and enriching image energy, a method of reconstructing remote sensing images by simplified adaptive dual-channel PCNN (Dual-PCNN) fusion in the NSCT transform domain is proposed to unify image noise reduction and image fusion under the same framework to obtain a noisy image information reconstruction method, which completes the complementary advantages between two image processing methods. Firstly, the impulse noise in the image is removed using IMFLED filtering, and then the Gaussian noise is processed by DnCNN and FFDNet respectively, and the results are imported into simplified parametric adaptive dual-channel PCNN to fuse the preprocessed images respectively. Then the DnCNN image and the preprocessed image are decomposed by NSCT to obtain each low-frequency sub-band and high-frequency sub-band. The low-frequency sub-band is stimulated with detail using the guided filter, and the high-frequency sub-band is enhanced by separating the detail layer through the guided filter to obtain the energy-detail-enhanced high-frequency sub-band. Finally, the corresponding sub-bands are fused into the simplified parametric adaptive dual-channel PCNN respectively, and the fused sub-band coefficients are reconstructed by NSCT inversion to obtain the final reconstructed remote sensing image. The experiments on grayscale images and remote sensing images show that this method achieves excellent results in both visual subjective and quantitative index evaluation, and the reconstructed images perform well in texture details, contour structure, and energy enrichment. After the information reconstruction of remote sensing images, the quality and resolution of remote sensing images are effectively improved, so that terrain information, landform features, and structural features can be extracted more accurately, which are widely used in remote sensing and geographic information fields such as landform analysis, agricultural monitoring, building inspection and environmental protection.INDEX TERMS Image reconstruction, non-subsampled contourlet transform (NSCT), pulse-coupled neural network (PCNN), convolutional neural network (CNN), guided filter.

show abstract

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Cited by 14 publications

References 49 publications

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

A Heterogeneity-Enhancement and Homogeneity-Restraint Network (HEHRNet) for Change Detection from Very High-Resolution Remote Sensing Imagery

Remote Sensing Image Reconstruction Method Based on Parameter Adaptive Dual-Channel Pulse-Coupled Neural Network to Optimize Multiscale Decomposition

Contact Info

Product

Resources

About