Landslide Inventory Mapping Method Based on Adaptive Histogram-Mean Distance With Bitemporal VHR Aerial Images

Liu, Tongfei; Gong, Maoguo; Jiang, Fenlong; Zhang, Yuanqiao; Li, Hao

doi:10.1109/lgrs.2021.3078732

Cited by 10 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During past few decades, many CD methods have been proposed and applied in practical applications [27,28]. In the early development of CD, two main steps are usually required to realize CD: measuring the difference image (DI) and obtaining the change detection map (CDM).…”

Section: Related Work 21 Traditional CD Methodsmentioning

confidence: 99%

Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images

et al. 2021

Self Cite

View full text Add to dashboard Cite

Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global combination ratio is usually required. To address these problems, in this paper, we propose a novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism to detect changes in hyperspectral images. The whole framework is divided into two steps. First, a self-supervised mapping from each patch of the difference map to the principal components of the central pixel of each patch is established. By using the multi-layer convolutional neural network, the main spatial features of differences can be extracted. In the second step, the attention mechanism is introduced. Specifically, the weighting factor between the spatial and spectral features of each pixel is adaptively calculated from the concatenated spatial and spectral features. Then, the calculated factor is applied proportionally to the corresponding features. Finally, by the joint analysis of the weighted spatial and spectral features, the change status of pixels in different positions can be obtained. Experimental results on several real hyperspectral change detection data sets show the effectiveness and advancement of the proposed method.

show abstract

Section: Related Work 21 Traditional CD Methodsmentioning

confidence: 99%

Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The method proposed by Sun et al improves the detection accuracy of building edge by combining CNN with active contour model [16]. Yuan et al designed a CNN with a simple structure, which integrates pixel-level prediction activated by multiple layers and introduces a symbolic distance function to establish boundaries to represent the output, which has a stronger representation ability [17,18]. In addition, BRRNet proposed by Shao et al introduced the atrous convolution of different dilation rates to extract more global features by gradually increasing the receiving field in the feature extraction process and the residual refinement module to further refine the residual between the result of the prediction module and the real result [19].…”

Section: Introductionmentioning

confidence: 99%

DFANet: Denoising Frequency Attention Network for Building Footprint Extraction in Very-High-Resolution Remote Sensing Images

Lu,

Liu,

Jiang

et al. 2023

Electronics

View full text Add to dashboard Cite

With the rapid development of very-high-resolution (VHR) remote-sensing technology, automatic identification and extraction of building footprints are significant for tracking urban development and evolution. Nevertheless, while VHR can more accurately characterize the details of buildings, it also inevitably enhances the background interference and noise information, which degrades the fine-grained detection of building footprints. In order to tackle the above issues, the attention mechanism is intensively exploited to provide a feasible solution. The attention mechanism is a computational intelligence technique inspired by the biological vision system capable of rapidly and automatically catching critical information. On the basis of the a priori frequency difference of different ground objects, we propose the denoising frequency attention network (DFANet) for building footprint extraction in VHR images. Specifically, we design the denoising frequency attention module and pyramid pooling module, which are embedded into the encoder–decoder network architecture. The denoising frequency attention module enables efficient filtering of high-frequency noises in the feature maps and enhancement of the frequency information related to buildings. In addition, the pyramid pooling module is leveraged to strengthen the adaptability and robustness of buildings at different scales. Experimental results of two commonly used real datasets demonstrate the effectiveness and superiority of the proposed method; the visualization and analysis also prove the critical role of the proposal.

show abstract

“…The changes in buildings in areas of interest are studied over time. Change detection (CD) is a technique that has been widely used in the field of remote sensing [4,5]. The purpose of CD is to obtain land cover changes by comparing and analyzing multi-temporal remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Discrete Cosine Transform Network for Building Change Detection in Very-High-Resolution Remote Sensing Images

Zhu,

Fan,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

With the rapid development and promotion of deep learning technology in the field of remote sensing, building change detection (BCD) has made great progress. Some recent approaches have improved detailed information about buildings by introducing high-frequency information. However, there are currently few methods considering the effect of other frequencies in the frequency domain for enhancing feature representation. To overcome this problem, we propose a multi-scale discrete cosine transform (DCT) network (MDNet) with U-shaped architecture, which is composed of two novel DCT-based modules, i.e., the dual-dimension DCT attention module (D3AM) and multi-scale DCT pyramid (MDP). The D3AM aims to employ the DCT to obtain frequency information from both spatial and channel dimensions for refining building feature representation. Furthermore, the proposed MDP can excavate multi-scale frequency information and construct a feature pyramid through multi-scale DCT, which can elevate multi-scale feature extraction of ground targets with various scales. The proposed MDNet was evaluated with three widely used BCD datasets (WHU-CD, LEVIR-CD, and Google), demonstrating that our approach can achieve more convincing results compared to other comparative methods. Moreover, extensive ablation experiments also present the effectiveness of our proposed D3AM and MDP.

show abstract

Landslide Inventory Mapping Method Based on Adaptive Histogram-Mean Distance With Bitemporal VHR Aerial Images

Cited by 10 publications

References 20 publications

Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images

Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images

DFANet: Denoising Frequency Attention Network for Building Footprint Extraction in Very-High-Resolution Remote Sensing Images

Multi-Scale Discrete Cosine Transform Network for Building Change Detection in Very-High-Resolution Remote Sensing Images

Contact Info

Product

Resources

About