Sparse representation-based archetypal graphs for spectral clustering

Roscher, Ribana; Lukas, Lukas; Drees,; Wenzel, Susanne

doi:10.1109/igarss.2017.8127425

Cited by 2 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same methodology, improved by Yang et al in [54] by applying deep canonical correlation analysis, could not perform better than 0.947 (standard deviation 0.02) in OA and 0.71 (standard deviation 0.1) in KC. In [55], Roscher et al achieved a KC of 0.80 on the same dataset but by using two Landsat 5 TM images, so performing homogeneous CD. Overall, our unsupervised method consistently outperforms the aforementioned methods, both in terms of OA and KC.…”

Section: Resultsmentioning

confidence: 99%

Unsupervised Image Regression for Heterogeneous Change Detection

Luppino

Bianchi

Moser

et al. 2019

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Change detection in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the problem in an unsupervised manner. In this paper we propose an unsupervised framework for bitemporal heterogeneous change detection based on the comparison of affinity matrices and image regression. First, our method quantifies the similarity of affinity matrices computed from co-located image patches in the two images. This is done to automatically identify pixels that are likely to be unchanged. With the identified pixels as pseudo-training data, we learn a transformation to map the first image to the domain of the other image, and vice versa. Four regression methods are selected to carry out the transformation: Gaussian process regression, support vector regression, random forest regression, and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate the potentials and limitations of our framework, and also the benefits and disadvantages of each regression method, we perform experiments on two real data sets. The results indicate that the comparison of the affinity matrices can already be considered a change detection method by itself. However, image regression is shown to improve the results obtained by the previous step alone and produces accurate change detection maps despite of the heterogeneity of the multitemporal input data. Notably, the random forest regression approach excels by achieving similar accuracy as the other methods, but with a significantly lower computational cost and with fast and robust tuning of hyperparameters.

show abstract

Section: Resultsmentioning

confidence: 99%

Unsupervised Image Regression for Heterogeneous Change Detection

Luppino

Bianchi

Moser

et al. 2019

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…So far, many methods [16][17][18][19][24][25] are proposed to measure the similarities of all the data points. In these methods, sparse representation is a typical resolution and has been confirmed its outperformed performance on the high-dimensional and/or highly sparse datasets.…”

Section: A Self-representation Weighted Based Density Calculation Met...mentioning

confidence: 99%

“…Therefore, in order to make full use of the local and the global information, we propose a sparse representation-based method [24,25] to calculate the weight of each data point, referring to [19], the objective function of the proposed method is defined as follows:…”

Section: A Self-representation Weighted Based Density Calculation Met...mentioning

confidence: 99%

A Self-Representation Weighted based Density Peaks Clustering Method

YU,

Zhang,

WANG

et al. 2023

Preprint

View full text Add to dashboard Cite

The calculation method of density has an important influence on the clustering performance of density peak clustering method (DPC), the different density calculation methods are applied for the different datasets. To solve this problem, a self-representation weighted density peak clustering method (SR-DPC) is proposed in this study. Different from DPC, SR-DPC not only considers the local information of data points, but also enhances the influence of different data points on the data center by introducing the idea of weighting, so as to improve the accuracy of finding the data center.Furthermore, SR-DPC can adaptively reflect the influence of different data points on the data center points through the feature representation among data points, and the weighted Gaussian kernel distance is adopted to replace the Euclidean distance, so as to improve its clustering performance. Experimental results based on synthetic datasets and real datasets show that SR-DPC method is effective and practical.

show abstract

Sparse representation-based archetypal graphs for spectral clustering

Cited by 2 publications

References 13 publications

Unsupervised Image Regression for Heterogeneous Change Detection

Unsupervised Image Regression for Heterogeneous Change Detection

A Self-Representation Weighted based Density Peaks Clustering Method

Contact Info

Product

Resources

About