Remote sensing image classification based on semi-supervised adaptive interval type-2 fuzzy c-means algorithm

Xu, Jindong; Feng, Guozheng; Zhao, Tianyu; Sun, Xiao; Zhu, Meng

doi:10.1016/j.cageo.2019.06.005

Cited by 31 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results demonstrate that the proposed has higher change detection accuracy as compared to other methods and also provides a better automation level. Although the accuracy assessments of supervised classification algorithms in RS have already been studied by many researchers, most of the assessments are only focused on the accuracy of specific datasets but neglect the poor manner with which the problem is posed [ 20 ]. High accuracy in one specific dataset can sometimes be deceptive because of overfitting.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Razaque

Frej

Almiani

et al. 2021

Sensors

View full text Add to dashboard Cite

Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Razaque

Frej

Almiani

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In this case, a soft clustering method emerged, known as fuzzy c-means clustering (FCC). Similar to K-means clustering, the FCC method is widely used in image segmentation [45,46]. Cao et al [47] applied FCC for providing sensor deployment strategy for indoor Computational Intelligence and Neuroscience environment control, thus proving that application of FCC can facilitate the decision-making process.…”

Section: Pi Selection and Clusteringmentioning

confidence: 99%

A Multistakeholder Approach to the Airport Gate Assignment Problem: Application of Fuzzy Theory for Optimal Performance Indicator Selection

Liang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Airport gate assignment performance indicator selection is a complicated decision-making problem with strong subjectivity and difficulty in measuring the importance of each indicator. A better selection of performance indicators (PIs) can greatly increase the airport overall benefit. We adopt a multicriteria decision-making approach to quantify qualitative PIs and conduct subsequent selection using the fuzzy clustering method. First, we identify 21 commonly used PIs through literature review and survey. Subsequently, the fuzzy analytic hierarchy process technique was employed to obtain the selection criteria weights based on the relative importance of significance, availability, and generalisability. Further, we aggregated the selection criteria weights and experts’ score to evaluate each PI for the clustering process. The fuzzy-possibilistic product partition c-means algorithm was applied to divide the PIs into different groups based on the three selection criteria as partitioning features. The cluster with highest weights of the centre was identified as the very high-influence cluster, and 10 PIs were identified as a result. This study revealed that the passenger-oriented objective is the most important performance criterion; however, the relevance of the airport/airline-oriented and robustness-oriented performance objectives was highlighted as well. It also offers a scientific approach to determine the objective functions for future gate assignment research. And, we believe, through slight modifications, this model can be used in other airports, other indicator selection problems, or other scenarios at the same airport to facilitate policy making and real situation practice, hence facilitate the management system for the airport.

show abstract

“…Unsupervised learning methods such as fuzzy and C-means algorithms [9] to address the limited availability of labeled datasets but they may lead to poor performance. In contrast, supervised learning methods such as support vector machine inhibit a better performance due to the associated prior knowledge obtained from labeled datasets.…”

Section: Introductionmentioning

confidence: 99%

Training Compact Change Detection Network for Remote Sensing Imagery

et al. 2021

View full text Add to dashboard Cite

Change Detection (CD) is a hot remote sensing topic where the change zones are highlighted by analyzing bi-temporal or multi-temporal images. Recently, Deep learning (DL) paved the road to implement various reliable change detection approaches that overcome traditional CD methods limitation. However, high performance DL based approaches have explosion number of parameters that demanded extensive computation and memory usage in addition to large volumes of training data. To address this issue, we proposed a teacher-student setting for remote sensing imagery change detection. To distill the knowledge from the over-parameterized Siamese teacher network, we proposed tiny student network that was trained using the obtained categorical distribution of probability from the teacher paired Softmax output at high temperature. Practical Swarm Optimization (PSO) was applied in order to optimally configure student architecture. Finally, ample experiments were conducted on LEVIR-CD dataset. Also, we introduced EGSAR-CD dataset, which contains of a large set of bi-temporal SAR images with 460 image pairs (256×256). Experiment results indicate that we can reach up to 5.4× reduction rate in number of parameters with loss of accuracy between 5% and 6% on the LEVIR-CD and EGSAR-CD datasets utilizing selfknowledge distillation.

show abstract

Remote sensing image classification based on semi-supervised adaptive interval type-2 fuzzy c-means algorithm

Cited by 31 publications

References 28 publications

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

A Multistakeholder Approach to the Airport Gate Assignment Problem: Application of Fuzzy Theory for Optimal Performance Indicator Selection

Training Compact Change Detection Network for Remote Sensing Imagery

Contact Info

Product

Resources

About