SPATIAL-SPECTRAL FUZZY K-MEANS CLUSTERING FOR REMOTE SENSING IMAGE SEGMENTATION
Keywords:spectral clustering, spatial information, remote sensing image, fuzzy clustering
Spectral clustering is a clustering method based on algebraic graph theory. The clustering effect by using spectral method depends heavily on the description of similarity between instances of the datasets. Althought, spectral clustering has been significant interest in recent times, but the raw spectral clustering is often based on Euclidean distance, but it is impossible to accurately reflect the complexity of the data. Despite having a well-defined mathematical framework, good performance and simplicity, it suffers from several drawbacks, such as it is unable to determine a reasonable cluster number, sensitive to initial condition and not robust to outliers. In this paper, we present a new approach named spatial-spectral fuzzy clustering which combines spectral clustering and fuzzy clustering with spatial information into a unified framework to solve these problems, the paper consists of three main steps: Step 1, calculate the spatial information value of the pixels, step 2 applies the spectral clustering algorithm to change the data space from the color space to the new space and step 3 clusters the data in new data space by fuzzy clustering algorithm. Experimental results on the remote sensing image were evaluated based on a number of indicators, such as IQI, MSE, DI and CSI, show that it can improve the clustering accuracy and avoid falling into local optimum.
Authors who publish with Vietnam Journal of Science and Technology agree with the following terms:
- The manuscript is not under consideration for publication elsewhere. When a manuscript is accepted for publication, the author agrees to automatic transfer of the copyright to the editorial office.
- The manuscript should not be published elsewhere in any language without the consent of the copyright holders. Authors have the right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of their work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are encouraged to post their work online (e.g., in institutional repositories or on their websites) prior to or during the submission process, as it can lead to productive exchanges or/and greater number of citation to the to-be-published work (See The Effect of Open Access).