A fuzzy spectral clustering algorithm for hyperspectral image classification

Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance. In the past years, a growing number of advanced hyperspectral remote sensing image classification techniques based on manifold learning, sparse representation and deep learning have been proposed and reported a good performance in accuracy and efficiency on state-of-the-art public datasets. However, most existing methods still face challenges in dealing with large-scale hyperspectral image datasets due to their high computational complexity. In this work, we propose an improved spectral clustering method for large-scale hyperspectral image classification without any prior information. The proposed algorithm introduces two efficient approximation techniques based on Nyström extension and anchor-based graph to construct the affinity matrix. We also propose an effective solution to solve the eigenvalue decomposition problem by multiplicative update optimization. Experiments on both the synthetic datasets and the hyperspectral image datasets were conducted to demonstrate the efficiency and effectiveness of the proposed algorithm.

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Clustering-Based Band Selection Using Structural Similarity Index and Entropy for Hyperspectral Image Classification

Traitement du signal ◽

10.18280/ts.370510 ◽

2020 ◽

Vol 37 (5) ◽

pp. 785-791

Author(s):

Arsalan Ghorbanian ◽

Yasser Maghsoudi ◽

Ali Mohammadzadeh

Keyword(s):

Image Classification ◽

Clustering Algorithm ◽

Hyperspectral Image ◽

State Of The Art ◽

Similarity Index ◽

Structural Similarity ◽

Kappa Coefficient ◽

Hyperspectral Image Classification ◽

Structural Similarity Index ◽

Classification Tasks

Despite the unique capabilities of hyperspectral images for classification tasks, handling the high dimension of these data is challenging. Therefore, dimension reduction algorithms have been proposed to solve this challenge. In this paper, an unsupervised Feature Selection (FS) algorithm was proposed for hyperspectral image classification. First, the entropy values of hyperspectral bands were employed to identify and remove noisy bands. Afterward, the Structural Similarity (SSIM) index and the k-means clustering algorithm were combined to select a few representative bands. Subsequently, the selected bands were injected into a supervised classifier, and the obtained Overall Accuracy (OA) and Kappa Coefficient (KC) were used to evaluate the performance of the proposed method. Finally, the results were compared with the ones achieved from other well-known and state-of-the-art FS approaches. The results revealed that the proposed method outperformed other FS algorithms. Furthermore, the proposed FS algorithm obtained equal or higher OA and KC in comparison with the case of employing all hyperspectral bands. Additionally, a stability analysis step was performed to investigate the consistency of the proposed method. The results suggest the potential of the FS approach for hyperspectral image classification.

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