Clustering Multispectral Images Using Spatial–Spectral Information

Abstract

Clustering is an important topic in image analysis and has many applications. Owing to the limitations of the feature space in multispectral images and spectral overlap of the clusters, it is required to use some additional information such as the spatial context in image clustering. To increase the accuracy of image clustering, a new Hierarchical Iterative Clustering Algorithm using Spatial and Spectral information (HICLASS) is introduced. This algorithm separates pixels into uncertain and certain categories based on decision distances in the feature space. The algorithm labels the certain pixels using the k-means clustering, and the uncertain ones with the help of information in both spatial and spectral domains of the image. The proposed algorithm is tested using simulated and real data. The benchmark results indicate better performance of HICLASS when compared with the k-means, local embeddings, and some proximity-based algorithms. The overall accuracy of the k-means has increased between 12.5% and 20.4% for different data. The HICLASS method increases the accuracy and generates more homogeneous regions, which are required for object-based applications.

An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE)

“…Clustering has been one of the fundamental research topics in data-mining studies [4,5]. It is usually referred to as a subcategory of an unsupervised algorithm employed for dividing data into categories of a similar pattern without using training samples [6].…”