Homogeneity Distance Classification Algorithm (HDCA): A Novel Algorithm for Satellite Image Classification

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2019-03, Vol.11 (5), p.546
• Main Authors: Firozjaei, Mohammad, Daryaei, Iman, Sedighi, Amir, Weng, Qihao, Alavipanah, Seyed
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Datasets; Gravity; HDCA; Homogeneity; Image classification; improved gravitational search algorithm; Information retrieval; Iterative methods; multispectral and hyperspectral imagery; Remote sensing; Satellite imagery; Satellites; Search algorithms; Support vector machines; Texture; weighted Manhattan distance
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11050546

Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called “homogeneity distance classification algorithm (HDCA)”. The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IGSA) was applied for selecting features and determining optimal feature space scale in HDCA. In the case of multispectral satellite image classification, the proposed method was compared with two well-known classification methods, Maximum Likelihood classifier (MLC) and Support Vector Machine (SVM). The results of the comparison indicated that overall accuracy values for HDCA, MLC, and SVM are 95.99, 93.15, and 95.00, respectively. Furthermore, the proposed HDCA method was also used for classifying hyperspectral reference datasets (Indian Pines, Salinas and Salinas-A scene). The classification results indicated substantial improvement over previous algorithms and studies by 2% in Indian Pines dataset, 0.7% in the Salinas dataset and 1.2% in the Salinas-A scene. These experimental results demonstrate that the proposed algorithm can classify both multispectral and hyperspectral remote sensing images with reliable accuracy because this algorithm uses the WMD in the classification process and the IGSA to select automatically optimal features for image classification based on spectral and texture information.