Land cover classification of satellite images has been a very predominant area since the last few years. An increase in the amount of information acquired by satellite imaging systems, urges the need for automatic tools for classification. Satellite images exhibit spatial and/or temporal dependencies in which the conventional machine learning algorithms fail to perform well. In this paper, we propose an improved framework for automated land cover classification using Spatial Spectral Schroedinger Eigenmaps (SSSE) optimized by Cuckoo Search (CS) algorithm. Support Vector Machine (SVM) is adopted for the final thematic map generation following dimensionality reduction and clustering by the proposed approach. The novelty of the proposed framework is that the applicability of optimized SSSE for land cover classification of medium and high resolution multi-spectral satellite images is tested for the first time. The proposed method makes land cover classification system fully automatic by optimizing the algorithm specific image dependent parameter α using CS algorithm. Experiments are carried out over publicly available high and medium resolution multi-spectral satellite image datasets (Landsat 5 TM and IKONOS 2 MS) and hyper-spectral satellite image datasets (Pavia University and Indian Pines) to assess the robustness of the proposed approach. Performance comparisons of the proposed method against state-of-the-art multi-spectral and hyper-spectral land cover classification methods reveal the efficiency of the proposed method.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Condensed Matter Physics