| タイトル | Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis |
| 本文（外部サイト） | http://hdl.handle.net/2060/20150001288 |
| 著者（英） | Tilton, James C.; Tarabalka, Yuliya |
| 著者所属（英） | NASA Goddard Space Flight Center |
| 発行日 | 2012-07-22 |
| 言語 | eng |
| 内容記述 | A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods. |
| NASA分類 | Earth Resources and Remote Sensing |
| レポートNO | GSFC-E-DAA-TN17814 |
| 権利 | Copyright, Distribution as joint owner in the copyright |
