INTRINSIC DIMENSIONALITY ESTIMATION IN HYPERSPECTRAL IMAGES

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HASANLOU M.; SAMADZADEGAN F.

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Journal: JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY Year:2014 | Volume:3 | Issue:3 Page(s): 101-109

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Title

INTRINSIC DIMENSIONALITY ESTIMATION IN HYPERSPECTRAL IMAGES

Author(s)

HASANLOU M. | SAMADZADEGAN F. | Issue Writer Certificate

Keywords

HYPER SPECTRAL IMAGESQ1

DIMENSION REDUCTIONQ2

INTRINSIC DIMENSION ESTIMATIONQ2

FEATURE EXTRACTIONQ2

SUPERVISED CLASSIFICATIONQ2

Abstract

By development of remote sensing sensors, hyper spectral remote sensing images are now widely available for monitoring the earth’s surface by using high spectral resolution and dimension. However, this large dimension not only increases computational complexity but also degrades classification accuracy. Dimensionality reduction is a major issue to improve the efficiency of the classifiers in HYPER SPECTRAL IMAGES. The common ways for dimensional reduction is FEATURE EXTRACTION. Ideally, the reduced representation has a dimensionality that corresponds to the intrinsic dimensionality of the data. There are a wide range of methods in intrinsic dimensionality estimation and dimensionality reduction of HYPER SPECTRAL IMAGES in literatures. In this paper, we discuss and compare five intrinsic dimensionality estimation (IDE) techniques for hyper spectral dimensionality reduction. We investigate the performance of these techniques for IDE on HYPER SPECTRAL IMAGES, and compare their performances for supervised image classification purpose. These techniques include; Eigen value estimator (EV), Maximum likelihood estimator (ML), Correlation dimension estimator (CD), Packing number estimator (PN) and geodesic minimum spanning tree (GMST) estimator. The K-Nearest Neighbor (KNN) classifier used for supervised image classification. The variety of the distance metric was used and compared in this classifier. The most useful and practical methods for reduction of dimensionality Principal Component Analysis (PCA) and Independent Components Analysis (ICA) were used in output of these IDE techniques. This study presented a review and comparative study of techniques in IDE. Then feature bands used in SUPERVISED CLASSIFICATION with variety in parameters usage.

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APA: Copy

HASANLOU, M., & SAMADZADEGAN, F.. (2014). INTRINSIC DIMENSIONALITY ESTIMATION IN HYPERSPECTRAL IMAGES. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, 3(3), 101-109. SID. https://sid.ir/paper/249416/en

Vancouver: Copy

HASANLOU M., SAMADZADEGAN F.. INTRINSIC DIMENSIONALITY ESTIMATION IN HYPERSPECTRAL IMAGES. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY[Internet]. 2014;3(3):101-109. Available from: https://sid.ir/paper/249416/en

IEEE: Copy

M. HASANLOU, and F. SAMADZADEGAN, “INTRINSIC DIMENSIONALITY ESTIMATION IN HYPERSPECTRAL IMAGES,” JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, vol. 3, no. 3, pp. 101–109, 2014, [Online]. Available: https://sid.ir/paper/249416/en

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