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A Semantic Feature Extraction Method For Hyperspectral Image Classification Based On Hashing Learning | IEEE Conference Publication | IEEE Xplore

A Semantic Feature Extraction Method For Hyperspectral Image Classification Based On Hashing Learning


Abstract:

Aiming at extraction the semantic feature of hyperspectral image, a semantic feature extraction method based on supervised hashing learning is proposed in the paper. Firs...Show More

Abstract:

Aiming at extraction the semantic feature of hyperspectral image, a semantic feature extraction method based on supervised hashing learning is proposed in the paper. Firstly, a set of hash functions are defined based on hyperspectral target subspace constraint which take into account the locality and discriminability between classes. Secondly, a semantic subspace is obtained through discriminative learning algorithm by the label information of hyperspectral image. Finally, the sparse binary hash codes are obtained by eigenvector mapping which represents the semantic features of targets. In the method, hashing learning uses the similarity binary codes to express the similarity of the original hyperspectral spatial data, and it uses both spectral features and spatial neighborhood features, which leads to strong distinguishing ability on a certain class of hyperspectral image. The experimental on real hyperspectral images classification results show that the fusion of the extracted semantic features with the original hyperspectral features can effectively improve the classification accuracy.
Date of Conference: 23-26 September 2018
Date Added to IEEE Xplore: 27 June 2019
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Conference Location: Amsterdam, Netherlands
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1. Introduction

Hyperspectral remote sensing combines spatial geometry imaging with spectroscopy. In recent years, it has been widely used in the fields of environmental monitoring, precision agriculture and defense security. The development of high spectral-spatial resolution make the hyperspectral data volume increase sharply, the high-dimensional data contains redundant information which may cause computational complexity and lead to the Hughes phenomenon, so, it is very necessary to reduce the amount of data and save resources. The feature extraction and band selection are the two main methods for reducing the dimension of HSIs. Recently, many band selection methods (BS) are proposed because it can preserve the original features of remote sensing data, such as, a Particle Swarm optimization (PSO)-based band selection is proposed in [1], Fishers linear discriminant analysis (LDA) [2], a supervised method based on the rough set theory for hyperspectral band selection is presented in [3], Wang et al. [4] introduce a manifold ranking (MR)-based band selection method. And also, a lot of methods based on spectral information have been proposed in[5–8]. But most band selection methods have disadvantages in the identification and discrimination of classes.

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