Abstract
Extended morphological profile (EMP) is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs) at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectralspatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA) C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.
Highlights
Hyperspectral imaging (Goetz, 2009) sensors capture an image scene in hundreds of fine contiguous spectral bands in ultraviolet to infrared region providing rich spectral and spatial information
PCA (Joliffe, 2002), discrete boundary feature extraction (DBFE) (Lee and Landgrebe, 1993), discrete wavelet transform (DWT) (Bruce et al, 2002), and maximum noise fraction (MNF) (Chang and Du, 1999), etc. are among the most important feature extraction techniques used for dimensionality reduction
This paper presents an Extended morphological profile (EMP) based spectral-spatial classification method for hyperspectral images in parallel processing architecture
Summary
Hyperspectral imaging (Goetz, 2009) sensors capture an image scene in hundreds of fine contiguous spectral bands in ultraviolet to infrared region providing rich spectral and spatial information. Classification is an important tool for hyperspectral image analysis having applications in many areas including urban development, environmental studies, agricultural monitoring, and defense etc. Often dimensionality reduction is performed as a pre-classification step to mitigate such problems. Are among the most important feature extraction techniques used for dimensionality reduction. Better classification accuracies can be achieved by integrating spectral and spatial contents. Spatial information is not directly inherent with the pixels and usually determined using pixel neighborhood relationships. Some classes in an image may have similar spectral characteristics and complementary spatial information can help better discriminating the classes
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