Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification

Razika Bazine,Kamel Boukhechba,Huayi Wu

doi:10.3390/rs11121405

Abstract

In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.

Highlights

Hyperspectral imagery is collected by hyperspectral remote sensors at hundreds of narrow spectral bands
The count of the retained coefficients after performing the spectral Discrete Cosine Transform (DCT) represents the spectral filter parameter. This parameter is set by comparing the classification accuracy Overall Accuracy (OA) with varying numbers of DCT coefficients where the count of the retained coefficients corresponds to the most accurate classification OA for each dataset
We present two effective classification frameworks based on cascaded spectral and spatial filtering where the spatial filter is performed in the DCT domain

Summary

Introduction

Hyperspectral imagery is collected by hyperspectral remote sensors at hundreds of narrow spectral bands. It contains rich discriminative spectral and spatial characteristics regarding material surfaces [1] This attribute makes hyperspectral imagery an interesting source of information for a wide variety of applications in areas such as agriculture, environmental planning, surveillance, target detection, medicine [2,3,4], etc. The SVM classifier is efficient [5], since it requires relatively few training samples to obtain high classification accuracy and robustness regarding the high spectral dimensionality [5]. These pixel-wise classifiers can fully use the spectral information in the hyperspectral imagery, the spatial dimension is not takenin consideration. The resulting classification maps are corrupted with salt-and-pepper noise [13]

Methods

Results

Conclusion