Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification

Razika Bazine,Huayi Wu,Kamel Boukhechba

doi:10.3390/rs11242906

Abstract

In this paper, spectral–spatial preprocessing using discrete wavelet transform (DWT) multilevel decomposition and spatial filtering is proposed for improving the accuracy of hyperspectral imagery classification. Specifically, spectral DWT multilevel decomposition (SDWT) is performed on the hyperspectral image to separate the approximation coefficients from the detail coefficients. For each level of decomposition, only the detail coefficients are spatially filtered instead of being discarded, as is often adopted by the wavelet-based approaches. Thus, three different spatial filters are explored, including two-dimensional DWT (2D-DWT), adaptive Wiener filter (AWF), and two-dimensional discrete cosine transform (2D-DCT). After the enhancement of the spectral information by performing the spatial filter on the detail coefficients, DWT reconstruction is carried out on both the approximation and the filtered detail coefficients. The final preprocessed image is fed into a linear support vector machine (SVM) classifier. Evaluation results on three widely used real hyperspectral datasets show that the proposed framework using spectral DWT multilevel decomposition with 2D-DCT filter (SDWT-2DCT_SVM) exhibits a significant performance and outperforms many state-of-the-art methods in terms of classification accuracy, even under the constraint of small training sample size, and execution time.

Highlights

Hyperspectral imagery contains rich discriminative spectral and spatial characteristics about surface materials [1], which makes it a prominent source of information for a varied range of applications in areas such as agriculture, environmental planning, surveillance, target detection, medicine [2,3,4], etc
Remote Sens. 2019, 11, 2906 representation, 3D feature extraction methods create a large number of spectral–spatial features, leading to another feature selection issue [40]. To handle these issues and fully exploit the spectral and spatial information in the original hyperspectral imagery, we propose three effective spectral–spatial hyperspectral classification frameworks based on performing the spatial filtering process in the spectral wavelet domain
We evaluated the performance of our proposed approaches on the Pavia University dataset against state-of-the-art methods with different training conditions, in which 2%, 4%, 6%, 8%, and 10% labeled samples of each class were randomly selected as training samples, and the rest were used for testing

Summary

Introduction

Hyperspectral imagery contains rich discriminative spectral and spatial characteristics about surface materials [1], which makes it a prominent source of information for a varied range of applications in areas such as agriculture, environmental planning, surveillance, target detection, medicine [2,3,4], etc. Most of these hyperspectral applications rely on the supervised classification task. The resulting classification maps are often corrupted with salt-and-pepper noise [15]

Methods

Results

Conclusion