Target Detection of Hyperspectral Images Based on Their Fourier Spectral Features

Original spectral features contain information pertinent to certain target spectral features. Without an efficient spectral feature extraction method, the target detection performance might be degraded. We present spectral feature extraction techniques based on the Fourier domain for use in target detection. These feature extraction methods are the Fourier magnitude (FM), Fourier phase (FP), and Fourier coefficient selection (FCS)methods. In our target detection experiments, we compared the proposed methods to the principle component analysis (PCA) and independent component analysis (ICA) methods and the original spectral features. The experiment results show that the FCS target detection accuracy is 95.75%, whereas the accuracies of the FM, FP, PCA, ICA methods,and the original spectral features are 86.76%, 36.28%, 84.51%, 74.49%,and 78.92%, respectively. The average feature extraction times of the proposed methods are 223% faster than that found for the PCA and 304% faster than the ICA methods

A Robust Texture Feature Extraction using the Localized Angular Phase

This paper proposes a novel descriptor, referred to as the localized angular phase (LAP), which is robust to illumination, scaling, and image blurring. LAP utilizes the phase information from the Fourier transform of the pixels in localized polar space with a fixed radius. The application examples of LAP are presented in terms of content-based image retrieval, classification, and feature extraction of realworld degraded images and computer-aided diagnosis using medical images. The experimental results show that the classification performance of LAP in terms of the latter application examples are better than those of local phase quantization (LPQ), local binary patterns (LBP), and local Fourier histogram (LFH). Specially, the capability of LAP to analyze degraded images and classify abnormal regions in medical images are superior to those of other methods since the best overall classification accuracy of LAP, LPQ, LBP, and LFH using degraded textures are 91.26, 61.23, 35.79, and 33.47%, respectively, while the sensitivity of LAP, LBP, and spatial gray level dependent method (SGLDM) in classifying abnormal lung regions in CT images are 100, 95.5, and 93.75%, respectively