A Spectral–Spatial Anomaly Target Detection Method Based on Fractional Fourier Transform and Saliency Weighted Collaborative Representation for Hyperspectral Images

Abstract

Anomaly target detection methods for hyperspectral images (HSI) often have the problems of potential anomalies and noise contamination when representing background. Therefore, a spectral-spatial hyperspectral anomaly detection method is proposed in this article, which is based on fractional Fourier transform (FrFT) and saliency weighted collaborative representation. First, hyperspectral pixels are projected to the fractional Fourier domain by the FrFT, which can enhance the capability of the detector to suppress the noise and make anomalies to be more distinctive. Then, a saliency weighted matrix is designed as the regularization matrix referring to context-aware saliency theory and combined with the FrFT-based collaborative representation detector. The saliencyweighted regularization matrix assigns different pixels with different weights by using both spectral and spatial information, which can reduce the influence of the potential anomalous pixels embedded in the background when applying collaborative representation theory. Finally, to further improve the performance of the proposed method, a spectral-spatial detection procedure is employed to calculate final anomaly scores by using both spectral information and spatial information. The proposed method is compared with nine state-of-the-art hyperspectral anomaly detection methods on six HSI datasets, including two synthetic HSI datasets and four real-world HSI datasets. Extensive experimental results illustrate that the proposed method's detection performance outperforms other nine well-known compared methods in terms of area under the receiver operating characteristic (ROC) curve values, visual detection characteristics, ROC curve, and separability.

Spectral–Spatial Complementary Decision Fusion for Hyperspectral Anomaly Detection

“…The alternative methods that we used in the the LSMAD [30], the LSDM-MoG [32], the CDAS the SSFSCRD [45], and the AED [62] methods. I of experimental results, the three-dimensional re…”

“…The alternative methods that we used in the experiment are the RX [15], the LRX [16], the LSMAD [30], the LSDM-MoG [32], the CDASC [21], the 2S-GLRT [20], the BFAD [44], the SSFSCRD [45], and the AED [62] methods. In the parameter analysis and discussion of experimental results, the three-dimensional receiver operating characteristic (3D ROC) [67] curve and the corresponding two-dimensional ROC (2D ROC) curves, and the area under the 2D ROC curve (AUC) are utilized as evaluation indicators to quantitatively evaluate the performance of the proposed method.…”

“…Yao and Zhao [44] proposed the bilateral filter-based anomaly detection (BFAD) by combining spatial weights and spectral weights. Zhao et al [45] proposed the spectral-spatial anomaly detection method based on fractional Fourier transform and saliency weighted collaborative representation (SSFSCRD) to reduce background contamination from noise and anomaly targets. Recently, deep-learning methods [46] using spatial information and spectral information have also received widespread attention.…”

An Unmixing-Based Network for Underwater Target Detection From Hyperspectral Imagery

“…According to related research works [25], [26], targets pixels usually occupy a tiny percent of the total HSI in hyperspectral target detection task. Following this viewpoint, target-water mixed pixels in the underwater HSI own the sparse characteristic as well.…”

A Self-Improving Framework for Joint Depth Estimation and Underwater Target Detection from Hyperspectral Imagery

“…Target detection is one of most significant research focuses on HSI data processing, which can be interrupted as a binary classifier to determine whether the given pixel belongs to target or background spectrum. Recent research [10][11][12][13][14] reveal that hyperspectral target detection (HTD), which employs the signature of desired target as prior knowledge, is capable of finishing a detection mission without any spatial information and achieves remarkable performance in most land-based scenarios.…”