Hyperspectral Anomaly Detection Based on Multiscale Central Difference Convolution Network

Xiaoyi Wang

doi:10.1109/LGRS.2023.3305814

Hyperspectral Anomaly Detection Based on Multiscale Central Difference Convolution Network

2023-01-01

1

PRE

AI

DOI：10.1109/LGRS.2023.3305814

Original

Original request for help

Save

Abstract

En 中文

Convolutional neural networks (CNNs) have a strong capacity to extract deep-level features from data. However, the standard convolution (SC) only considers the intensity information and ignores the spatial gradient information. Since spatial difference features are more robust to illumination invariance, this letter proposes a multiscale central difference convolutional (MSCDC) network for hyperspectral anomaly detection. Specifically, we use central difference convolution (CDC) to combine intensity and gradient information. This solution improves the representation ability of hyperspectral images (HSIs) and enhances the difference between the background and the anomalies. Furthermore, to fully utilize local spatial information and adapt to targets with different sizes, CDC kernels of three different sizes are used to capture high-, mid-, and low-level features, respectively. Finally, an SC is used to fuse multiscale features and obtain more reliable spatial information. Compared with five popular hyperspectral anomaly detection methods on four real-world HSI datasets, the proposed MSCDC exhibits excellent performance.

Keywords:

Anomaly detection

central differential convolution

hyperspectral image (HSI)

multiscale

spatial difference

Hyperspectral Anomaly Detection Based on Multiscale Central Difference Convolution Network

Abstract

Journal

Organization