Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision

Abstract

Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the effect of hyperspectral anomaly detection (HAD). In this paper, a dual dictionaries construction method via two-stage complementary decision (DDC–TSCD) for HAD is proposed. In the first stage, an adaptive inner window–based saliency detection was proposed to yield a coarse binary map, acting as the indicator to select pure background pixels. For the second stage, a background estimation network was designed to generate a fine binary map. Finally, the coarse binary map and fine binary map worked together to construct a pure background dictionary and potential anomaly dictionary in the guidance of the superpixels derived from the first stage. The experiments conducted on public datasets (i.e., HYDICE, Pavia, Los Angeles, San Diego-I, San Diego-II and Texas Coast) demonstrate that DDC–TSCD achieves satisfactory AUC values, which are separately 0.9991, 0.9951, 0.9968, 0.9923, 0.9986 and 0.9969, as compared to four typical methods and three state-of-the-art methods.

Dual Collaborative Constraints Regularized Low-Rank and Sparse Representation via Robust Dictionaries Construction for Hyperspectral Anomaly Detection

“…where softmax( ( ; )) 13)- (15). With this condition, we can acquire the density map through utilizing pdf under the estimated parameters.…”

“…YPERSPECTRAL images (HSIs) have been widely used in the field of band selection [1][2][3], image classification [4][5][6], hyperspectral pansharpening [7][8][9], hyperspectral unmixing [10,11], anomaly detection [12][13][14][15] and target detection [16][17][18], owing to the rich spectral information. Hyperspectral anomaly detection (HAD), which aims to search for the spectral signatures deviated from the background, has become a research hot topic due to the widespread application on the military defense, maritime rescue and mineral exploration.…”

Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation

“…Among them, HSIs can provide rich spectral information (i.e., about 10-nm spectral resolution), which makes it possible to identify the ground objects with different characteristics by means of the spectral information [6]. Based on the above advantage, the HSIs are widely employed in the field of hyperspectral image classification [7]- [8], hyperspectral unmixing [9], [10], hyperspectral pansharpening [11], [12], band selection [13], [14], hyperspectral anomaly detection (HAD) [15], [16] and hyperspectral target detection [17], [18], etc. The HAD, which aims to search for the pixels whose spectral signatures are deviated from the surrounding background pixels without priori knowledge about anomalies, has attracted extensive attention in the military and civilian fields.…”

Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network

“…Machine learning-based approaches extract meaningful attributes from raw data to describe and represent the data. These methods have shown remarkable performance in computer vision [ 16 ], natural language processing [ 17 ], recommendation systems [ 18 ], object detection [ 19 ], anomaly detection [ 20 , 21 , 22 ], and other domains. In recent years, the field of deep learning has witnessed remarkable progress, giving rise to a diverse range of network models, such as convolutional neural network (CNN) [ 23 ], recurrent neural network (RNN) [ 24 ], graph neural network (GNN) [ 25 ], and transformer network [ 26 ] models.…”