Hyperspectral anomaly detection: a performance comparison of existing techniques
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Digital Earth"
DOI: 10.1080/17538947.2022.2146770
Abstract: ABSTRACT Anomaly detection in Hyperspectral Imagery (HSI) has received considerable attention because of its potential application in several areas. Numerous anomaly detection algorithms for HSI have been proposed in the literature; however, due to the… read more here.
Keywords: performance comparison; detection; hyperspectral anomaly; anomaly detection ... See more keywords
Hyperspectral Anomaly Detection by Fractional Fourier Entropy
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DOI: 10.1109/jstars.2019.2940278
Abstract: Anomaly detection is an important task in hyperspectral remote sensing. Most widely used detectors, such as Reed–Xiaoli (RX), have been developed only using original spectral signatures, which may lack the capability of signal enhancement and… read more here.
Keywords: hyperspectral anomaly; anomaly detection; fourier entropy; fractional fourier ... See more keywords
Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2021.3103858
Abstract: Hyperspectral anomaly detection has attracted extensive interests for its wide use in military and civilian fields, and three main categories of detection methods have been developed successively over past few decades, including statistical model-based, representation-based,… read more here.
Keywords: purification framework; detection; hyperspectral anomaly; background purification ... See more keywords
Self-Adaptive Low-Rank and Sparse Decomposition for Hyperspectral Anomaly Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3172120
Abstract: Hyperspectral anomaly detection is a widely used technique for exploring target of interest in hyperspectral images (HSIs). In recent years, the low-rank and sparse-decomposition-based anomaly detection model has attracted extensive attention. However, these models suffer… read more here.
Keywords: hyperspectral anomaly; rank sparse; low rank; detection ... See more keywords
Normalizing Flow-Based Probability Distribution Representation Detector for Hyperspectral Anomaly Detection
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DOI: 10.1109/jstars.2022.3182538
Abstract: Due to the powerful reconstruction ability, deep learning based hyperspectral anomaly detection methods have been prevalent in recent years. However, the capability of neural networks and the meaning of latent space remains unexplainable to some… read more here.
Keywords: normalizing flow; hyperspectral anomaly; detection; anomaly detection ... See more keywords
Subfeature Ensemble-Based Hyperspectral Anomaly Detection Algorithm
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3191725
Abstract: Hyperspectral images (HSIs) have always played an important role in remote sensing applications. Anomaly detection has become a hot spot in HSI processing in recent years. The popular detecting method is to accurately segment anomalies… read more here.
Keywords: hyperspectral anomaly; detection algorithm; subfeature; detection ... See more keywords
Learning-Free Hyperspectral Anomaly Detection With Unpredictive Frequency Residual Priors
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DOI: 10.1109/jstars.2022.3194203
Abstract: Hyperspectral anomaly detection aims to fast and credibly find nontrivial candidate targets without prior knowledge, which has become an increasingly pressing need as imagery swath and resolution are growing rapidly. Relevant state-of-the-art learning-based anomaly detection… read more here.
Keywords: hyperspectral anomaly; frequency; learning free; detection ... See more keywords
Dynamic Negative Sampling Autoencoder for Hyperspectral Anomaly Detection
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DOI: 10.1109/jstars.2022.3220514
Abstract: Hyperspectral anomaly detection (HAD) aims at detecting the anomalies without any prerequisite information, which gains lots of attention in recent years. Most of existing detectors locate the anomalies by eliminating the background. The background is… read more here.
Keywords: negative sampling; hyperspectral anomaly; dynamic negative; anomaly detection ... See more keywords
Transferred CNN Based on Tensor for Hyperspectral Anomaly Detection
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DOI: 10.1109/lgrs.2019.2962582
Abstract: Nowadays, deep learning (DM) and tensor theory have become research hotspot in hyperspectral images (HSIs) processing. In this letter, transferred convolutional neural network (CNN) based on tensor (TCNNT) is proposed for hyperspectral anomaly detection (AD).… read more here.
Keywords: tensor; hyperspectral anomaly; anomaly detection; cnn based ... See more keywords
Hyperspectral Anomaly Detection Based on Low-Rank Representation Using Local Outlier Factor
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DOI: 10.1109/lgrs.2020.2994745
Abstract: In recent years, low-rank representation (LRR) has attracted considerable attention in the field of hyperspectral anomaly detection. The main objective of LRR-based methods is to extract anomalies from the complex background. However, the presence of… read more here.
Keywords: rank representation; low rank; detection based; hyperspectral anomaly ... See more keywords
Hyperspectral Anomaly Detection via Integration of Feature Extraction and Background Purification
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.2998809
Abstract: Anomaly detection (AD) has become a hotspot in hyperspectral imagery (HSI) processing due to its advantage in detecting potential targets without prior knowledge, and a variety of algorithms are proposed for a better performance. However,… read more here.
Keywords: detection; anomaly detection; integration feature; detection via ... See more keywords