E2E-LIADE: End-to-End Local Invariant Autoencoding Density Estimation Model for Anomaly Target Detection in Hyperspectral Image

Hyperspectral anomaly target detection (also known as hyperspectral anomaly detection (HAD)] is a technique aiming to identify samples with atypical spectra. Although some density estimation-based methods have been developed, they may suffer from two issues: 1) separated two-stage optimization with inconsistent objective functions makes the representation learning model fail to dig out characterization customized for HAD and 2) incapability of learning a low-dimensional representation that preserves the inherent information from the original high-dimensional spectral space. To address these problems, we propose a novel end-to-end local invariant autoencoding density estimation (E2E-LIADE) model. To satisfy the assumption on the manifold, the E2E-LIADE introduces a local invariant autoencoder (LIA) to capture the intrinsic low-dimensional manifold embedded in the original space. Augmented low-dimensional representation (ALDR) can be generated by concatenating the local invariant constrained by a graph regularizer and the reconstruction error. In particular, an end-to-end (E2E) multidistance measure, including mean-squared error (MSE) and orthogonal projection divergence (OPD), is imposed on the LIA with respect to hyperspectral data. More important, E2E-LIADE simultaneously optimizes the ALDR of the LIA and a density estimation network in an E2E manner to avoid the model being trapped in a local optimum, resulting in an energy map in which each pixel represents a negative log likelihood for the spectrum. Finally, a postprocessing procedure is conducted on the energy map to suppress the background. The experimental results demonstrate that compared to the state of the art, the proposed E2E-LIADE offers more satisfactory performance.

[1]  Weiying Xie,et al.  Spectral Adversarial Feature Learning for Anomaly Detection in Hyperspectral Imagery , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Ran Tao,et al.  Low-Rank and Sparse Decomposition With Mixture of Gaussian for Hyperspectral Anomaly Detection , 2020, IEEE Transactions on Cybernetics.

[3]  Weiying Xie,et al.  Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Bo Du,et al.  Dimensionality Reduction With Enhanced Hybrid-Graph Discriminant Learning for Hyperspectral Image Classification , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Ping Zhong,et al.  Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Xiuping Jia,et al.  Spectral constraint adversarial autoencoders approach to feature representation in hyperspectral anomaly detection , 2019, Neural Networks.

[7]  Bo Du,et al.  Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image , 2019, IEEE Transactions on Cybernetics.

[8]  Jon Atli Benediktsson,et al.  Deep Learning for Hyperspectral Image Classification: An Overview , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Raghavendra Chalapathy University of Sydney,et al.  Deep Learning for Anomaly Detection: A Survey , 2019, ArXiv.

[10]  Baozhi Cheng,et al.  A stacked autoencoders-based adaptive subspace model for hyperspectral anomaly detection , 2019, Infrared Physics & Technology.

[11]  Haibo He,et al.  Dimensionality Reduction of Hyperspectral Imagery Based on Spatial–Spectral Manifold Learning , 2018, IEEE Transactions on Cybernetics.

[12]  Changzhe Jiao,et al.  Discriminative Multiple Instance Hyperspectral Target Characterization , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Hongwei Liu,et al.  A Review of the Autoencoder and Its Variants: A Comparative Perspective from Target Recognition in Synthetic-Aperture Radar Images , 2018, IEEE Geoscience and Remote Sensing Magazine.

[14]  Wei Li,et al.  Diverse Region-Based CNN for Hyperspectral Image Classification , 2018, IEEE Transactions on Image Processing.

[15]  Ning Ma,et al.  An Unsupervised Deep Hyperspectral Anomaly Detector , 2018, Sensors.

[16]  Naoto Yokoya,et al.  Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art , 2017, IEEE Geoscience and Remote Sensing Magazine.

[17]  Kenli Li,et al.  Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Wei Li,et al.  Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery , 2017, IEEE Geoscience and Remote Sensing Letters.

[19]  Yuan Yuan,et al.  Hyperspectral Anomaly Detection by Graph Pixel Selection , 2016, IEEE Transactions on Cybernetics.

[20]  Liang-pei Zhang,et al.  A robust background regression based score estimation algorithm for hyperspectral anomaly detection , 2016 .

[21]  Qian Du,et al.  A survey on representation-based classification and detection in hyperspectral remote sensing imagery , 2016, Pattern Recognit. Lett..

[22]  Antonio J. Plaza,et al.  Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Bo Du,et al.  A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Li Ma,et al.  Hyperspectral Anomaly Detection by the Use of Background Joint Sparse Representation , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Qian Du,et al.  Collaborative Representation for Hyperspectral Anomaly Detection , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Quoc V. Le,et al.  Stochastic Gradient Descent , 2014, Machine Learning with Neural Networks.

[27]  Hans-Peter Kriegel,et al.  A survey on unsupervised outlier detection in high‐dimensional numerical data , 2012, Stat. Anal. Data Min..

[28]  Barnabás Póczos,et al.  Group Anomaly Detection using Flexible Genre Models , 2011, NIPS.

[29]  Peter A. Flach,et al.  A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance , 2011, ICML.

[30]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  S Matteoli,et al.  A tutorial overview of anomaly detection in hyperspectral images , 2010, IEEE Aerospace and Electronic Systems Magazine.

[32]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[33]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[34]  Mark J. Carlotto,et al.  A cluster-based approach for detecting man-made objects and changes in imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[35]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[36]  Xiaoli Yu,et al.  Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..

[37]  J. Tukey,et al.  Variations of Box Plots , 1978 .

[38]  Tiziana Veracini,et al.  A Locally Adaptive Background Density Estimator: An Evolution for RX-Based Anomaly Detectors , 2014, IEEE Geoscience and Remote Sensing Letters.

[39]  Charles Elkan,et al.  Expectation Maximization Algorithm , 2010, Encyclopedia of Machine Learning.