Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

10.36227/techrxiv.14428880 ◽

2021 ◽

Author(s):

Xiangyu Song

Keyword(s):

Anomaly Detection ◽

Spatial Information ◽

Hyperspectral Image ◽

Gabor Filter ◽

Poor Performance ◽

Hyperspectral Data ◽

Relative Mass ◽

Anomaly Score ◽

Spectral Anomaly ◽

Isolation Forest

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods.

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Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

10.36227/techrxiv.14428880.v2 ◽

2021 ◽

Author(s):

Xiangyu Song ◽

Sunil Aryal ◽

Kai Ming Ting ◽

zhen Liu ◽

Bin He

Keyword(s):

Anomaly Detection ◽

Spatial Information ◽

Hyperspectral Image ◽

Gabor Filter ◽

Poor Performance ◽

Hyperspectral Data ◽

Relative Mass ◽

Anomaly Score ◽

Spectral Anomaly ◽

Isolation Forest

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods.

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Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

10.36227/techrxiv.14428880.v1 ◽

2021 ◽

Author(s):

Xiangyu Song

Keyword(s):

Anomaly Detection ◽

Spatial Information ◽

Hyperspectral Image ◽

Gabor Filter ◽

Poor Performance ◽

Hyperspectral Data ◽

Relative Mass ◽

Anomaly Score ◽

Spectral Anomaly ◽

Isolation Forest

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods.

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Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

Remote Sensing ◽

10.3390/rs13020268 ◽

2021 ◽

Vol 13 (2) ◽

pp. 268

Author(s):

Xiaochen Lv ◽

Wenhong Wang ◽

Hongfu Liu

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

Imbalanced Data ◽

Reconstruction Error ◽

Hyperspectral Data ◽

Weight Matrix ◽

Hyperspectral Images ◽

Mixed Data ◽

Sparsity Constraints ◽

Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

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An effective hyperspectral image retrieval method using integrated spectral and textural features

Sensor Review ◽

10.1108/sr-10-2014-0716 ◽

2015 ◽

Vol 35 (3) ◽

pp. 274-281 ◽

Cited By ~ 10

Author(s):

Zhenfeng Shao ◽

Weixun Zhou ◽

Qimin Cheng ◽

Chunyuan Diao ◽

Lei Zhang

Keyword(s):

Design Methodology ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Textural Features ◽

Washington Dc ◽

Query Image ◽

Retrieval Method ◽

Content Type ◽

Similar Images

Purpose – The purpose of this paper is to improve the retrieval results of hyperspectral image by integrating both spectral and textural features. For this purpose, an improved multiscale opponent representation for hyperspectral texture is proposed to represent the spatial information of the hyperspectral scene. Design/methodology/approach – In the presented approach, end-member signatures are extracted as spectral features by means of the widely used end-member induction algorithm N-FINDR, and the improved multiscale opponent representation is extracted from the first three principal components of the hyperspectral data based on Gabor filters. Then, the combination similarity between query image and other images in the database is calculated, and the first k more similar images are returned in descending order of the combination similarity. Findings – Some experiments are calculated using the airborne hyperspectral data of Washington DC Mall. According to the experimental results, the proposed method improves the retrieval results, especially for image categories that have regular textural structures. Originality/value – The paper presents an effective retrieval method for hyperspectral images.

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Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior

Remote Sensing ◽

10.3390/rs11242897 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2897 ◽

Cited By ~ 3

Author(s):

Yuhui Zheng ◽

Feiyang Wu ◽

Hiuk Jae Shim ◽

Le Sun

Keyword(s):

Mixed Model ◽

Linear Mixed Model ◽

Spatial Information ◽

Hyperspectral Image ◽

Simulated Data ◽

Hyperspectral Data ◽

Low Rank ◽

Data Sets ◽

Self Similarity ◽

Model Framework

Hyperspectral unmixing is a key preprocessing technique for hyperspectral image analysis. To further improve the unmixing performance, in this paper, a nonlocal low-rank prior associated with spatial smoothness and spectral collaborative sparsity are integrated together for unmixing the hyperspectral data. The proposed method is based on a fact that hyperspectral images have self-similarity in nonlocal sense and smoothness in local sense. To explore the spatial self-similarity, nonlocal cubic patches are grouped together to compose a low-rank matrix. Then, based on the linear mixed model framework, the nuclear norm is constrained to the abundance matrix of these similar patches to enforce low-rank property. In addition, the local spatial information and spectral characteristic are also taken into account by introducing TV regularization and collaborative sparse terms, respectively. Finally, the results of the experiments on two simulated data sets and two real data sets show that the proposed algorithm produces better performance than other state-of-the-art algorithms.

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JL-GFDN: A Novel Gabor Filter-Based Deep Network Using Joint Spectral-Spatial Local Binary Pattern for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs12122016 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2016 ◽

Cited By ~ 2

Author(s):

Tao Zhang ◽

Puzhao Zhang ◽

Weilin Zhong ◽

Zhen Yang ◽

Fan Yang

Keyword(s):

Local Binary Pattern ◽

Hyperspectral Image ◽

Gabor Filter ◽

Spectral Characteristics ◽

Three Dimensional ◽

Real Data ◽

Hyperspectral Data ◽

Data Cube ◽

Data Sets ◽

Deep Network

The traditional local binary pattern (LBP, hereinafter we also call it a two-dimensional local binary pattern 2D-LBP) is unable to depict the spectral characteristics of a hyperspectral image (HSI). To cure this deficiency, this paper develops a joint spectral-spatial 2D-LBP feature (J2D-LBP) by averaging three different 2D-LBP features in a three-dimensional hyperspectral data cube. Subsequently, J2D-LBP is added into the Gabor filter-based deep network (GFDN), and then a novel classification method JL-GFDN is proposed. Different from the original GFDN framework, JL-GFDN further fuses the spectral and spatial features together for HSI classification. Three real data sets are adopted to evaluate the effectiveness of JL-GFDN, and the experimental results verify that (i) JL-GFDN has a better classification accuracy than the original GFDN; (ii) J2D-LBP is more effective in HSI classification in comparison with the traditional 2D-LBP.

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Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs

Remote Sensing ◽

10.3390/rs10081271 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1271 ◽

Cited By ~ 14

Author(s):

Feng Gao ◽

Qun Wang ◽

Junyu Dong ◽

Qizhi Xu

Keyword(s):

Image Classification ◽

Spatial Information ◽

Hyperspectral Image ◽

Small Sample ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

High Dimensional ◽

Hyperspectral Image Classification ◽

Spatial Classification ◽

Spatial Features

Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.

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Active Semi-Supervised Random Forest for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs11242974 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2974 ◽

Cited By ~ 3

Author(s):

Youqiang Zhang ◽

Guo Cao ◽

Xuesong Li ◽

Bisheng Wang ◽

Peng Fu

Keyword(s):

Random Forest ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Data Sets ◽

Great Success ◽

Full Potential ◽

Hyperspectral Image Classification ◽

Unified Framework ◽

Proposed Model

Random forest (RF) has obtained great success in hyperspectral image (HSI) classification. However, RF cannot leverage its full potential in the case of limited labeled samples. To address this issue, we propose a unified framework that embeds active learning (AL) and semi-supervised learning (SSL) into RF (ASSRF). Our aim is to utilize AL and SSL simultaneously to improve the performance of RF. The objective of the proposed method is to use a small number of manually labeled samples to train classifiers with relative high classification accuracy. To achieve this goal, a new query function is designed to query the most informative samples for manual labeling, and a new pseudolabeling strategy is introduced to select some samples for pseudolabeling. Compared with other AL- and SSL-based methods, the proposed method has several advantages. First, ASSRF utilizes the spatial information to construct a query function for AL, which can select more informative samples. Second, in addition to providing more labeled samples for SSL, the proposed pseudolabeling method avoids bias caused by AL-labeled samples. Finally, the proposed model retains the advantages of RF. To demonstrate the effectiveness of ASSRF, we conducted experiments on three real hyperspectral data sets. The experimental results have shown that our proposed method outperforms other state-of-the-art methods.

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A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended Multi-Attribute Profile

Remote Sensing ◽

10.3390/rs13193954 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3954

Author(s):

Senhao Liu ◽

Lifu Zhang ◽

Yi Cen ◽

Likun Chen ◽

Yibo Wang

Keyword(s):

Anomaly Detection ◽

Spatial Information ◽

Hyperspectral Image ◽

Detection Algorithm ◽

Operational Efficiency ◽

Low Rank ◽

Spectral Information ◽

False Alarms ◽

Diverse Application ◽

Similar Materials

To address the difficulty of separating background materials from similar materials associated with the use of “single-spectral information” for hyperspectral anomaly detection, a fast hyperspectral anomaly detection algorithm based on what we term the “greedy bilateral smoothing and extended multi-attribute profile” (GBSAED) method is proposed to improve detection precision and operation efficiency. This method utilizes “greedy bilateral smoothing” to decompose the low-rank part of a hyperspectral image (HSI) dataset and calculate spectral anomalies. This process improves the operational efficiency. Then, the extended multi-attribute profile is used to extract spatial anomalies and restrict the shape of anomalies. Finally, the two components are combined to limit false alarms and obtain appropriate detection results. This new method considers both spectral and spatial information with an improved structure that ensures operational efficiency. Using five real HSI datasets, this study demonstrates that the GBSAED method is more robust than eight representative algorithms under diverse application scenarios and greatly improves detection precision and operational efficiency.

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