Hyperspectral Anomaly Detection Using Dual Window Density

Tu, Bing; Yang, Xianchang; Zhou, Chengle; He, Danbing

doi:10.1109/tgrs.2020.2988385

Cited by 38 publications

(21 citation statements)

References 49 publications

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“…Gaussian kernel (5) where d c is the cut-off distance measuring the radius of the search region. Unlike the cut-off kernel, the Gaussian kernel can decrease the negative impact of the statistical errors introduced by the limited availability of samples.…”

Section: Density Peak Clustering (Dpc)mentioning

confidence: 99%

“…L AND use and land cover classification using different kinds of Earth observation (EO) data, e.g., hyperspectral images (HSIs) [1], synthetic aperture radar (SAR) [2], light detection and ranging (LiDAR) [3], and others [4], is a challenging task in geoscience and remote sensing. Since HSIs can provide a wealth of spectral information about the physical properties of the observed materials, it is now widely brought into focus in terms of data processing, such as anomaly detection [5], spectral unmixing [6], dimensionality reduction [7], and image classification [8], and used in plentiful application fields, including national defense [9], precision agriculture [10]- [12], and environment monitoring [13], [14].…”

Section: Introductionmentioning

confidence: 99%

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Structure-Aware Multikernel Learning for Hyperspectral Image Classification

Zhou

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, the inclusion of spatial information has drawn increasing attention in hyperspectral image (HSI) applications due to its effectiveness in terms of improving classification accuracy. However, most of the techniques that include such spatial knowledge in the analysis are based on spatial-spectral weak assumptions, i.e., all pixels in a spatial region are assumed to belong to the same class and close pixels in spectral space are assigned the same label. This paper proposes a novel structure-aware multi-kernel learning (SaMKL) method for HSI classification, which takes into account structural issues in order to effectively overcome the aforementioned weak assumptions and introduce a true multi-kernel learning process (based on multiple features derived from the original HSI), thus improving the spectral separability of such features. The proposed SaMKL method is composed of the following main steps. First, multiple (i.e., spectral, spatial, and textural) features are extracted from the original HSI based on various filtering operators. Then, a k-peak density approach is designed to define superpixel regions that can properly capture the structural information of HSIs and overcome the above-mentioned weak assumptions. Next, three sets of composite kernels are separately constructed to make full use of the spectral, spatial, and textural information. Meanwhile, these three sets of composited kernels are independently incorporated into a support vector machine classifier to obtain their corresponding classification results. Finally, majority voting is used as a simple and effective method to obtain the final classification labels. Experimental results on real HSI datasets indicate that the SaMKL outperforms other well-known and state-of-the-art classification approaches, in particular, when very limited labeled samples are available a priori.

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Section: Density Peak Clustering (Dpc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structure-Aware Multikernel Learning for Hyperspectral Image Classification

Zhou

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Each pixel in the hyperspectral image (HSI) contains hundreds of continuous spectral channels, which correspond to the detailed spectrum of the reflected light. The abundant spectral information of HSI can be exploited to distinguish objects of various materials and thus has spawned many application research fields such as anomaly detection [2]- [4], spectral unmixing [5]- [7], semantic segmentation [8]- [10] and classification [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Hyperspectral Image Classification Using Robust Dual-Stage Spatial Embedding

Shan

et al. 2021

IEEE Access

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Recently, many spectral-special classification models have emerged one after another in the remote sensing community. These models aim to introduce the spatial information of the pixel to improve the accuracy of the class attribute of the pixel. However, for the spectral-spatial classification algorithms, not all pixels need to introduce the corresponding spatial information since the use of a large amount of spatial information has a costly time. To solve this problem, this paper proposes a robust dualstage spatial embedding (RDSSE) model for spectral-spatial classification of hyperspectral image, which is composed of the following several main steps: First, an over-segmentation algorithm is employed to cluster original hyperspectral image into many superpixel blocks with shape adaptive characteristics. Then, we design a k-peak criterion to fuse the spectral feature of pixels within and between superpixels. Next, a low time-consumption spectral classifier is introduced to conduct primary classification for a testing pixel to achieve the corresponding probability distribution. Specifically, the difference between the probability of the largest class and that of the second-largest class is served as class confidence. Finally, the predicted label of the low-confidence testing pixels is reclassified based on a high-accuracy spectral-spatial classification method. Experimental results on several real images illustrated that the proposed RDSSE can obtain superior performance with respect to several competitive methods.

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“…In other words, the wide spectral coverage and high spectral resolution of HSIs tremendously enhances peoples cognitive competence of ground objects. Due to these advantages, hyperspectral imaging technique plays a vital role in geological exploration, urban expansion, agricultural and forestry monitoring, military, and other industries [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification

Zheng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral images (HSIs) have fine spectral information and rich spatial information, of which the feature quality is one of the key factors that affect the classification performance. Therefore, how to extract essential features and eliminate redundant features from hyperspectral data is the main research focus of this paper. Here, we propose a spectral-spatial feature extraction method based on ensemble empirical mode decomposition (SFEEMD) for HSI classification, which contains several steps as follows: Firstly, the dimension reduction for HSI is performed by using principal component analysis method. Secondly, in order to decrease the sensitivity to noise and extract rough outline features, the adaptive total variation filtering (ATVF) is conducted on the selected principal components. Furthermore, by using the ensemble empirical mode decomposition (EEMD) to resolve each spectral band into sequence components, the features of HSIs can be better coalesced into the transform domain. Finally, the first K principal components of the input image and the outputs of the ATVF and EEMD are integrated into a stacking system to obtain the final feature image, which is then classified by a pixel-wise classifier. The experimental results of three authentic hyperspectral data sets show that the proposed algorithm obtains superior classification performance compared with other methods.

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Hyperspectral Anomaly Detection Using Dual Window Density

Cited by 38 publications

References 49 publications

Structure-Aware Multikernel Learning for Hyperspectral Image Classification

Structure-Aware Multikernel Learning for Hyperspectral Image Classification

Spectral-Spatial Hyperspectral Image Classification Using Robust Dual-Stage Spatial Embedding

Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification

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