Supervised Distance-Based Feature Selection for Hyperspectral Target Detection

Rad, Amir Moeini; Abkar, Ali; Mojaradi, Barat

doi:10.3390/rs11172049

Cited by 4 publications

(3 citation statements)

References 57 publications

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“…An increasing number of application fields now rely on HSI, for instance, urban planning, geological exploration, military surveillance and precision agriculture [2]. Among these application fields, many critical tasks of computer vision processing such as unmixing [3], target detection [4], and classification [5] demand extremely high data quality. However, during sampling and acquisition, HSI is constantly degraded by mixed noises, for example, Gaussian noise, impulse noise, stripes and deadlines [6].…”

Section: Introductionmentioning

confidence: 99%

SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image

Sun

Zheng

et al. 2020

Remote Sensing

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During the process of signal sampling and digital imaging, hyperspectral images (HSI) inevitably suffer from the contamination of mixed noises. The fidelity and efficiency of subsequent applications are considerably reduced along with this degradation. Recently, as a formidable implement for image processing, low-rank regularization has been widely extended to the restoration of HSI. Meanwhile, further exploration of the non-local self-similarity of low-rank images are proven useful in exploiting the spatial redundancy of HSI. Better preservation of spatial-spectral features is achieved under both low-rank and non-local regularizations. However, existing methods generally regularize the original space of HSI, the exploration of the intrinsic properties in subspace, which leads to better denoising performance, is relatively rare. To address these challenges, a joint method of subspace low-rank learning and non-local 4-d transform filtering, named SLRL4D, is put forward for HSI restoration. Technically, the original HSI is projected into a low-dimensional subspace. Then, both spectral and spatial correlations are explored simultaneously by imposing low-rank learning and non-local 4-d transform filtering on the subspace. The alternating direction method of multipliers-based algorithm is designed to solve the formulated convex signal-noise isolation problem. Finally, experiments on multiple datasets are conducted to illustrate the accuracy and efficiency of SLRL4D.

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Section: Introductionmentioning

confidence: 99%

SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image

Sun

Zheng

et al. 2020

Remote Sensing

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“…Its rich information opens the possibility to differentiate several objects of interest based on their spectral signatures. Therefore, it is widely used in many remote-sensing research fields, such as spectral unmixing [2][3][4], target detection [5][6][7], and classification [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Anomaly Detection Algorithm Based on Morphological Profile and Attribute Filter with Band Selection and Automatic Determination of Maximum Area

2020

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Anomaly detection is one of the most challenging topics in hyperspectral imaging due to the high spectral resolution of the images and the lack of spatial and spectral information about the anomaly. In this paper, a novel hyperspectral anomaly detection method called morphological profile and attribute filter (MPAF) algorithm is proposed. Aiming to increase the detection accuracy and reduce computing time, it consists of three steps. First, select a band containing rich information for anomaly detection using a novel band selection algorithm based on entropy and histogram counts. Second, remove the background of the selected band with morphological profile. Third, filter the false anomalous pixels with attribute filter. A novel algorithm is also proposed in this paper to define the maximum area of anomalous objects. Experiments were run on real hyperspectral datasets to evaluate the performance, and analysis was also conducted to verify the contribution of each step of MPAF. The results show that the performance of MPAF yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), precision-recall, and computing time, i.e., 0.9916, 0.7055, and 0.25 seconds, respectively. Compared with four other anomaly detection algorithms, MPAF yielded the highest average AUC for ROC and precision-recall in eight out of thirteen and nine out of thirteen datasets, respectively. Further analysis also proved that each step of MPAF has its effectiveness in the detection performance.

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“…Hyperspectral remote sensing can obtain a great deal of information about an object via hundreds of narrow, continuous spectral bands. Hyperspectral imaging techniques have been widely used in many applications, such as landmine detection [1], agricultural monitoring [2], land cover classification [3], and target detection [4]. Many of these applications are based on hyperspectral image (HSI) classification at the pixel level.…”

Section: Introductionmentioning

confidence: 99%

Active Semi-Supervised Random Forest for Hyperspectral Image Classification

Zhang

Cao

et al. 2019

Remote Sensing

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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.Random forest, proposed by Breiman [16], is one of the most important machine learning methods. Compared with other machine learning methods, RF has the following advantages [9][10][11][17][18][19][20][21][22][23][24]. First, RF is suitable for high-dimensional data, which can alleviate the influence of curse of dimensionality [20]. Second, RF is very fast because it is implemented in parallel [9]. Third, RF is effective to handle data with imbalanced class labels or missing values [21]. Fourth, RF is not easy to fall into overfitting compared with the Boosting method [18]. Moreover, RF measures the importance of variables automatically [25]. Finally, RF can obtain a higher classification accuracy compared to other well-known classifiers such as SVM [5,6] and maximum likelihood (ML) [17,23], with fewer parameters.Active learning is a kind of iterative method that queries the most informative samples for manual labeling at each iteration [14]. Initially, the data set consists of two parts: a small number of labeled samples and a large number of unlabeled samples. At each iteration, a query function is adopted to investigate the unlabeled samples and select the most informative samples for manual labeling. After multiple iterations, the performance of the classifier improves along with the increase in the number of manually labeled samples. The query function plays an important role in determining the samples selected for manual labeling, which directly affects the classification performance [15].Semi-supervised learning, another way to tackle the issue of limited samples, trains a classifier with the labeled and unlabeled samples together, which does not r...

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Supervised Distance-Based Feature Selection for Hyperspectral Target Detection

Cited by 4 publications

References 57 publications

SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image

SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image

A Hyperspectral Anomaly Detection Algorithm Based on Morphological Profile and Attribute Filter with Band Selection and Automatic Determination of Maximum Area

Active Semi-Supervised Random Forest for Hyperspectral Image Classification

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