Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection

Zhang, Yuxiang; Du, Bo; Zhang, Liangpei; Liu, Tongliang

doi:10.1109/tgrs.2016.2616649

Cited by 94 publications

(60 citation statements)

References 41 publications

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“…For a hyperspectral data, the EMAP dataset was first calculated. Then multiple tasks were constructed through a band cross-grouping strategy [19]. Specifically, each sub-EMAP is generated from the original EMAP according to the band order at equal intervals.…”

Section: Framework Of the Mtjslr-emap Detectormentioning

confidence: 99%

“…To eliminate redundancies of hyper-dimensional data while keeping as much information as possible [16], dimensionality reduction (DR) has been applied in target detection [17,18]. However, it is still critical to preserving all informative subspace in the DR process [19]. There exists a dilemma to reduce the redundancy without loss of information.…”

Section: Introductionmentioning

confidence: 99%

“…Since filters with slightly different parameters may produce similar profiles [24], there exists redundancy in EMAP. Fortunately, multi-task learning (MTL) technique has been applied in hyperspectral target detection to tackle the high-dimensional redundancy [19,25]. MTL is an inductive transfer method that improves generalization by using shared information across all related tasks [26].…”

Section: Introductionmentioning

confidence: 99%

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Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection

Zhang

Wang

et al. 2019

Remote Sensing

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Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use all bands information and ignore the inter-band redundancy. Moreover, they do not make full use of the difference between the background and target samples. To alleviate these problems, we proposed a novel joint sparse and low-rank multi-task learning (MTL) with extended multi-attribute profile (EMAP) algorithm (MTJSLR-EMAP). Briefly, the spatial features of HSI were first extracted by morphological attribute filters. Then the MTL was exploited to reduce band redundancy and retain the discriminative information simultaneously. Considering the distribution difference between the background and target samples, the target and background pixels were separately modeled with different regularization terms. In each task, a background pixel can be low-rank represented by the background samples while a target pixel can be sparsely represented by the target samples. Finally, the proposed algorithm was compared with six detectors including constrained energy minimization (CEM), adaptive coherence estimator (ACE), hierarchical CEM (hCEM), sparsity-based detector (STD), joint sparse representation and MTL detector (JSR-MTL), independent encoding JSR-MTL (IEJSR-MTL) on three datasets. Corresponding to each competitor, it has the average detection performance improvement of about 19.94%, 22.53%, 16.92%, 14.87%, 14.73%, 4.21% respectively. Extensive experimental results demonstrated that MTJSLR-EMAP outperforms several state-of-the-art algorithms.

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Section: Framework Of the Mtjslr-emap Detectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection

Zhang

Wang

et al. 2019

Remote Sensing

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“…This detailed spectral and spatial information increases the discriminative ability of HSIs compared to conventional colour images or multi-spectral images. As a result, hyperspectral imaging has been used in a wide range of applications including classification [1]- [3], object tracking [4]- [6], environmental monitoring [7], [8] and object detection [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification

Sellars

Avilés-Rivero

Schönlieb

2020

IEEE Trans. Geosci. Remote Sensing

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A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data. In this paper we present a novel graph-based framework, which aims to tackle this problem in the presence of large scale data input. Our approach utilises a novel superpixel method, specifically designed for hyperspectral data, to define meaningful local regions in an image, which with high probability share the same classification label. We then extract spectral and spatial features from these regions and use these to produce a contracted weighted graph-representation, where each node represents a region rather than a pixel. Our graph is then fed into a graph-based semi-supervised classifier which gives the final classification. We show that using superpixels in a graph representation is an effective tool for speeding up graphical classifiers applied to hyperspectral images. We demonstrate through exhaustive quantitative and qualitative results that our proposed method produces accurate classifications when an incredibly small amount of labelled data is used. We show that our approach mitigates the major drawbacks of existing approaches, resulting in our approach outperforming several comparative state-of-theart techniques.

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“…Typical supervised classifiers include the support vector machine (SVM) [12,13], artificial neural networks (ANN) [14] and sparse representation-based classification (SRC) [15,16], etc. SVM is a kind of kernel-based method that aims at exploring the optimal separating hyperplane between different classes, ANN is motivated by the biological learning process of human brain, while SRC stems from the rapid development of compressed sensing in recent years.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

He¹,

Liu²,

Wang³

et al. 2017

Remote Sensing

147

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Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs). Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF) is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator) trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method, especially with a limited number of labeled samples.

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Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection

Cited by 94 publications

References 41 publications

Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection

Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection

Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

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