Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification

Tong, Fei; Tong, Hengjian; Jiang, Junjun; Zhang, Yun

doi:10.3390/rs9090872

Cited by 27 publications

(11 citation statements)

References 44 publications

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“…Although the resulting images look realistic, the compensated high-frequency details such as image edges may cause inconsistency with the high-resolution ground truth [22]. Some works show that this issue negatively impacts land cover classification results [23,24]. Edge information is an important feature for object detection [25], and therefore, this information is needed to be preserved in the enhanced images to get good detection accuracy.…”

Section: Problem Description and Motivationmentioning

confidence: 99%

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Rabbi

Ray

Schubert³

et al. 2020

Preprint

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The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.

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Section: Problem Description and Motivationmentioning

confidence: 99%

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Rabbi

Ray

Schubert³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The class label with the minimal representative error will be assigned to the unlabeled samples. Some researchers extended the SRC-based methods to utilize spatial information in hyperspectral images, e.g., joint SRC [27,28], kernel SRC [29][30][31], adaptive SRC [32,33], etc. In addition, there was also some work that used multi-objective optimization to improve SRC directly [34,35].…”

Section: Introductionmentioning

confidence: 99%

A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification

Cui

et al. 2020

Remote Sensing

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Hyperspectral image classification methods may not achieve good performance when a limited number of training samples are provided. However, labeling sufficient samples of hyperspectral images to achieve adequate training is quite expensive and difficult. In this paper, we propose a novel sample pseudo-labeling method based on sparse representation (SRSPL) for hyperspectral image classification, in which sparse representation is used to select the purest samples to extend the training set. The proposed method consists of the following three steps. First, intrinsic image decomposition is used to obtain the reflectance components of hyperspectral images. Second, hyperspectral pixels are sparsely represented using an overcomplete dictionary composed of all training samples. Finally, information entropy is defined for the vectorized sparse representation, and then the pixels with low information entropy are selected as pseudo-labeled samples to augment the training set. The quality of the generated pseudo-labeled samples is evaluated based on classification accuracy, i.e., overall accuracy, average accuracy, and Kappa coefficient. Experimental results on four real hyperspectral data sets demonstrate excellent classification performance using the new added pseudo-labeled samples, which indicates that the generated samples are of high confidence.

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“…Multiscale based methods have also been applied in hyperspectral images processing [30][31][32][33][34][35]. Li et al [36] proposed a multiscale spatial information fusion (MSIF) method for hyperspectral image classification.…”

Section: Introductionmentioning

confidence: 99%

Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

Lei

Song

et al. 2019

Remote Sensing

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Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. In available tensor- and low rank-based methods, how to construct appropriate tensor samples and determine the optimal rank of hyperspectral images along each mode are still challenging issues. To address these drawbacks, an unsupervised tensor-based multiscale low rank decomposition (T-MLRD) method for hyperspectral images dimensionality reduction is proposed in this paper. By regarding the raw cube hyperspectral image as the only tensor sample, T-MLRD needs no labeled samples and avoids the processing of constructing tensor samples. In addition, a novel multiscale low rank estimating method is proposed to obtain the optimal rank along each mode of hyperspectral image which avoids the complicated rank computing. Finally, the multiscale low rank feature representation is fused to achieve dimensionality reduction. Experimental results on real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches.

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Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification

Cited by 27 publications

References 44 publications

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification

Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

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