ALBERT

Description: Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to directly estimate the clear images from the stripe images without considering the intrinsic properties of stripe noise, which causes the image structure destroyed. In this paper, we propose a new destriping method from the perspective of image decomposition, which takes the intrinsic properties of stripe noise and image characteristics into full consideration. The proposed method integrates the unidirectional total variation (TV) regularization, group sparsity regularization, and TV regularization together in an image decomposition framework. The first two terms are utilized to exploit the stripe noise properties by implementing statistical analysis, and the TV regularization is adopted to explore the spatial piecewise smooth structure of stripe-free image. Moreover, an efficient alternating minimization scheme is designed to solve the proposed model. Extensive experiments on simulated and real data demonstrate that our method outperforms several existing state-of-the-art destriping methods in terms of both quantitative and qualitative assessments.

Description: Remote Sensing, Vol. 10, Pages 622: A Framelet-Based Iterative Pan-Sharpening Approach Remote Sensing doi: 10.3390/rs10040622 Authors: Zi-Yao Zhang Ting-Zhu Huang Liang-Jian Deng Jie Huang Xi-Le Zhao Chao-Chao Zheng Pan-sharpening is used to fuse multispectral images and panchromatic images to produce a multispectral image with high spatial resolution. In this paper, we design a new iterative method based on framelet for pan-sharpening. The proposed model takes advantage of the upsampled multispectral image and a linear relation between the panchromatic image and the latent high-resolution multispectral image. Since the sparsity of the pan-sharpened image under a B-spline framelet transform is assumed, we regularize the model by penalizing l 1 norm of a framelet based term. The model is solved by a designed algorithm based on alternating direction method of multipliers (ADMM). For better performance, we propose an iterative strategy to pick up more spectral and spatial details. Experiments on four datasets demonstrate that the proposed method outperforms several existing pan-sharpening methods.

Description: Remote Sensing, Vol. 10, Pages 1046: Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing Remote Sensing doi: 10.3390/rs10071046 Authors: Si Wang Ting-Zhu Huang Xi-Le Zhao Gang Liu Yougan Cheng Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images. Plenty of sparse unmixing methods have been proposed in the literature that achieved promising performance. However, many of these methods overlook the latent geometrical structure of the hyperspectral data which limit their performance to some extent. To address this issue, a double reweighted sparse and graph regularized unmixing method is proposed in this paper. Specifically, a graph regularizer is employed to capture the correlation information between abundance vectors, which makes use of the property that similar pixels in a spectral neighborhood have higher probability to share similar abundances. In this way, the latent geometrical structure of the hyperspectral data can be transferred to the abundance space. In addition, a double weighted sparse regularizer is used to enhance the sparsity of endmembers and the fractional abundance maps, where one weight is introduced to promote the sparsity of endmembers as a hyperspectral image typically contains fewer endmembers compared to the overcomplete spectral library and the other weight is exploited to improve the sparsity of the abundance matrix. The weights of the double weighted sparse regularizer used for the next iteration are adaptively computed from the current abundance matrix. The experimental results on synthetic and real hyperspectral data demonstrate the superiority of our method compared with some state-of-the-art approaches.

Description: Remote Sensing, Vol. 10, Pages 116: Multispectral Image Denoising via Nonlocal Multitask Sparse Learning Remote Sensing doi: 10.3390/rs10010116 Authors: Ya-Ru Fan Ting-Zhu Huang Xi-Le Zhao Liang-Jian Deng Shanxiong Fan The goal of multispectral imaging is to obtain the spectrum for each pixel in the image of a scene and deliver much reliable information. It has been widely applied to several fields including mineralogy, oceanography and astronomy. However, multispectral images (MSIs) are often corrupted by various noises. In this paper, we propose a MSI denoising model based on nonlocal multitask sparse learning. The nonlocal self-similarity across space and the high correlation of the MSI along the spectrum via multitask sparse learning are fully exploited in the proposed model. A nonnegative matrix factorization (NMF) based algorithm is developed to solve the proposed model. Experimental results on both simulated and real data demonstrate that the proposed method performs better than several existing state-of-the-art denoising methods.

Description: Remote Sensing, Vol. 10, Pages 361: Directional ℓ0 Sparse Modeling for Image Stripe Noise Removal Remote Sensing doi: 10.3390/rs10030361 Authors: Hong-Xia Dou Ting-Zhu Huang Liang-Jian Deng Xi-Le Zhao Jie Huang Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to enhance the visual quality of images, while preserving image details of stripe-free regions. Instead of solving the underlying image by variety of algorithms, we first estimate the stripe noise from the degraded images, then compute the final destriping image by the difference of the known stripe image and the estimated stripe noise. In this paper, we propose a non-convex ℓ 0 sparse model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise, and the locally continuous property of the underlying image as well. Moreover, the proposed non-convex model is solved by a proximal alternating direction method of multipliers (PADMM) based algorithm. In addition, we also give the corresponding theoretical analysis of the proposed algorithm. Extensive experimental results on simulated and real data demonstrate that the proposed method outperforms recent competitive destriping methods, both visually and quantitatively.