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To the best of our knowledge, this is the first time that salient objects are detected based on extracting explicit material property embedded in the spectral responses via retrieval of endmembers and estimating their abundance.

Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 150 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.

Hyperspectral unmixing is one of the most important techniques in remote sensing image analysis. In recent years, nonnegative matrix factorization (NMF) method is widely used in hyperspectral unmixing. In order to solve the nonconvex problem of NMF method, a number of constraints have been introduced into NMF models, including sparsity, manifold, smoothness, et al. However, these constraints ignore an important property of hyperspectral image, i.e., the spectral responses in a homogeneous region are similar at each pixel but vary in different homogeneous regions. In this paper. We introduce a novel region based structure preserving NMF (R-NMF) to explore consistent data distribution in the same region while discriminating different data structures across regions in the unmixed data. In this method, a graph cut algorithm is first applied to segment the hyperspectral image to small homogeneous regions. Then two constraints are applied to the unmixing model, which preserve the structural consistency within region while discriminating the differences between regions. Results on both synthetic and real data have validated the effectiveness of this method, and shown that it has outperformed several state-of-theart unmixing approaches.

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Hyperspectral unmixing is an important technique for estimating fractions of various materials from remote sensing imagery. Most unmixing methods make the assumption that no prior knowledge of endmembers is available before the estimation. This is, however, not true for some unmixing tasks for which part of the endmember signatures may be known in advance. In this paper, we address the hyperspectral unmixing problem with partially known endmembers. We extend nonnegative matrix factorization (NMF) based unmixing algorithms to incorporate prior information into their models. The proposed approach uses the spectral signature of known endmembers as a constraint, among others, in the unmixing model, and propagate the knowledge by an optimization process which minimizes the difference between image data and the prior knowledge. Results on both synthetic and real data have validated the effectiveness of the proposed method, and shown that it has outperformed several state-of-the-art methods that use or do not use prior knowledge of endmembers.

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