This paper proposes a spatially constrained Bag-of-Visual-Words (BOV) method for hyperspectral image classification. We firstly extract the texture feature. The spectral and texture features are used as two types of low-level features, based on which, the high-level visual-words are constructed by the proposed method. We use the entropy rate superpixel segmentation method to segment the hyperspectral into patches that well keep the homogeneousness of regions. The patches are regarded as documents in BOV model. Then k-means clustering is implemented to cluster pixels to construct codebook. Finally, the BOV representation is constructed with the statistics of the occurrence of visual-words for each patch. Experiments on a real data show that the proposed method is comparable to several state of the art methods.

Building effective image representations from hyperspectral data helps to improve the performance for classification. In this letter, we develop a hierarchical discriminative feature learning algorithm for hyperspectral image classification, which is a deformation of the spatial-pyramid-matching model based on the sparse codes learned from the discriminative dictionary in each layer of a two-layer hierarchical scheme. The pooling features achieved by the proposed method are more robust and discriminative for the classification. We evaluate the proposed method on two hyperspectral data sets: Indiana Pines and Salinas scene. The results show our method possessing state-of-the-art classification accuracy.

Spectral-spatial representation based algorithms have been widely applied in hyperspectral image (HSI) classification, which exploit the fact that pixels in a local patch often have similar spectral reflectance values and probably belong to the same class. Collaborative representation (CR) is a typical supervised classification method for high-dimensional data, which has been widely used for spectral-spatial representation based HSI classification. However, it suffers from the degraded representation of redundant and irrelative pixels when all of the labeled pixels are used as a dictionary for representation. In this letter, a novel method, local CR with adaptive dictionary selection, is proposed to solve this problem, in which we first average the values of pixels from local patches to incorporate the contextual information of neighbors, and then, an adaptive dictionary selection method is presented to select the most similar pixels to each test pixel from the dictionary to reduce the influence of redundant and irrelevant pixels in representation. Experimental results on two HSIs show that the proposed method outperforms some spectral-spatial representation based algorithms in terms of classification accuracy.

Generalized bilinear model (GBM) has been widely used for nonlinear hyperspectral image unmixing. However, it does not take the sparse information of abundance into account, which is a significant characteristic resulting from the correlation of hyperspectral data. This paper aims to extend the GBM by incorporating the sparsity constraint of abundance matrix with the semi-nonnegative matrix factorization, by dividing GBM into the linear part and the second-order part, which are optimized using an alternating optimization algorithm respectively. L1/2-norm is used to explore the sparse characteristic, and the L1/2-constrained semi-nonnegative matrix factorization (L1/2-semi-NMF) algorithm is presented, which leads to better results on both synthetic and real data.

Due to the imbalance in obtaining labeled samples for different land-cover classes, hyperspectral image classification encounters the issue of imbalanced classification. In this paper, a novel and effective method is proposed to address the imbalanced learning problem in hyperspectral image classification, which combines support vector machine (SVM) and sampling strategy. The main novelty and contribution of our paper are that we propose to do sampling referring to the support vectors (SVs) rather than the training data to provide a balanced distribution during the model learning. Sampling among the training data may be time consuming, while sampling referring to the SVs is more efficient and representative with much lower complexity. Therefore, the proposed method is expected to be simple and effective for imbalanced learning problem. Experimental results on real hyperspectral image dataset show that our method can effectively improve the classification accuracy for the minority classes in the imbalanced dataset.

In this letter, a simple and effective unsupervised approach based on the combined difference image and$k$-means clustering is proposed for the synthetic aperture radar (SAR) image change detection task. First, we use one of the most popular denoising methods, the probabilistic-patch-based algorithm, for speckle noise reduction of the two multitemporal SAR images, and the subtraction operator and the log ratio operator are applied to generate two kinds of simple change maps. Then, the mean filter and the median filter are used to the two change maps, respectively, where the mean filter focuses on making the change map smooth and the local area consistent, and the median filter is used to preserve the edge information. Second, a simple combination framework which uses the maps obtained by the mean filter and the median filter is proposed to generate a better change map. Finally, the$k$-means clustering algorithm with$k = 2$is used to cluster it into two classes, changed area and unchanged area. Local consistency and edge information of the difference image are considered in this method. Experimental results obtained on four real SAR image data sets confirm the effectiveness of the proposed approach.

Semantic concept detection is a very promising way to manage huge amounts of personal contents. In this paper, we propose discriminative structure learning for semantic concept detection with graph embedding. We focus on the task of whole-image categorization and employ graphical model inference based semi-supervised learning (SSL) to detect the semantic category of an image. To effectively extract global features from images, we utilize the spatial pyramid image representation. Then, we perform data warping over the histogram intersection kernel-based graph to learn discriminative features and make image distributions more discriminative for both labeled and unlabeled images. By data warping, each cluster of images is mapped into a relatively compact cluster as well as clusters become well-separated. Moreover, we adopt low-rank representation (LRR) in the embedded space to capture the global discriminative structure from the learned features for label propagation due to its good ability of capturing the global structure of data distributions and robustness against noise and outliers. Finally, we design a smooth nonlinear detector on the captured global discriminative structure to effectively propagate the concepts of labeled images to unlabeled images. Extensive experiments are conducted on four publicly available databases to verify the superiority of the proposed method compared to the state-of-the-art methods.

A novel spectral-spatial classification method based on mean shift and sparse representation classifier (SRC) for hyperspectral images is proposed in this paper. Firstly, the nonnegative matrix factorization, is used as a preprocessing for mean shift. Then, the mean shift algorithm is adopted to partition an image into amount of blocks and get the segmentation map. Through this way, many size-variable and close regions can be got while the boundary information is remained. Secondly, the classification map is obtained by using the SRC. Finally, the fusion of the segmentation map and the classification map is done by using the majority vote rule. Experimental results on two real hyperspectral images demonstrate the effectiveness and good performance of the proposed method.

Hyperspectral images (HSIs) provide a vast amount of geometrical, radiation, and spectral information about a scene. However, high-dimensional data make HSI classification complex and time consuming. It is important to reduce the dimensionality and find a low-dimensional representation of the high-dimensional data. Since the labels of HSI data are really difficult to collect while the unlabeled data are abundant and easy to obtain, in this letter, a semisupervised dimensionality reduction method using both limited labeled samples and a large number of unlabeled samples based on a local scaling cut (LSC) criterion is proposed. LSC is similar to linear discriminant analysis (LDA), but it can handle the heteroscedastic and multimodal data for which LDA fails. The framework of our proposed method contains two terms: 1) a discrimination term based on the labeled samples and 2) a regularization term based on the prior knowledge provided by both labeled and unlabeled samples. Experimental results show that our proposed algorithm provides a relatively promising performance compared with other methods. Moreover, the algorithm is stable and insensitive to parameters.

In this paper we propose an unsupervised approach for SAR image change detection task. A new method based on compressed sensing is applied. First using the PPB method for the speckle reduction, and then the logarithm ratio method is applied to generate a simple change map, and then the compressed sensing-based method is used to part the change map into a low rank part and a sparse part, where the sparse part is correspond to the changed area, finally k-means algorithm is applied to cluster the sparse part into two clusters. Experiment results show the effectiveness and feasibility of the proposed method.