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Recently, many deep learning methods are applied with the spatial information to learn features for polarimetric synthetic aperture radar (PolSAR) image classification. However, without considering the multi-scale information, the classification performance of these methods are limited. Hence, this paper proposes a multi-scale feature extraction method based on stacked sparse autoencoder (SSAE), named the multi-scale SSAE (MS-SSAE), to improve the classification performance. This method extracts the deep multi-scale features by a two-stage framework. In the first stage, the SSAE uses training data at different scales to extract the multi-scale features. Then, a 1-D average pooling strategy is proposed to reduce the feature dimensionality at the second stage. Therefore, the MS-SSAE can capture discriminative multi-scale features. The experimental results certify that the MS-SSAE can not only improve the classification accuracy, but also remain the details in the image. [ABSTRACT FROM AUTHOR]

Many works have been recently presented to extract efficient features for automatic target recognition of synthetic aperture radar (SAR) images. However, they are limited in the discriminative ability of similar targets and robustness to the remarkable speckle noises and background clutters existed in images. In this paper, we propose a Complementary Spatial Pyramid Coding (CSPC) approach in the framework of Spatial Pyramid Matching (SPM). Both the coding coefficients and coding residuals are explored to develop more discriminative and robust features for representing SAR images. Multiple codebooks are first built from some training example images, where each codebook is formulated by local features of a certain class of samples. Then multiple sparse coding models are developed to derive features of a target under these codebooks. Additionally, these coding residuals are further sparsely encoded in the same way to that of local features. Finally, the encoded local features and the residual features are pooled according to spatial pyramid respectively, then concatenated to form the complementary features for the subsequent classification. The experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public database verify the superior performance of the proposed method to some related approaches. [ABSTRACT FROM AUTHOR]

With the introduction of spatial-spectral fusion and deep learning, the classification performance of hyperspectral imagery (HSI) has been promoted greatly. For some widely used datasets, the classification accuracy almost reaches 100%. However, for hyperspectral image classification, random sampling is still the most common strategy to collect the training and test samples. Because the training and test samples are randomly selected from the same image, so they have a high correlation and the classification results are overoptimistic. Besides, random sampling is not a good choice for practical applications because we cannot always collect training and test samples from the same region. Disjoint sampling selects training and testing samples from different local regions, which will provide a more objective performance evaluation for HSI classification models. In this paper, we first show the huge classification performance difference caused by different sampling strategies with a simple experiment, then we analyze the underlying reasons from the spectral information, spatial-spectral combination, sample overlapping and spatial distance, finally, a semi-supervised feature learning method is proposed for disjoint HSI classification, in which the spatial and spectral information are exploited effectively and reasonably. The experimental results based on three HSI datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Semi-supervised learning is a popular machine learning technique where only a small number of labeled examples are available and a large pool of unlabeled examples can be obtained easily. In co-training by committee, a paradigm of semi-supervised learning, it is necessary to pick out a fixed number of most confident examples according to the ranking of class probability values at each iteration. Unfortunately, the class probability values may repeat, which results in the problem that some unlabeled instances share the same probability and will be picked out randomly. This brings a negative effect on the improvement of the performance of classifiers. In this paper, we propose a simple method to deal with this problem under the intuition that different probabilities are crucial. The distance metric between unlabeled instances and labeled instances can be combined with the probabilities of class membership of committee. Two distance metrics are considered to assign each unlabeled example a unique probability value. In order to prove that our method can get higher-quality examples and reduce the introduction of noise, a data editing technique is used to compare with our method. Experimental results verify the effectiveness of our method and the data editing technique, and also confirm that the method for the first distance metric is generally better than the data editing technique. [Copyright &y& Elsevier]

Spectral–spatial classification methods can improve the classification accuracy of hyperspectral imagery (HSI) dramatically. However, this improvement is caused in part by the overlap between training set and test set. In this paper, a novel non-overlapping spectral-spatial classification framework based on set-to-sets distance is proposed. First, each class is sampled using a controlled random sampling method, which is regarded as a training set. The image is segmented into many superpixels and each superpixel is taken as a test set. In order to ensure that the test set and the training set are not overlapped with each other, the training pixels contained in each test superpixel are deleted. Then, the training set of each class is compressed into a more compact set to reduce the computational complexity and kernel trick is used to make samples be approximately linearly separable. Finally, each test set is modeled as a convex hull, this hull is represented collaboratively with all training sets. With the resolved representation coefficients, the distance between the test set and each training set can be calculated for classification. Experimental results based on three real HSI data sets demonstrate the superiority of the proposed method to state-of-the-art algorithms under the non-overlapping sampling strategy. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN) has proven effective at solving difficult image classification problems, but it can be challenging to design its architecture. We try to simplify the search process of the optimal architecture and minimize human participation, so quantum behaved Particle Swarm Optimization with binary encoding (BQPSO) is employed after analyzing the limitations of traditional Particle Swarm Optimization (PSO). To do this, we propose a novel and robust binary encoding strategy which does not require users with domain knowledge on CNN. Then a new quantum behaved evolving strategy is proposed to ensure the effectiveness of evolved CNN architectures. Finally, the performance of our algorithm is measured by the classification accuracy for several benchmark datasets commonly used in deep learning. The experimental results prove that our proposed method can achieve better performance and robustness than the traditional method. This is the first completely automatic algorithm in the area of using quantum behaved PSO to evolve CNN architectures. [ABSTRACT FROM AUTHOR]

The high dimensionality of hyperspectral imagery often introduces challenge for the conventional data analysis techniques. In order to improve the classification performance of hyperspectral imagery, metric learning is often introduced to assign small distances between samples from the same class and large distances from different class. However, most of the traditional metric learning methods only adopt linear transformations, which cannot capture the complex nonlinear relationships between high dimensional samples. Inspired by the successful application of deep learning for the classification of hyperspectral imagery. In this paper, the deep neural network is introduced to learn the discriminating metric for the classification of hyperspectral images. In order to improve the reliability of classification results, the spectral and spatial information are combined with weighted classification probabilities. Experimental results demonstrate that the proposed method achieves satisfactory classification performance when compared with other metric learning methods or deep models. [ABSTRACT FROM AUTHOR]

Abstract: Graph-based semi-supervised learning has been widely researched in recent years. A novel Low-Rank Representation with Local Constraint (LRRLC) approach for graph construction is proposed in this paper. The LRRLC is derived from the original Low-Rank Representation (LRR) algorithm by incorporating the local information of data. Rank constraint has the capacity to capture the global structure of data. Therefore, LRRLC is able to capture both the global structure by LRR and the local structure by the locally constrained regularization term simultaneously. The regularization term is induced by the locality assumption that similar samples have large similarity coefficients. The measurement of similarity among all samples is obtained by LRR in this paper. Considering the non-negativity restriction of the coefficients in physical interpretation, the regularization term can be written as a weighted -norm. Then a semi-supervised learning framework based on local and global consistency is used for the classification task. Experimental results show that the LRRLC algorithm provides better representation of data structure and achieves higher classification accuracy in comparison with the state-of-the-art graphs on real face and digit databases. [Copyright &y& Elsevier]