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In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm...
In this paper, we present a novel classification model which combines the convolutional sparse coding framework with the classification strategy. In the training phase, the proposed model trained a convolutional filter bank by all images of each class. In the test phase, the label of test image is determined by all convolutional filter banks. Compared with canonical sparse representation and dictionary...
Low-level feature encoding combined with Spatial Pyramid Matching (SPM) is widely adopted in the image classification system nowadays to extract features, which are usually high-dimensional. This not only makes the classification problem computationally prohibitive, but also raises other issues, such as the “curse of dimensionality”. In this paper we present supervised dimensionality reduction (DR)...
Localizing seismic structures that can form traps for hydrocarbon reservoirs within large seismic volumes is a very challenging task. Due to the lack of accurately labeled data, we propose a weakly-supervised model for labeling seismic volumes using only a few labeled exemplars. Using six manually-labeled patches, we are able to extract patches that contain instances of similar geophysical structures...
Plankton image classification plays an important role in the ocean ecosystems research. Recently, a large scale database for plankton classification with over 3 million images annotated with over 100 classes was released. However, the database suffers from imbalanced class distribution in which over 90% of images belong to only 5 classes. Due to this class-imbalance problem, the existing classification...
Fisher discrimination dictionary sparse learning (FDDL) has led to interesting image recognition results where the Fisher discrimination criterion is subject to the coding coefficients. But Fisher discrimination criterion has the limitations of data distribution assumptions and does not consider the local manifold structure of the coding coefficients. In this paper, we will introduce a novel Fisher...
To train a scene classifier with good generalization capability, a large number of human labeled training images are often needed. However, a large number of well-labeled training images may not always be available. To alleviate this problem, the web resources-aided scene classification framework was proposed. The present paper is a new development based on our previously proposed framework [1], with...
As an application of image recognition, special vehicle recognition is very important in military field. This paper proposes a deep-transfer model (DTM) to overcome the problems in existing recognition methods. The DTM combines deep-learning and transfer-learning to solve the difficulty in training deep model with insufficient simples, improving the performance of the recognition algorithm. At last,...
In this paper, a novel kernel low rank representation (KLRR) method for hyperspectral image classification is proposed. Firstly, we extract the global structure characteristics information of the hyperspectral image based on low rank representation (LRR), then use it as a prior to constrain the recovery coefficient matrix. In order to further improve the classification efficiency and deal with the...
The methodology of sparse representations (SRs) has being popular in hyperspectral image (HSI) classification. To boost the SR-based classification for HSIs, in this paper we present a designation of sparse representation involving random subspace. First, random band selection or random projection generates data subspaces from an original HSI. Then, the sparse representation on each subspace is solved...
This paper proposes a novel solution to solve the problem of imbalanced training samples in hyperspectral image classification. It consists of two parts: one is for large-size sample sets and the other is for small-size sets. We exploit an orthogonal projection based algorithm to select samples from large-size ones; meanwhile, we propose an algorithm based on the orthogonal complementary subspace...
In this paper, we describe a novel deep convolutional neural networks (CNN) based approach called contextual deep CNN that can jointly exploit spatial and spectral features for hyperspectral image classification. The contextual deep CNN first concurrently applies multiple 3-dimensional local convolutional filters with different sizes jointly exploiting spatial and spectral features of a hyperspectral...
This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral images (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of ideal kernel according to a regularization kernel learning framework,...
Active learning has obtained a great success in supervised remotely sensed hyperspectral image classification, since it can be used to select highly informative training samples. As an intrinsically biased sampling approach, it generally favors the selection of samples following discriminative distributions, i.e., those located in low density areas in feature space. However, the hyperspectral data...
In this paper, a novel feature extraction framework is proposed for hyperspectral image classification. Inspired by the role of discriminant function in classifier, which intends to learn a mapping from the input features to label information in class space, we develop a feature extraction framework to learn the new feature representation of original input features in class space, by establishing...
A probability graph model can effectively model spectral and spatial dependencies within remote sensing images for land cover classification. The most common structure used to unify this probabilistic information is a second order Markov network that encapsulate unary and pairwise potentials. In this paper we explore various heuristics to discover new graph structures that will assist with classifying...
Conditional Random Field(CRF) has been successfully applied to the hyperspectral image classification. However, it suffers from the availability of large amount of labeled pixels, which is labor- and time-consuming to obtain in practice. In this paper, a semi-supervised CRF(ssCRF) is proposed for hyperspectral image classification with limited labeled pixels. Laplacian Support Vector Machine(LapSVM),...
This paper proposes a new supervised classification method for hyperspectral images combining the spectral and spatial information. The main contribution is presented by combining subspace-based support vector machine (SVMsub) and Markov random field (MRF). A SVM classifier integrated with a subspace projection is first used to model the posterior distributions of the classes from the spectral information...
This paper presents a novel extended multi-structure local binary pattern (EMSLBP) approach for high-resolution image classification, generalizing the well-known local binary pattern (LBP) approach. In the proposed EMSLBP approach, three-coupled descriptors with multi-structure sampling are proposed to extract complementary features (pixel value and radial difference) from local image patches. The...
This paper presents a sparse representation (SR)-based selective ensemble learning method for Polarimetric SAR image classification. Sparse representation uses the least dictionary atoms which come from a structured dictionary to represent the data, however, different training samples will lead to different options of the selected atoms and the corresponding coefficients, which will lead different...
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