Yanfei Zhong

An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery

A new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing. Modern commercial imaging satellites, owing to their large volume of high-resolution imagery, offer greater opportunities for automated image analysis. Hence, we propose a novel unsupervised machine-learning algorithm namely unsupervised artificial immune classifier (UAIC) to perform remote sensing image classification. In addition to their nonlinear classification properties, UAIC possesses biological properties such as clonal selection, immune network, and immune memory. The implementation of UAIC comprises two steps: initially, the first clustering centers are acquired by randomly choosing from the input remote sensing image. Then, the classification task is carried out. This assigns each pixel to the class that maximizes stimulation between the antigen and the antibody. Subsequently, based on the class, the antibody population is evolved and the memory cell pool is updated by immune algorithms until the stopping criterion is met. The classification results are evaluated by comparing with four known algorithms: K-means, ISODATA, fuzzy K-means, and self-organizing map. It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments we carried out.

Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery

A new stochastic search strategy inspired by the clonal selection theory in an artificial immune system is proposed for dimensionality reduction of hyperspectral remote-sensing imagery. The clonal selection theory is employed to describe the basic features of an immune response to an antigenic stimulus in order to meet the requirement of diversity in the antibody population. In our proposed strategy, dimensionality reduction is formulated as an optimization problem that searches an optimum with less number of features in a feature space. In line with this novel strategy, a feature subset search algorithm, clonal selection Feature-Selection (CSFS) algorithm, and a feature-weighting algorithm, Clonal-Selection Feature-Weighting (CSFW) algorithm, have been developed. In the CSFS, each solution is evolved in binary space, and the value of each bit is either 0 or 1, which indicates that the corresponding feature is either removed or selected, respectively. In CSFW, each antibody is directly represented by a string consisting of integer numbers and their corresponding weights. These algorithms are compared with the following four well-known algorithms: sequential forward selection, sequential forward floating selection, genetic-algorithm-based feature selection, and decision-boundary feature extraction using the hyperspectral remote-sensing imagery acquired by the Pushbroom Hyperspectral Imager and the Airborne Visible/Infrared Imaging Spectrometer, respectively. Experimental results demonstrate that CSFS and CSFW outperform other algorithms and hence provide effective new options for dimensionality reduction of hyperspectral remote-sensing imagery.

A new sub-pixel mapping algorithm based on a BP neural network with an observation model

The mixed pixel is a common problem in remote sensing classification. Even though the composition of these pixels for different classes can be estimated with a pixel un-mixing model, the output provides no indication of how such classes are distributed spatially within these pixels. Sub-pixel mapping is a technique designed to use the output information with the assumption of spatial dependence to obtain a sharpened image. Pixels are divided into sub-pixels, representing the land cover class fractions. This paper proposes a new algorithm based on a back-propagation (BP) network combined with an observation model. This method provides an effective method of obtaining the sub-pixel mapping result and can provide an approximation of the reference classification image. With the upscale factor, the model was tested on both a simple artificial image and a remote sensing image, and the results confirm that the proposed mapping algorithm has better performance than the original BPNN model.

An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery

The artificial immune network (AIN), a computational intelligence model based on artificial immune systems inspired by the vertebrate immune system, has been widely utilized for pattern recognition and data analysis. However, due to the inherent complexity of current AIN models, their application to multi-/hyperspectral remote sensing image classification has been severely restricted. This paper presents a novel supervised AIN-namely, the artificial antibody network (ABNet), based on immune network theory-aimed at performing multi-/hyperspectral image classification. To construct the ABNet, the artificial antibody population (AB) model was utilized. AB is the set of antibodies where each antibody has two attributes-its center vector and recognizing radius-thus each can recognize all antigens within its recognizing radius. In contrast to the traditional AIN model, ABNet can adaptively obtain these two parameters by evolving the antigens without relying on user-defined parameters in the training step. During the process of training, to enlarge the recognizing range, the immune operators (such as clone, mutation, and selection) were used to enhance the AB model to find better antibody in the feature space, which may recognize as much antigen as possible. After the training process, the trained ABNet was utilized to classify the remote sensing image, exhibiting superior learning abilities. Three experiments with different types of images were performed to evaluate the performance of the proposed algorithm in comparison to other supervised classification algorithms: minimum distance, Gaussian maximum likelihood, back-propagation neural network, and our previously developed artificial immune classifiers-resource-limited classification of remote sensing image and multiple-valued immune network classifier. The experimental results demonstrate that ABNet has remarkable recognizing accuracy and ability to provide effective classification for multi-/hyperspectral remote sensing imagery, superior to other methods.

Remote Sensing Image Subpixel Mapping Based on Adaptive Differential Evolution

In this paper, a novel subpixel mapping algorithm based on an adaptive differential evolution (DE) algorithm, namely, adaptive-DE subpixel mapping (ADESM), is developed to perform the subpixel mapping task for remote sensing images. Subpixel mapping may provide a fine-resolution map of class labels from coarser spectral unmixing fraction images, with the assumption of spatial dependence. In ADESM, to utilize DE, the subpixel mapping problem is transformed into an optimization problem by maximizing the spatial dependence index. The traditional DE algorithm is an efficient and powerful population-based stochastic global optimizer in continuous optimization problems, but it cannot be applied to the subpixel mapping problem in a discrete search space. In addition, it is not an easy task to properly set control parameters in DE. To avoid these problems, this paper utilizes an adaptive strategy without user-defined parameters, and a reversible-conversion strategy between continuous space and discrete space, to improve the classical DE algorithm. During the process of evolution, they are further improved by enhanced evolution operators, e.g., mutation, crossover, repair, exchange, insertion, and an effective local search to generate new candidate solutions. Experimental results using different types of remote images show that the ADESM algorithm consistently outperforms the previous subpixel mapping algorithms in all the experiments. Based on sensitivity analysis, ADESM, with its self-adaptive control parameter setting, is better than, or at least comparable to, the standard DE algorithm, when considering the accuracy of subpixel mapping, and hence provides an effective new approach to subpixel mapping for remote sensing imagery.

Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery

Scene classification has been proved to be an effective method for high spatial resolution (HSR) remote sensing image semantic interpretation. The probabilistic topic model (PTM) has been successfully applied to natural scenes by utilizing a single feature (e.g., the spectral feature); however, it is inadequate for HSR images due to the complex structure of the land-cover classes. Although several studies have investigated techniques that combine multiple features, the different features are usually quantized after simple concatenation (CAT-PTM). Unfortunately, due to the inadequate fusion capacity of k-means clustering, the words of the visual dictionary obtained by CAT-PTM are highly correlated. In this paper, a semantic allocation level (SAL) multifeature fusion strategy based on PTM, namely, SAL-PTM (SAL-pLSA and SAL-LDA) for HSR imagery is proposed. In SAL-PTM: 1) the complementary spectral, texture, and scale-invariant-featuretransform features are effectively combined; 2) the three features are extracted and quantized separately by k-means clustering, which can provide appropriate low-level feature descriptions for the semantic representations; and 3)the latent semantic allocations of the three features are captured separately by PTM, which follows the core idea of PTM-based scene classification. The probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) models were compared to test the effect of different PTMs for HSR imagery. A U.S. Geological Survey data set and the UC Merced data set were utilized to evaluate SAL-PTM in comparison with the conventional methods. The experimental results confirmed that SAL-PTM is superior to the single-feature methods and CAT-PTM in the scene classification of HSR imagery.

Hybrid Detectors Based on Selective Endmembers

Subpixel target detection is a challenge in hyperspectral image analysis. As the spatial resolution of hyperspectral imagery is usually limited, subpixel targets only occupy part of the pixel area. In such cases, the spatial characteristics of the targets are hard to acquire, and the only information we can use comes from spectral characteristics. Several kinds of method based on spectral characteristics have been proposed in the past. One is the linear unmixing method, which can provide the abundances of different endmembers in the hyperspectral imagery, including the target abundance. Another focuses on providing statistically reliable rules to separate subpixel targets from their backgrounds. Recently, hybrid detectors combining the aforementioned two methods were put forward, which cannot only figure out the quantitative information of the endmembers but also put this quantitative information into an adaptive matched subspace detector or adaptive cosine/coherent estimate detector to separate the target pixels from the background with statistically reliable rules. However, in these methods, all the endmembers are used to construct the statistical rule, while in most cases only some of the endmembers are actually contained in the pixels. This paper proposes hybrid endmembers selective detectors in which different kinds of endmembers are used according to different pixels to ensure that the true composition of endmembers in each pixel is applied in the detection procedure. Three different types of hyperspectral data were used in our experiments, and our proposed hybrid endmember selective detectors showed better performances than the current hybrid detectors in all the experiments.

Sub-Pixel Mapping Based on a MAP Model With Multiple Shifted Hyperspectral Imagery

Sub-pixel mapping is technique used to obtain the spatial distribution of different classes at the sub-pixel scale by transforming fraction images to a classification map with a higher resolution. Traditional sub-pixel mapping algorithms only utilize a low-resolution image, the information of which is not enough to obtain a high-resolution land-cover map. The accuracy of sub-pixel mapping can be improved by incorporating auxiliary datasets, such as multiple shifted images in the same area, to provide more sub-pixel land-cover information. In this paper, a sub-pixel mapping framework based on a maximum a posteriori (MAP) model is proposed to utilize the complementary information of multiple shifted images. In the proposed framework, the sub-pixel mapping problem is transformed to a regularization problem, and the MAP model is used to regularize the sub-pixel mapping problem to be well-posed by adding some prior information, such as a Laplacian model. The proposed algorithm was compared with a traditional sub-pixel mapping algorithm based on a single image, and another multiple shifted images based sub-pixel mapping method, using both synthetic and real hyperspectral images. Experimental results demonstrated that the proposed approach outperforms the traditional sub-pixel mapping algorithms, and hence provides an effective option to improve the accuracy of sub-pixel mapping for hyperspectral imagery.

AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.

Non-Local Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Sparse unmixing is a promising approach that acts as a semi-supervised unmixing strategy by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, conventional sparse unmixing involves finding the optimal subset of signatures for the observed data in a very large standard spectral library, without considering the spatial information. In this paper, a new sparse unmixing algorithm based on non-local means, namely non-local sparse unmixing (NLSU), is proposed to perform the unmixing task for hyperspectral remote sensing imagery. In NLSU, the non-local means method, as a regularizer for sparse unmixing, is used to exploit the similar patterns and structures in the abundance image. The NLSU algorithm based on the sparse spectral unmixing model can improve the spectral unmixing accuracy by incorporating the non-local spatial information by means of a weighting average for all the pixels in the abundance image. Five experiments with three simulated and two real hyperspectral images were performed to evaluate the performance of the proposed algorithm in comparison to the previous sparse unmixing methods: sparse unmixing via variable splitting and augmented Lagrangian (SUnSAL) and sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAL-TV). The experimental results demonstrate that NLSU outperforms the other algorithms, with a better spectral unmixing accuracy, and is an effective spectral unmixing algorithm for hyperspectral remote sensing imagery.