Lefei Zhang

On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification

In hyperspectral remote sensing image classification, multiple features, e.g., spectral, texture, and shape features, are employed to represent pixels from different perspectives. It has been widely acknowledged that properly combining multiple features always results in good classification performance. In this paper, we introduce the patch alignment framework to linearly combine multiple features in the optimal way and obtain a unified low-dimensional representation of these multiple features for subsequent classification. Each feature has its particular contribution to the unified representation determined by simultaneously optimizing the weights in the objective function. This scheme considers the specific statistical properties of each feature to achieve a physically meaningful unified low-dimensional representation of multiple features. Experiments on the classification of the hyperspectral digital imagery collection experiment and reflective optics system imaging spectrometer hyperspectral data sets suggest that this scheme is effective.

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification. As a matter of fact, by carefully addressing the practical demands in RS applications and designing the input?output levels of the whole network, we have found that DL is actually everywhere in RS data analysis: from the traditional topics of image preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.In this technical tutorial, a general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks. Although extensive experimental results confirm the excellent performance of the DL-based algorithms in RS big data analysis, even more exciting prospects can be expected for DL in RS. Key bottlenecks and potential directions are also indicated in this article, guiding further research into DL for RS data.

Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction

In this paper, we propose a method for the dimensionality reduction (DR) of spectral-spatial features in hyperspectral images (HSIs), under the umbrella of multilinear algebra, i.e., the algebra of tensors. The proposed approach is a tensor extension of conventional supervised manifold-learning-based DR. In particular, we define a tensor organization scheme for representing a pixels spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification. The optimal solution of TDLA is obtained by alternately optimizing each mode of the input tensors. The methods are tested on three public real HSI data sets collected by hyperspectral digital imagery collection experiment, reflective optics system imaging spectrometer, and airborne visible/infrared imaging spectrometer. The classification results show significant improvements in classification accuracies while using a small number of features.

Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding

In computer vision and pattern recognition researches, the studied objects are often characterized by multiple feature representations with high dimensionality, thus it is essential to encode that multiview feature into a unified and discriminative embedding that is optimal for a given task. To address this challenge, this paper proposes an ensemble manifold regularized sparse low-rank approximation (EMR-SLRA) algorithm for multiview feature embedding. The EMR-SLRA algorithm is based on the framework of least-squares component analysis, in particular, the low dimensional feature representation and the projection matrix are obtained by the low-rank approximation of the concatenated multiview feature matrix. By considering the complementary property among multiple features, EMR-SLRA simultaneously enforces the ensemble manifold regularization on the output feature embedding. In order to further enhance its robustness against the noise, the group sparsity is introduced into the objective formulation to impose direct noise reduction on the input multiview feature matrix. Since there is no closed-form solution for EMR-SLRA, this paper provides an efficient EMR-SLRA optimization procedure to obtain the output feature embedding. Experiments on the pattern recognition applications confirm the effectiveness of the EMR-SLRA algorithm compare with some other multiview feature dimensionality reduction approaches. HighlightsThis paper proposes a novel EMR-SLRA algorithm for multiview feature embedding.The least-squares component analysis is generalized to multiview version.The ensemble manifold regularization is enforced to explore the complementarity.The group sparsity is introduced to promote the robustness against the noise.An efficient iterative procedure is developed to solve EMR-SLRA.

Sparse Transfer Manifold Embedding for Hyperspectral Target Detection

Target detection is one of the most important applications in hyperspectral remote sensing image analysis. However, the state-of-the-art machine-learning-based algorithms for hyperspectral target detection cannot perform well when the training samples, especially for the target samples, are limited in number. This is because the training data and test data are drawn from different distributions in practice and given a small-size training set in a high-dimensional space, traditional learning models without the sparse constraint face the over-fitting problem. Therefore, in this paper, we introduce a novel feature extraction algorithm named sparse transfer manifold embedding (STME), which can effectively and efficiently encode the discriminative information from limited training data and the sample distribution information from unlimited test data to find a low-dimensional feature embedding by a sparse transformation. Technically speaking, STME is particularly designed for hyperspectral target detection by introducing sparse and transfer constraints. As a result of this, it can avoid over-fitting when only very few training samples are provided. The proposed feature extraction algorithm was applied to extensive experiments to detect targets of interest, and STME showed the outstanding detection performance on most of the hyperspectral datasets.

Joint Collaborative Representation With Multitask Learning for Hyperspectral Image Classification

In this paper, we propose a joint collaborative representation (CR) classification method with multitask learning for hyperspectral imagery. The proposed approach consists of the following aspects. First, several complementary features are extracted from the hyperspectral image. We next apply these features into the unified multitask-learning-based CR framework to acquire a representation vector and adaptive weight for each feature. Finally, the contextual neighborhood information of the image is incorporated into each feature to further improve the classification performance. The experimental results suggest that the proposed algorithm obtains a competitive performance and outperforms other state-of-the-art regression-based classifiers and the classical support vector machine classifier.

Stacked Convolutional Denoising Auto-Encoders for Feature Representation

Deep networks have achieved excellent performance in learning representation from visual data. However, the supervised deep models like convolutional neural network require large quantities of labeled data, which are very expensive to obtain. To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hierarchical representations without any label information. The network, optimized by layer-wise training, is constructed by stacking layers of denoising auto-encoders in a convolutional way. In each layer, high dimensional feature maps are generated by convolving features of the lower layer with kernels learned by a denoising auto-encoder. The auto-encoder is trained on patches extracted from feature maps in the lower layer to learn robust feature detectors. To better train the large network, a layer-wise whitening technique is introduced into the model. Before each convolutional layer, a whitening layer is embedded to sphere the input data. By layers of mapping, raw images are transformed into high-level feature representations which would boost the performance of the subsequent support vector machine classifier. The proposed algorithm is evaluated by extensive experimentations and demonstrates superior classification performance to state-of-the-art unsupervised networks.

Hyperspectral Remote Sensing Image Subpixel Target Detection Based on Supervised Metric Learning

The detection and identification of target pixels such as certain minerals and man-made objects from hyperspectral remote sensing images is of great interest for both civilian and military applications. However, due to the restriction in the spatial resolution of most airborne or satellite hyperspectral sensors, the targets often appear as subpixels in the hyperspectral image (HSI). The observed spectral feature of the desired target pixel (positive sample) is therefore a mixed signature of the reference target spectrum and the background pixels spectra (negative samples), which belong to various land cover classes. In this paper, we propose a novel supervised metric learning (SML) algorithm, which can effectively learn a distance metric for hyperspectral target detection, by which target pixels are easily detected in positive space while the background pixels are pushed into negative space as far as possible. The proposed SML algorithm first maximizes the distance between the positive and negative samples by an objective function of the supervised distance maximization. Then, by considering the variety of the background spectral features, we put a similarity propagation constraint into the SML to simultaneously link the target pixels with positive samples, as well as the background pixels with negative samples, which helps to reject false alarms in the target detection. Finally, a manifold smoothness regularization is imposed on the positive samples to preserve their local geometry in the obtained metric. Based on the public data sets of mineral detection in an Airborne Visible/Infrared Imaging Spectrometer image and fabric and vehicle detection in a Hyperspectral Mapper image, quantitative comparisons of several HSI target detection methods, as well as some state-of-the-art metric learning algorithms, were performed. All the experimental results demonstrate the effectiveness of the proposed SML algorithm for hyperspectral target detection.

A Multifeature Tensor for Remote-Sensing Target Recognition

In remote-sensing image target recognition, the target or background object is usually transformed to a feature vector, such as a spectral feature vector. However, this kind of vector represents only one pixel of a remote-sensing image that considers the spectral information but ignores the spatial relationship of neighboring pixels (i.e., the local texture and structure). In this letter, we propose a new way to represent an image object as a multifeature tensor that encodes both the spectral and textural information (Gabor function) and then apply the support tensor machine for target recognition. A range of experiments demonstrates that the effectiveness of the proposed method can deliver a high and correct recognition rate with a small number of training samples.

Supervised Graph Embedding for Polarimetric SAR Image Classification

This letter introduces an efficiency-manifold-learning-based supervised graph embedding (SGE) algorithm for polarimetric synthetic aperture radar (POLSAR) image classification. We use a linear dimensionality reduction technology named SGE to obtain a low-dimensional subspace which can preserve the discriminative information from training samples. Various POLSAR decomposition features are stacked into the input feature cube in the original high-dimensional feature space. The SGE is then implemented to project the input feature into the learned subspace for subsequent classification. The suggested method is validated by the full polarimetric airborne SAR system EMISAR, in Foulum, Denmark. The experiments show that the SGE presents a favorable classification accuracy and the valid components of the multifeature cube are also distinguished.