Jie Geng

High-Resolution SAR Image Classification via Deep Convolutional Autoencoders

Synthetic aperture radar (SAR) image classification is a hot topic in the interpretation of SAR images. However, the absence of effective feature representation and the presence of speckle noise in SAR images make classification difficult to handle. In order to overcome these problems, a deep convolutional autoencoder (DCAE) is proposed to extract features and conduct classification automatically. The deep network is composed of eight layers: a convolutional layer to extract texture features, a scale transformation layer to aggregate neighbor information, four layers based on sparse autoencoders to optimize features and classify, and last two layers for postprocessing. Compared with hand-crafted features, the DCAE network provides an automatic method to learn discriminative features from the image. A series of filters is designed as convolutional units to comprise the gray-level cooccurrence matrix and Gabor features together. Scale transformation is conducted to reduce the influence of the noise, which integrates the correlated neighbor pixels. Sparse autoencoders seek better representation of features to match the classifier, since training labels are added to fine-tune the parameters of the networks. Morphological smoothing removes the isolated points of the classification map. The whole network is designed ingeniously, and each part has a contribution to the classification accuracy. The experiments of TerraSAR-X image demonstrate that the DCAE network can extract efficient features and perform better classification result compared with some related algorithms.

Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder

Deep learning, which represents data by a hierarchical network, has proven to be efficient in computer vision. To investigate the effect of deep features in hyperspectral image (HSI) classification, this paper focuses on how to extract and utilize deep features in HSI classification framework. First, in order to extract spectral-spatial information, an improved deep network, spatial updated deep auto-encoder (SDAE), is proposed. SDAE, which is an improved deep auto-encoder (DAE), considers sample similarity by adding a regularization term in the energy function, and updates features by integrating contextual information. Second, in order to deal with the small training set using deep features, a collaborative representation-based classification is applied. Moreover, in order to suppress salt-and-pepper noise and smooth the result, we compute the residual of collaborative representation of all samples as a residual matrix, which can be effectively used in a graph-cut-based spatial regularization. The proposed method inherits the advantages of deep learning and has solutions to add spatial information of HSI in the learning network. Using collaborative representation-based classification with deep features makes the proposed classifier extremely robust under a small training set. Extensive experiments demonstrate that the proposed method provides encouraging results compared with some related techniques.

Deep Supervised and Contractive Neural Network for SAR Image Classification

The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches.

Hyperspectral image classification with small training set by deep network and relative distance prior

This paper presents a hyperspectral image classification method based on deep network, which has shown great potential in various machine learning tasks. Since the quantity of training samples is the primary restriction of the performance of classification methods, we impose a new prior on the deep network to deal with the instability of parameter estimation under this circumstances. On the one hand, the proposed method adjusts parameters of the whole network to minimize the classification error as all supervised deep learning algorithm, on the other hand, unlike others, it also minimize the discrepancy within each class and maximize the difference between different classes. The experimental results showed that the proposed method is able to achieve great performance under small training set.

Weighted Fusion-Based Representation Classifiers for Marine Floating Raft Detection of SAR Images

Detection of a marine floating raft is significant for ocean utilization, which provides a basis for marine ecosystem protection. In this case study, supervised classifiers of weighted fusion-based representation are proposed to detect marine floating raft using synthetic aperture radar images. To remove the speckle noise and obtain more discriminative features, a weighted low-rank matrix factorization (WLRMF) model is developed to optimize features before detection, where the matrix of patch features is decomposed to acquire the denoised features. Weighted fusion-based representation classifiers (WFRCs) with weighted multiplication are proposed to combine the sparse representation classifier (SRC) and the collaborative representation classifier (CRC) for floating raft detection, which can capture the competition between the floating raft and water surface as well as the collaboration within-class samples. Experiments on the study area of the Bohai Sea confirm that the proposed approach produces better results than some related methods. It is demonstrated that the WLRMF model extracts effective features and overcomes the influence of speckle noise at the same time, and the WFRC model is able to take advantages of the SRC in competition and CRC in collaboration for improving detection accuracies.

Joint collaborative representation for polarimetric SAR image classification

Polarimetric synthetic aperture radar (PolSAR) images are widely applied in terrain and ground cover classification. Feature extraction and classifier design are both important in Pol- SAR image classification. In this paper, various target decompositions are applied to obtain different polarimetric features. Since that neighboring pixels usually belong to the same species, they can be simultaneously represented through linear combinations of training samples. Therefore, a collaborative representation-based classifier with spatially joint regularization is adopted for classification. Experimental results demonstrate that the joint collaborative representation model performs better than other state-of-the-art methods, such as support vector machine and simultaneous sparse representation.

Classification of fusing SAR and multispectral image via deep bimodal autoencoders

Classification of multisensor data provides potential advantages over a single sensor in accuracy. In this paper, deep bimodal autoencoders are proposed for classification of fusing synthetic aperture radar (SAR) and multispectral images. The proposed deep network based on autoencoders is trained to discover both independencies of each modality and correlations across the modalities. Specifically, the sparse encoding layers in the front are applied to learn features of each modality, then shared representation layers in the middle are developed to learn fused features of two modalities, finally softmax classifier in the top is adopted for classification. Experimental results demonstrate that the proposed network is able to yield superior classification performance compared with some related networks.

Change detection of SAR images based on supervised contractive autoencoders and fuzzy clustering

In this paper, supervised contractive autoencoders (SCAEs) combined with fuzzy c-means (FCM) clustering are developed for change detection of synthetic aperture radar (SAR) images, which aim to take advantage of deep neural networks to capture changed features. Given two original SAR images, Lee filter is used in preprocessing and the difference image (DI) is obtained by log ratio method. Then, FCM is adopted to analyse DI, which yields pseudo labels for guiding the training of SCAEs. Finally, SCAEs are developed to learn changed features from bitemporal images and DI, which can obtain discriminative features and improve detection accuracies. Experiments on three data demonstrate that the proposed method outperforms some related approaches.

An iterative low-rank representation for SAR image despeckling

Speckle noises are inherent issues in synthetic aperture radar (SAR) images, which hampers the analysis and interpretation of SAR images. In this paper, we propose an iterative low-rank representation algorithm for SAR image despeckling. The original SAR image is first transformed to the logarithmic image, which is then filtered iteratively by the proposed low-rank representation model. Specifically, in each iteration, similar patches measured by the Mahalanobis distance are collected into a group, and then filtered by the nuclear regularized low-rank representation. Finally, all of the filtered patches are aggregated to form the denoised image. Experimental results demonstrate that the proposed algorithm is able to yield state-of-the-art SAR image despeckling performance.

Spectral-spatial information extraction and classification of mangrove species using joint sparse representation

Classification of mangrove species is very important for monitoring and protecting the coastal ecosystem. In this paper, we present a new spectral-spatial classifier that uses multi-spectral image captured by the ZY-3 satellite to distinguish seven mangrove species in the Beihai ecological monitoring area, Guangxi, China. In order to extract the spatial information, a correlative filter is designed to incorporate neighborhood correlative information before classification. Moreover, a feature optimization algorithm based on dictionary learning is applied to reduce the noise and improve the discrimination of sample features. Finally, a classification method using joint sparse representation is proposed to extract the mangrove region and recognize seven mangrove species. The classification results show that the major species in the study area are Aegiceras corniculatum and Avicenna marina that conform to field investigations. The overall accuracy reaches 95.62% and the kappa coefficient achieves the value of 0.9380. Hence, the accuracy and efficiency of our proposed method are demonstrated in mangrove species classification.