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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sea Ice Change Detection in SAR Images Based on Collaborative Representation
摘要： Sea ice change detection from synthetic aperture radar (SAR) images is important for navigation safety and natural resource extraction. This paper proposed a sea ice change detection method from SAR images based on collaborative representation. First, neighborhood-based ratio is used to generate a difference image (DI). Then, some reliable samples are selected from the DI by hierarchical fuzzy C-means (FCM) clustering. Finally, based upon these samples, collaborative representation method is utilized to classify pixels from the original SAR images into unchanged and changed class. From there, the final change map can be obtained. Experimental results on two real sea ice datasets demonstrate the superiority of the proposed method over two closely related methods.
关键词： sea ice change detection,synthetic aperture radar,clustering method,collaborative representation
更新于2025-09-23 15:23:52
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Laplacian Regularized Spatial-Aware Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery
摘要： Dimensionality Reduction (DR) models are of signi?cance to extract low-dimensional features for Hyperspectral Images (HSIs) data analysis where there exist lots of noisy and redundant spectral features. Among many DR techniques, the Graph-Embedding Discriminant Analysis framework has demonstrated its effectiveness for HSI feature reduction. Based on this framework, many representation based models are developed to learn the similarity graphs, but most of these methods ignore the spatial information, resulting in unsatisfactory performance of DR models. In this paper, we ?rstly propose a novel supervised DR algorithm termed Spatial-aware Collaborative Graph for Discriminant Analysis (SaCGDA) by introducing a simple but ef?cient spatial constraint into Collaborative Graph-based Discriminate Analysis (CGDA) which is inspired by recently developed Spatial-aware Collaborative Representation (SaCR). In order to make the representation of samples on the data manifold smoother, i.e., similar pixels share similar representations, we further add the spectral Laplacian regularization and propose the Laplacian regularized SaCGDA (LapSaCGDA), where the two spectral and spatial constraints can exploit the intrinsic geometric structures embedded in HSIs ef?ciently. Experiments on three HSIs data sets verify that the proposed SaCGDA and LapSaCGDA outperform other state-of-the-art methods.
关键词： hyperspectral imagery,graph embedding,dimensionality reduction,collaborative representation,discriminant analysis
更新于2025-09-23 15:22:29
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Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares
摘要： As a widely used classi?er, sparse representation classi?cation (SRC) has shown its good performance for hyperspectral image classi?cation. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classi?cation purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., (cid:96)2-norm) can be used to regularize the coding coef?cients, except for the sparsity (cid:96)1-norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS) for hyperspectral image classi?cation. Furthermore, in order to improve the classi?cation performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1) the coef?cient-level regularization strategy, and (2) the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information ef?ciently in the regularization framework.
关键词： least squares,hyperspectral,sparse representation,regularization,image classification,posterior probability,collaborative representation
更新于2025-09-23 15:21:21
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A New Local Knowledge-Based Collaborative Representation for Image Recognition
摘要： Recently, collaborative representation based classifiers (CRC) have shown outstanding performances in recognition tasks. The key to success of most CRC algorithms states that the testing samples can be coded well by a suitable dictionary globally, while the local knowledge between samples has not been fully considered. We observe that the representations of similar samples have a high degree of similarity. In order to take advantage of this important similarity information, this paper proposes a new local knowledge-based collaborative representation model for image classification. Specifically, certain adjacent training samples of the testing image should be determined firstly, and then the representations of these neighborhoods can be applied to guide the coefficients of the testing samples to be more discriminative. Further, we derive a robust version of the proposed method to treat the face recognition with occlusions or corruptions. Extensive experiments are carried out to show the superiority of the proposed method over other state-of-the-art classifiers on various image recognition tasks.
关键词： supervised learning,image recognition,robustness,Collaborative representation,local consistency
更新于2025-09-23 15:19:57
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Gabor-Filtering-Based Probabilistic Collaborative Representation for Hyperspectral Image Classification
摘要： This paper presents Gabor-filtering-based probabilistic collaborative representation for hyperspectral image classification. Compared with the original collaborative representation classifier (CRC) and the CRC using Gabor features, the proposed classifier offers superior classification performance. The regularized versions of CRC using Gabor features have excellent classification performance; however, those classifiers have high computational cost. Experimental results show that the proposed approach can generate high classification accuracy with lower computational cost.
关键词： probabilistic,classification,Gabor filtering,collaborative representation,Hyperspectral imagery
更新于2025-09-09 09:28:46
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Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification
摘要： In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.
关键词： remote-sensing images,kernel space,collaborative representation,hybrid collaborative representation
更新于2025-09-04 15:30:14
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Hyperspectral Anomaly Detection Using Collaborative Representation With Outlier Removal
摘要： Recently, collaborative representation detector (CRD) has been popularly used for hyperspectral anomaly detection. For the original CRD, the least squares solution becomes more unstable when more classes, i.e., samples for anomaly detection are involved, and the detection error is likely to happen if the test pixel is an anomalous pixel and several samples from background are similar anomalous. In this paper, we propose a hyperspectral anomaly detection method that uses CRD with principal component analysis (PCA) for removing outlier (PCAroCRD). According to the different background modeling methods, global and local versions are proposed. In the proposed algorithm, the spatial-domain PCA is adopted to extract main pixel information of global/local background that will be used as samples for collaborative representation, and simultaneously the information of abnormal pixels in global/local background can be removed. Fewer useful samples can also keep the detection result stable. Experimental results indicate that the PCAroCRD outperforms the original CRD, kernel version of CRD, advanced CRD (CRDBORAD), morphology-based CRD, Global Reed–Xiaoli (RX) algorithm, and the Local RX.
关键词： hyperspectral imagery,target detection,collaborative representation (CR),PCA,Anomaly detection
更新于2025-09-04 15:30:14