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Extended attribute profiles on GPU applied to hyperspectral image classification
摘要： Extended pro?les are an important technique for modelling the spatial information of hyperspectral images at different levels of detail. They are used extensively as a pre-processing stage, especially in classi?cation schemes. In particular, attribute pro?les, based on the application of morphological attribute ?lters to the connected components of the image, have been shown to provide very good results. In this paper we present a parallel implementation of the attribute pro?les in CUDA for multispectral and hyperspectral imagery considering the attributes area and standard deviation. The pro?le computation is based on the max-tree approach but without building the tree itself. Instead, a matrix-based data structure is used along with a recursive ?ooding (component merging) and ?lter process. Additionally, a previous feature extraction stage based on wavelets is applied to the hyperspectral image in order to extract the most valuable spectral information, reducing the size of the resulting pro?le. This scheme ef?ciently exploits the thousands of available threads on the GPU, obtaining a considerable reduction in execution time as compared to the OpenMP CPU implementation.
关键词： Remote sensing,Attribute pro?les,GPU,Real-time,Hyperspectral,Supervised classi?cation
更新于2025-09-23 15:22:29
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Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification
摘要： Hyperspectral and light detection and ranging (LiDAR) data fusion and classi?cation has been an active research topic, and intensive studies have been made based on mathematical morphology. However, matrix-based concatenation of morphological features may not be so distinctive, compact, and optimal for classi?cation. In this work, we propose a novel Coupled Higher-Order Tensor Factorization (CHOTF) model for hyperspectral and LiDAR data classi?cation. The innovative contributions of our work are that we model different features as multiple third-order tensors, and we formulate a CHOTF model to jointly factorize those tensors. Firstly, third-order tensors are built based on spectral-spatial features extracted via attribute pro?les (APs). Secondly, the CHOTF model is de?ned to jointly factorize the multiple higher-order tensors. Then, the latent features are generated by mode-n tensor-matrix product based on the shared and unshared factors. Lastly, classi?cation is conducted by using sparse multinomial logistic regression (SMLR). Experimental results, conducted with two popular hyperspectral and LiDAR data sets collected over the University of Houston and the city of Trento, respectively, indicate that the proposed framework outperforms the other methods, i.e., different dimensionality-reduction-based methods, independent third-order tensor factorization based methods, and some recently proposed hyperspectral and LiDAR data fusion and classi?cation methods.
关键词： attribute pro?les,classi?cation,hyperspectral remote sensing image (HSI),data fusion,light detection and ranging (LiDAR),coupled tensor factorization
更新于2025-09-19 17:13:59