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[Communications in Computer and Information Science] Advances in Signal Processing and Intelligent Recognition Systems Volume 968 (4th International Symposium SIRS 2018, Bangalore, India, September 19–22, 2018, Revised Selected Papers) || Pre-processed Hyperspectral Image Analysis Using Tensor Decomposition Techniques
摘要： Hyperspectral remote sensing image analysis has always been a challenging task and hence there are several techniques employed for exploring the images. Recent approaches include visualizing hyperspectral images as third order tensors and processing using various tensor decomposition methods. This paper focuses on behavioural analysis of hyperspectral images processed with various decompositions. The experiments includes processing raw hyperspectral image and pre-processed hyperspectral image with tensor decomposition methods such as, Multilinear Singular Value Decomposition and Low Multilinear Rank Approximation technique. The results are projected based on relative reconstruction error, classification and pixel reflectance spectrums. The analysis provides correlated experimental results, which emphasizes the need of pre-processing for hyperspectral images and the trend followed by the tensor decomposition methods.
关键词： Low Multilinear Rank Approximation,Remote sensing image,Pixel reflectance spectrums,Multilinear Singular Value Decomposition,Relative reconstruction error,Tensor decomposition
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Salient Object Detection Via Double Sparse Representations Under Visual Attention Guidance
摘要： This paper introduces a novel method for salient object detection from the perspective of sparse representation under visual attention guidance. After pretreatment and regional analysis with eye fixation detection and multi scale segmentation, regions that are used to make up the foreground and background dictionaries are respectively selected by sorting the visual attraction level of all image regions. For saliency measurement, the reconstruction errors instead of common local and global contrasts are used as the saliency indicator, which is expected to improve the object integrity. In addition, the multi scale workflow is conductive to enhance the robustness for objects of different sizes. The proposed method was compared to six state-of-the-art saliency detection methods using three benchmark datasets, and it was confirmed to have more favorable performance in the detection of multiple objects as well as maintaining the integrity of the object area.
关键词： Salient object detection,visual attention guidance,reconstruction error,sparse representation
更新于2025-09-10 09:29:36