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[IEEE 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) - Guadalajara, Jalisco, Mexico (2018.11.14-2018.11.16)] 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) - Convolutional Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Images
摘要： The recent impulse in development of artificial intelligence (AI) methodologies has simplified the application of this in multiple research areas. This simplification was not favorable before, due to the limitations in dimensionality, processing time, computational resources, among others. Working with multispectral remote sensing (RS) images, in an artificial neural network (NN) was quite complex. Due the methods used required millions of processes that took a long time to be executed and produce competitive results compared with the state of the art (SoA). Deep learning (DL) strategies have been applied to alleviate these limitations and have greatly improved the use of neural networks. Therefore, this paper presents the analysis of DL-NNs to perform semantic segmentation of multispectral RS images. Images are captured by the constellation of satellites Sentinel-2 from the European Space Agency. The objective of this research is to classify each pixel of a scene into five categories: 1-vegetation, 2-soil, 3-water, 4-clouds and 5-cloud shadows. The selection of spectral bands for the formation of input datasets for segmentation of these classes is very important. The spectral signatures of each material aid to discern among several classes. Results presented in this work, show that the AI strategy proposed offer better accuracy segmentation than other methods of the SoA in competitive processing time.
关键词： semantic segmentation,Convolutional neural networks,remote sensing,multispectral images
更新于2025-09-19 17:15:36
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Nonlocal Coupled Tensor CP Decomposition for Hyperspectral and Multispectral Image Fusion
摘要： Hyperspectral (HS) super-resolution, which aims at enhancing the spatial resolution of hyperspectral images (HSIs), has recently attracted considerable attention. A common way of HS super-resolution is to fuse the HSI with a higher spatial-resolution multispectral image (MSI). Various approaches have been proposed to solve this problem by establishing the degradation model of low spatial-resolution HSIs and MSIs based on matrix factorization methods, e.g., unmixing and sparse representation. However, this category of approaches cannot well construct the relationship between the high-spatial-resolution (HR) HSI and MSI. In fact, since the HSI and the MSI capture the same scene, these two image sources must have common factors. In this paper, a nonlocal tensor decomposition model for hyperspectral and multispectral image fusion (HSI-MSI fusion) is proposed. First, the nonlocal similar patch tensors of the HSI are constructed according to the MSI for the purpose of calculating the smooth order of all the patches for clustering. Then, the relationship between the HR HSI and the MSI is explored through coupled tensor canonical polyadic (CP) decomposition. The fundamental idea of the proposed model is that the factor matrices in the CP decomposition of the HR HSI’s nonlocal tensor can be shared with the matrices factorized by the MSI’s nonlocal tensor. Alternating direction method of multipliers is used to solve the proposed model. Through this method, the spatial structure of the MSI can be successfully transferred to the HSI. Experimental results on three synthetic data sets and one real data set suggest that the proposed method substantially outperforms the existing state-of-the-art HSI-MSI fusion methods.
关键词： nonlocal tensor,multispectral images (MSIs),Coupled canonical polyadic (CP) decomposition,data fusion,hyperspectral images (HSIs)
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC) - Xi'an, China (2019.6.12-2019.6.14)] 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC) - A Non-Ohmic Normally-off GaN RB-MISHEMT Featuring a Gate-Controlled Schottky Tunnel Junction
摘要： Due to the limited number of spectral channels in multispectral remote sensing images, change information, especially the multiclass changes, may be insuf?ciently represented, resulting in inaccurate detection of changes. In this paper, we propose to use unsupervised band expansion techniques to generate arti?cial spectral and spatial bands to enhance the change representation and discrimination for change detection (CD) from multispectral images. In particular, in the proposed approach, two simple nonlinear functions, i.e., multiplication and division, are applied for spectral expansion. Multiscale morphological reconstruction is used to extend the band spatial information. The expanded band sets are then used and validated in three popular unsupervised CD techniques for solving a multiclass CD problem. Experimental results obtained on three real bitemporal multispectral remote sensing datasets con?rm the effectiveness of the proposed approach.
关键词： Change detection (CD),remote sensing,nonlinear band expansion,change vector analysis,multitemporal analysis,multispectral images,dimensionality expansion
更新于2025-09-11 14:15:04