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过滤筛选
- 2018
- Fruit defects
- Jujube
- Principal component analysis
- Hyperspectral imaging
- hyperspectral images
- spectral and spatial features
- classification
- SVM
- mutual information
- GLCM
- Optoelectronic Information Science and Engineering
- Mohammed V University in Rabat
- Southern Taiwan University of Science and Technology
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[IEEE 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Bangalore (2018.2.9-2018.2.10)] 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Sparse Reconstruction of Hyperspectral Image using Bregman Iterations
摘要： Hyperspectral image processing plays an important role in satellite communication. Hyperspectral Image (HSI) processing requires very high ‘computational resources’ in terms of computational time and storage due to extremely large volumes of data collected by imaging spectrometers on-board the satellite. The bandwidth available to transmit the image data from satellite to the ground station is limited. As a result, Hyperspectral image compression is an active research area in the research community in past few years. The research work in the paper proposes a new scheme, Sparsification of HSI and reconstruction (SHSIR) for the reconstruction of hyperspectral image data acquired in Compressive sensing (CS) fashion. Compressed measurements similar to compressive sensing acquisition are generated using measurement matrices containing gaussian i.i.d entries. Now the reconstruction is solving the constrained optimization problem with non smooth terms. Adaptive Bregman iterations method of multipliers is used to convert the difficult optimization problem into a simple cyclic sequence problem. Experimental results from research work indicates that the proposed method performs better than the other existing techniques.
关键词： SHSIR algorithm,Hyperspectral image (HSI),Compressive sensing (CS)
更新于2025-09-23 15:22:29
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Discriminative Transfer Joint Matching for Domain Adaptation in Hyperspectral Image Classification
摘要： Domain adaptation, which aims at learning an accurate classifier for a new domain (target domain) using labeled information from an old domain (source domain), has shown promising value in remote sensing fields yet still been a challenging problem. In this letter, we focus on knowledge transfer between hyperspectral remotely sensed images in the context of land-cover classification under unsupervised setting where labeled samples are available only for the source image. Specifically, a discriminative transfer joint matching (DTJM) method is proposed, which matches source and target features in the kernel principal component analysis space by minimizing the empirical maximum mean discrepancy, performs instance reweighting by imposing an l2,1-norm on the embedding matrix, and preserves the local manifold structure of data from different domains and meanwhile maximizes the dependence between the embedding and labels. The proposed approach is compared with some state-of-the-art feature extraction techniques with and without using label information of source data. Experimental results on two benchmark hyperspectral data sets show the effectiveness of the proposed DTJM.
关键词： hyperspectral image classification,domain adaptation,Discriminative transfer joint matching (DTJM),manifold
更新于2025-09-23 15:22:29
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Hyperspectral Coastal Wetland Classification Based on a Multiobject Convolutional Neural Network Model and Decision Fusion
摘要： The phenomenon of spectral aliasing exists for coastal wetland object types, which leads to class mixing. This letter proposes a multiobject convolutional neural network (CNN) decision fusion classification method for hyperspectral images of coastal wetlands. This method adopts decision fusion based on fuzzy membership rules applied to single-object CNN classification to obtain higher classification accuracy. Experimental results demonstrate the effectiveness of the proposed method for the six object types, including water, tidal flat, reed, and other vegetation types. The overall accuracy of the decision fusion classification method based on fuzzy membership is 82.11%, which is 3.33% and 6.24% higher than those of single-object feature band CNN and support vector machine methods. The classification method based on multiobject CNN decision fusion inherits the characteristics of single-object feature bands of the CNN, making it a practical approach to image classification under the challenging conditions in which class mixing occurs.
关键词： decision fusion,convolutional neural network (CNN),hyperspectral image,Classification
更新于2025-09-23 15:22:29
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Nonconvex-sparsity and Nonlocal-smoothness Based Blind Hyperspectral Unmixing
摘要： Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data exploitation, aims to decompose mixed pixels into a collection of constituent materials weighted by the corresponding fractional abundances. In recent years, nonnegative matrix factorization (NMF) based methods have become more and more popular for this task and achieved promising performance. Among these methods, two types of properties upon the abundances, namely the sparseness and the structural smoothness, have been explored and shown to be important for blind HU. However, all of previous methods ignores another important insightful property possessed by a natural hyperspectral images (HSI), non-local smoothness, which means that similar patches in a larger region of an HSI are sharing the similar smoothness structure. Based on previous attempts on other tasks, such a prior structure reflects intrinsic configurations underlying a HSI, and is thus expected to largely improve the performance of the investigated HU problem. In this paper, we firstly consider such prior in HSI by encoding it as the non-local total variation (NLTV) regularizer. Furthermore, by fully exploring the intrinsic structure of HSI, we generalize NLTV to non-local HSI TV (NLHTV) to make the model more suitable for the bind HU task. By incorporating these two regularizers, together with a non-convex log-sum form regularizer characterizing the sparseness of abundance maps, to the NMF model, we propose novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NLHTV-LSRNMF), respectively. To solve the proposed models, an efficient algorithm is designed based on alternative optimization strategy (AOS) and alternating direction method of multipliers (ADMM). Extensive experiments conducted on both simulated and real hyperspectral data sets substantiate the superiority of the proposed approach over other competing ones for blind HU task.
关键词： log-sum penalty,non-negative matrix factorization,non-local total variation regularization,blind unmixing,Hyperspectral imaging
更新于2025-09-23 15:22:29
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An Outlier-insensitive Unmixing Algorithm with Spatially Varying Hyperspectral Signatures
摘要： Effective hyperspectral unmixing (HU) is essential to the estimation of the underlying materials’ signatures (endmember signatures) and their spatial distributions (abundance maps) from a given image (data) of a hyperspectral scene. Recently, investigating HU under the non-negligible endmember variability (EV) and outlier effects (OE) has drawn extensive attention. Some state-of-the-art works either consider EV or consider OE, but none of them considers both EV and OE simultaneously. In this paper, we propose a novel HU algorithm, referred to as the variability/outlier-insensitive multi-convex unmixing (VOIMU) algorithm, that is robust against both EV and OE. Considering two suitable regularizers, a nonconvex minimization problem is formulated for which the perturbed linear mixing model (PLMM) proposed by Thouvenin et al., is used for modeling EV, while OE is implicitly handled by applying a p quasi-norm to the data fitting with 0 < p < 1. Then we reformulate it into a multi-convex problem which is then solved by the block coordinate decent (BCD) method, with convergence guarantee by casting it into the block successive upper bound minimization (BSUM) framework. The proposed VOIMU algorithm can yield a stationary-point solution with convergence guarantee, together with some intriguing information of potential outlier pixels though outliers are neither physically modeled in the above problem nor detected in the algorithm operation. Finally, we provide some simulation results and experimental results using real data to demonstrate the efficacy and practical applicability of the proposed VOIMU algorithm.
关键词： block successive upper bound minimization (BSUM),endmember variability,alternating direction method of multipliers (ADMM),outlier effects,block coordinate decent (BCD) method,Hyperspectral imaging
更新于2025-09-23 15:22:29
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Partially Asynchronous Distributed Unmixing of Hyperspectral Images
摘要： So far, the problem of unmixing large or multitemporal hyperspectral data sets has been specifically addressed in the remote sensing literature only by a few dedicated strategies. Among them, some attempts have been made within a distributed estimation framework, in particular, relying on the alternating direction method of multipliers. In this paper, we propose to study the interest of a partially asynchronous distributed unmixing procedure based on a recently proposed asynchronous algorithm. Under standard assumptions, the proposed algorithm inherits its convergence properties from recent contributions in nonconvex optimization, while allowing the problem of interest to be efficiently addressed. Comparisons with a distributed synchronous counterpart of the proposed unmixing procedure allow its interest to be assessed on synthetic and real data. Besides, thanks to its genericity and flexibility, the procedure investigated in this paper can be implemented to address various matrix factorization problems.
关键词： partially asynchronous distributed estimation,Hyperspectral (HS) unmixing,nonconvex optimization
更新于2025-09-23 15:22:29
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[IEEE 2018 International Conference on Microwave and Millimeter Wave Technology (ICMMT) - Chengdu, China (2018.5.7-2018.5.11)] 2018 International Conference on Microwave and Millimeter Wave Technology (ICMMT) - Harmonic-Suppressed LTCC Bandpass Filter Using a New Feeding Scheme
摘要： Extreme-learning machines (ELM) have attracted significant attention in hyperspectral image classification due to their extremely fast and simple training structure. However, their shallow architecture may not be capable of further improving classification accuracy. Recently, deep-learning-based algorithms have focused on deep feature extraction. In this paper, a deep neural network-based kernel extreme-learning machine (KELM) is proposed. Furthermore, an excellent spatial guided filter with first-principal component (GFFPC) is also proposed for spatial feature enhancement. Consequently, a new classification framework derived from the deep KELM network and GFFPC is presented to generate deep spectral and spatial features. Experimental results demonstrate that the proposed framework outperforms some state-of-the-art algorithms with very low cost, which can be used for real-time processes.
关键词： spectral and spatial features,deep layer,kernel-based ELM,hyperspectral classification
更新于2025-09-23 15:22:29
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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 - Enrichment of the Satellitesceneontology with Hyperspectral Images/ Crops and Feature Vectors of Radiometric Indices
摘要： Semantic classification and annotation of satellite images are of great importance and require knowledge resources. The complexity of satellite scenes makes its classification and annotation hard tasks and we are still far from totally resolving the semantic gap problem. There are several knowledge resources such as semantic networks, taxonomies and ontologies. In this paper, we propose to enrich the Satellite Scene Ontology using real hyperspectral scenes, the USGS spectral library and the WordNet lexical database. The resulting ontology would be published online for further exploitation by researchers.
关键词： WordNet,Radiometric Indices,Satellite Scene Ontology,Ontology,Hyperspectral Images (HSI),Spectral Signature,USGS Spectral Library
更新于2025-09-23 15:22:29
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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 - Fusion of Hyperspectral and Panchromatic Images using Structure Tensor
摘要： In this paper, a new hyperspectral image fusion method with structure tensor is proposed. The proposed method utilizes PCA transformation to obtain the spatial details of HS image. Then, an image enhancement approach is applied to the PAN image to sharpen spatial information. Since structure tensor represents structure and spatial information, structure tensor is introduced to extract spatial details of the enhanced PAN image. Unlike traditional methods which extract details only from PAN image, the proposed method considers spatial details of the HS and PAN images simultaneously, and a weighted fusion method is presented to integrate spatial details of the two images to obtain complete spatial details. Finally, an injection gains matrix is constructed to reduce spectral and spatial distortion, and the fused image is generated by injecting the complete spatial information. Experimental results demonstrate that the proposed method obtains the excellent performance in both objective and subjective evaluations.
关键词： panchromatic image,hyperspectral image,weighted fusion,Structure tensor
更新于2025-09-23 15:22:29
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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 - EnMAP-Box 3 a free and open source Python plug-in for QGIS
摘要： The EnMAP-Box is a toolbox designed to process imaging spectroscopy data and particularly developed to handle data from the upcoming EnMAP (Environmental Mapping and Analysis Program) sensor. In near future, it will offer algorithms for user-defined Level-2a preprocessing, for advanced processing of spectral imagery as well as for EnMAP-specific product generation. It serves as a platform for sharing and distributing algorithms and methods among scientists and potential end-users. Starting with version 3.0 the EnMAP-Box is a free and open source (FOSS) Python plug-in for the geographic information system QGIS.
关键词： QGIS Plug-In,algorithm development,hyperspectral,FOSS,EnMAP
更新于2025-09-23 15:22:29