- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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
-
Learning Dual Geometric Low-Rank Structure for Semisupervised Hyperspectral Image Classification
摘要： Most of the available graph-based semisupervised hyperspectral image classification methods adopt the cluster assumption to construct a Laplacian regularizer. However, they sometimes fail due to the existence of mixed pixels whose recorded spectra are a combination of several materials. In this paper, we propose a geometric low-rank Laplacian regularized semisupervised classifier, by exploring both the global spectral geometric structure and local spatial geometric structure of hyperspectral data. A new geometric regularized Laplacian low-rank representation (GLapLRR)-based graph is developed to evaluate spectral-spatial affinity of mixed pixels. By revealing the global low-rank and local spatial structure of images via GLapLRR, the constructed graph has the characteristics of spatial–spectral geometry description, robustness, and low sparsity, from which a more accurate classification of mixed pixels can be achieved. The proposed method is experimentally evaluated on three real hyperspectral datasets, and the results show that the proposed method outperforms its counterparts, when only a small number of labeled instances are available.
关键词： Dual geometric low-rank structure,mixed pixels,spectral-spatial affinity,hyperspectral image classification (HIC),support vector machine,semisupervised
更新于2025-09-23 15:23:52
-
Hyperspectral band selection for soybean classification based on information measure in FRS theory
摘要： Soybeans and soy foods have attracted widespread attention due to their health benefits. Special varieties of soybeans are in demand from soybean processing enterprises. Because of the advantage of rapid measurement with minimal sample preparation, hyperspectral imaging technology is used for classifying soybean varieties. Based on fuzzy rough set (FRS) theory, the research of hyperspectral band selection can provide the foundation for variety classification. The performance of band selection with Gaussian membership functions and triangular membership functions under various parameters were explored. Appropriate ranges of parameters were determined by the number of bands and mutual information of subsets relative to the decision. The effectiveness of the proposed algorithms was validated through experiments on soybean hyperspectral datasets by building two classification methods, namely Extreme Learning Machine and Random Forest. Compared with ranking methods, the proposed algorithm provides a promising improvement in classification accuracy by selecting highly informative bands. To further reduce the size of the subset, a post-pruning design was used. For the Gaussian membership function, a subset containing eight bands achieved an average accuracy of 99.11% after post-pruning. As well as classification accuracy, we explored stability of band selection algorithm under small perturbations. The band selection algorithm of the Gaussian membership function was more stable than that of the triangular membership function. The results showed that the information measure (IM) based band selection algorithm is effective at obtaining satisfactory classification accuracy and providing stable results under perturbations.
关键词： Soybean classification,Information measure,Band selection,Fuzzy rough set,Hyperspectral imaging
更新于2025-09-23 15:23:52
-
Segmented and non-segmented stacked denoising autoencoder for hyperspectral band reduction
摘要： Hyperspectral image (HSI) analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information. Existing band reduction (BR) methods have the capability to reveal the nonlinear properties exhibited in the data but at the expense of losing its original representation. To cope with the said issue, an unsupervised non-linear segmented and non-segmented stacked denoising autoencoder (UDAE)-based BR method is proposed. Our aim is to find an optimal mapping and construct a lower-dimensional space that has a similar structure to the original data with least reconstruction error. The proposed method first confronts the original HS data into smaller regions in the spatial domain and then each region is processed by UDAE individually. This results in reduced complexity and improved efficiency of BR for classification. Our experiments on publicly available HS datasets with various types of classifiers demonstrate the effectiveness of UDAE method which equates favorably with other state-of-the-art dimensionality reduction and BR methods.
关键词： Autoencoder (AE),Hyperspectral imaging (HSI),Classification,Clustering,Band reduction (BR)
更新于2025-09-23 15:23:52
-
Estimation of the number of endmembers in hyperspectral data using a weight-sequence geometry method
摘要： The terrestrial reflection or emission spectrum obtained by the remote sensor is recorded in units of pixels. In most cases, a pixel usually contains many types of terrains. This pixel is a mixed pixel, and each of the terrains in the mixed pixels is called 'endmember'. Estimating the number of endmembers is a significant step in many hyperspectral data mining techniques, such as target classification and endmember extraction. The paper proposes a separative detection method by the use of a weight-sequence geometry to estimate the number of endmembers. This method projects the spectral matrix into the orthogonal subspace by eigenvalue decomposition at first. Then, on the basis of the normalized eigenvalue sequence, the separative detection method innovatively uses a geometric criterion to find the separation point between the main factors and minor factors. Finally, the number of endmembers is determined by the sequence of the 'separation point'. Validation through a series of simulated and real hyperspectral data, it indicates that the proposed method can accurately and rapidly detect the number of endmembers in the hyperspectral data without any prior information. In addition, the new method is also applicable to the ultra-high resolution remote spectral data in the future.
关键词： number of endmembers,Hyperspectral data,separative detection method,hyperspectral unmixing
更新于2025-09-23 15:22:29
-
Use of A Portable Camera for Proximal Soil Sensing with Hyperspectral Image Data
摘要： In soil proximal sensing with visible and near-infrared spectroscopy, the currently available hyperspectral snapshot camera technique allows a rapid image data acquisition in a portable mode. This study describes how readings of a hyperspectral camera in the 450–950 nm region could be utilised for estimating soil parameters, which were soil organic carbon (OC), hot-water extractable-C, total nitrogen and clay content; readings were performed in the lab for raw samples without any crushing. As multivariate methods, we used PLSR with full spectra (FS) and also combined with two conceptually different methods of spectral variable selection (CARS, “competitive adaptive reweighted sampling” and IRIV, “iteratively retaining informative variables”). For the accuracy of obtained estimates, it was beneficial to use segmented images instead of image mean spectra, for which we applied a regular decomposing in sub-images all of the same size and k-means clustering. Based on FS-PLSR with image mean spectra, obtained estimates were not useful with RPD values less than 1.50 and R2 values being 0.51 in the best case. With segmented images, improvements were marked for all soil properties; RPD reached values ≥ 1.68 and R2 ≥ 0.66. For all image data and variables, IRIV-PLSR slightly outperformed CARS-PLSR.
关键词： spectral variable selection,hyperspectral snapshot camera,partial least squares regression,multivariate calibration,hyperspectral imaging,proximal soil sensing
更新于2025-09-23 15:22:29
-
[IEEE 2018 International Conference on Communication and Signal Processing (ICCSP) - Chennai (2018.4.3-2018.4.5)] 2018 International Conference on Communication and Signal Processing (ICCSP) - A Comparative Analysis of Total Variation and Least Square Based Hyperspectral Image Denoising Methods
摘要： Hyperspectral image (HSI) with high spectral resolution will be always degraded by the noise accumulation. Therefore, image denoising is a fundamental preprocessing technique which improves the precision of successive processes like image classification, unmixing etc. In this paper, we compare least square (LS) weighted regularization in spectral domain with spatial least square and total variation (TV) denoising techniques. These methods are experimented on real, and noise simulated hyperspectral image datasets. The contrast and edges of the image are well preserved in the spectral LS. The image contrast varies in spatial LS, and edge informations are lost in TV. The experimental results show that, the spectral LS is superior to other two techniques in terms of visual interpretation, Signal-to-Noise Ratio (SNR) and Structural Similarity (SSIM) Index.
关键词： IBBC,SNR,Least Square,Hyperspectral Image,Denoising,Spectral domain,Total Variation,SSIM
更新于2025-09-23 15:22:29
-
[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 - Sparse and Smooth Feature Extraction for Hyperspectral Imagery
摘要： In this paper, a hyperspectral feature extraction (FE) method called sparse and smooth low-rank analysis (SSLRA) is proposed. First, we propose a new low-rank model for hyperspectral images (HSIs). In the new model, HSI is decomposed into smooth and sparse unknown features which live in an unknown orthogonal subspace. Then, the sparse and smooth features are simultaneously estimated using a non-convex constrained penalized cost function. In the experiments, SSLRA is applied on a real HSI and the smooth features extracted are used for the HSI classification. The results confirm improvements in classification accuracies compared to state-of-the-art FE methods.
关键词： regularization,Feature extraction,sparsity,low-rank model,total variation,hyperspectral image
更新于2025-09-23 15:22:29
-
[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 - Extraction of Structural and Mineralogical Features from Hyperspectral Drill-Core Scans
摘要： For vein hosted mineralization such as encountered in porphyry systems, the documentation of the main alteration assemblages associated with specific vein generations is essential in understanding the geometry of the mineralized body. Hence, mineralogical and structural information are highly relevant for characterizing the system. In this paper, we present an approach for the extraction of both mineralogical and structural information from hyperspectral scans. We propose a parallel framework which includes a typical mineral mapping technique for the extraction of mineralogical information as well as a ridge detection method, for the extraction of veins, applied on mineral abundance maps. In the proposed framework, the abundance maps are obtained from hyperspectral VNIR-SWIR drill-core scans using a linear spectral unmixing technique. Drill cores hosting porphyry stockwork type mineralization are used for the evaluation of the proposed technique and the experimental results show that the method offers a tool for accurately characterizing the mineralized body.
关键词： Core scanning,feature extraction,hyperspectral imaging,mineral mapping,image segmentation
更新于2025-09-23 15:22:29
-
[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 - Adaptive Hyperspectral Mixed Noise Removal
摘要： This paper proposes a new denoising method for hyperspectral images (HSIs) corrupted by mixtures (in a statistical sense) of stripe noise, Gaussian noise, and impulsive noise. The proposed method has three distinctive features: 1) it exploits the intrinsic characteristics of HSIs, namely, low-rank and self-similarity; 2) the observation noise is assumed to be additive and modeled by a mixture of Gaussian (MoG) densities; 3) the inference is performed with an expectation maximization (EM) algorithm, which, in addition to the clean HSI, also estimates the mixture parameters (posterior probability of each mode and variances). Comparisons of the proposed method with state-of-the-art algorithms provide experimental evidence of the effectiveness of the proposed denoising algorithm.
关键词： expectation maximization,low-rank,mixed noise,self-similarity,Denoising,mixture of Gaussians,hyperspectral images
更新于2025-09-23 15:22:29
-
Constrained Nonnegative Tensor Factorization for Spectral Unmixing of Hyperspectral Images: A Case Study of Urban Impervious Surface Extraction
摘要： In recent years, a new genre of hyperspectral unmixing methods based on nonnegative matrix factorization (NMF) have been proposed. Unlike traditional spectral unmixing methods, the NMF-based hyperspectral unmixing methods no longer depend on pure pixels in the original image. The NMF is based on linear algebra, which requires that the hyperspectral data cube is converted from 3-D cube to a 2-D matrix. Due to this conversion, the spatial information in the relative positions of the pixels is lost. With the emergence of multilinear algebra, the tensorial representation of hyperspectral imagery that preserves spectral and spatial information has become popular. The tensor-based spectral unmixing was first realized in 2017 using the matrix-vector nonnegative tensor factorization (MVNTF) decomposition. Using the construction of MVNTF spectral unmixing, this letter proposes to integrate three additional constraints (sparseness, volume, and nonlinearity) to the cost function. As we show in this letter, we found that the three constraints greatly improved the impervious surface area fraction/classification results. The constraints also shortened the processing time.
关键词： hyperspectral imagery,spectral unmixing,Constraints,nonnegative tensor factorization
更新于2025-09-23 15:22:29