- 标题
- 摘要
- 关键词
- 实验方案
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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 - Detection of Fusarium Wilt on Phalaenopsis Stem Base Region Using Band Selection Techniques
摘要： Phalaenopsis is a significant agriculture product with high economic value in Taiwan. However, the fusarium wilt causes Phalaenopsis leaves turning yellow, thinning, water loss, and finally died. This paper presents an emerging method to detect fusarium wilt on Phalaenopsis stem base. In order to build the detection models, the hyperspectral databases are generated form two statues of Phalaenopsis samples, which are health and disease sample. We applied band selection (BS) processing base on band prioritization (BP) and band de-correlation (BD) to extract the significant bands and eliminate the redundant bands. Then, three algorithms were used, orthogonal subspace projection (OSP), constrain energy minimization (CEM), and support vector machine (SVM) to detect the fusarium wilt.
关键词： OSP,SVM,Hyperspectral image,Phalaenopsis,Band selection,fusarium wilt,CEM
更新于2025-09-10 09:29:36
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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 - Urban Vegetation Mapping Using Hyperspectral Imagery and Spectral Library
摘要： The development and expansion of urbanized areas around the cities, brings new challenging issues about the organization, the monitoring, and the distribution of green spaces within the cities (e.g. grass, trees, shrubs, etc.). Indeed, these spaces brings better life quality for population and preserve biodiversity. This study, aims to 1) investigate the feasibility of urban vegetation mapping by species using multiband imagery and spectral libraries and to 2) determine at what scale the mapping is reliable (e.g. trees scale, group of trees scale, high/short vegetation scale).
关键词： spectral library,regularization,Hyperspectral,band selection,vegetation mapping
更新于2025-09-10 09:29:36
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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 - Selecting Band Subsets from Hyperspectral Image Through a Novel Evolutionary-Based Strategy
摘要： Hyperspectral dimensionality reduction by optimal band selection attracts wide attention recently because a few pivotal and physically meaningful bands can not only represent the whole image cube without losing effectiveness but also mitigate the computational burden. In this paper, we construct an efficient searching strategy based on the clonal selection principle to optimize a geometry-based criterion named maximum ellipsoid volume (MEV). The main contributions are two-fold: 1) a subtle relationship that can accelerate the calculation of the criterion and 2) an evolutionary strategy to relieve the heavy computational burden of obtaining the desired bands from numerous quality candidates. The experimental result on a real hyperspectral data demonstrates that the proposed method is effective.
关键词： maximum ellipsoid volume,Band selection,hyperspectral image,clonal selection principle
更新于2025-09-09 09:28:46
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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 - Hyperspectral Band Selection Using Pair-Wise Constraint and Band-Wise Correlation
摘要： In this paper, a novel supervised band selection (BS) method based on pair-wise constraint and band-wise correlation information is proposed for the dimension reduction of hyperspectral images. On the one hand, the band-wise correlation information, is used for selecting band-subset with lower redundancy and higher representation. This process is achieved by first partitioning all spectral bands into continuous groups and then calculate a band-wise correlation matrix within each group, which is used later for selecting bands of more representation and lower redundancy. On the other hand, pair-wise supervised information (i.e., whether a pair of labeled samples are from the same class) is exploited for selecting band-subsets to better discriminate different classes. That is, a few bands are adaptively chosen for each pair of labeled samples according to spectral-similarity, to ensure that the distance between samples from different classes is far and keep sample-pair from same class close. By the joint use of both pair-wise constraint information and band-wise correlation information, the proposed BS method can lead to select optimal band-subsets with low-redundancy, high-representation and high-discrimination. Experimental results demonstrate the effectiveness of the proposed BS method.
关键词： Band selection,Hyperspectral image,Pair-wise constraint,Band-wise correlation
更新于2025-09-09 09:28:46
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Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification
摘要： Hyperspectral image (HSI), with hundreds of narrow and adjacent spectral bands, supplies plentiful information to distinguish various land-cover types. However, these spectral bands ordinarily contain a lot of redundant information, leading to the Hughes phenomenon and an increase in computing time. As a popular dimensionality reduction technology, band/feature selection is indispensable for HSI classification. Based on improved subspace decomposition (ISD) and the artificial bee colony (ABC) algorithm, this paper proposes a band selection technique known as ISD-ABC to address the problem of dimensionality reduction in HSI classification. Subspace decomposition is achieved by calculating the correlation coefficients between adjacent bands and using the visualization result of the HSI spectral curve. The artificial bee colony algorithm is first applied to optimize the combination of selected bands with the guidance of ISD and maximum entropy (ME). Using the selected band subset, support vector machine (SVM) with five-fold cross validation is applied for HSI classification. To evaluate the effectiveness of the proposed method, experiments are conducted on two AVIRIS datasets (Indian Pines and Salinas) and a ROSIS dataset (Pavia University). Three indices, namely, overall accuracy (OA), average accuracy (AA) and kappa coefficient (KC), are used to assess the classification results. The experimental results successfully demonstrate that the proposed method provides good classification accuracy compared with six other state-of-the-art band selection techniques.
关键词： dimensionality reduction,band selection,Hyperspectral image,ABC algorithm,subspace decomposition
更新于2025-09-09 09:28:46
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[Smart Innovation, Systems and Technologies] Information Systems and Technologies to Support Learning Volume 111 (Proceedings of EMENA-ISTL 2018) || A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
摘要： Band selection is a great challenging task in the classi?cation of hyperspectral remotely sensed images HSI. This is resulting from its high spectral resolution, the many class outputs and the limited number of training samples. For this purpose, this paper introduces a new ?lter approach for dimension reduction and classi?cation of hyperspectral images using information theoretic (normalized mutual information) and support vector machines SVM. This method consists to select a minimal subset of the most informative and relevant bands from the input datasets for better classi?cation ef?ciency. We applied our proposed algorithm on two well-known benchmark datasets gathered by the NASA’s AVIRIS sensor over Indiana and Salinas valley in USA. The experimental results were assessed based on different evaluation metrics widely used in this area. The comparison with the state of the art methods proves that our method could produce good performance with reduced number of selected bands in a good timing.
关键词： Support vector machines,Classi?cation,Dimension reduction,Band selection,Hyperspectral images,Normalized mutual information
更新于2025-09-09 09:28:46
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Selection of Informative Spectral Bands for PLS Models to Estimate Foliar Chlorophyll Content Using Hyperspectral Reflectance
摘要： Partial least-squares (PLS) regression is a popular method for modeling chemical constituents from spectroscopic data and has been widely applied to retrieve leaf chemical components via hyperspectral remote sensing. However, one persistent challenge for applying the PLS regression is the selection of informative spectral bands among the vast array of acquired spectra. No consensus has been reached yet on how to select informative bands regardless of many techniques being proposed. In this paper, we have composited four individual data sets containing a total of 598 leaf samples from various species to evaluate four different band elimination/selection methods. Results revealed that the stepwise-PLS approach was optimal to estimate leaf chlorophyll content even under different spectral resolutions, from which informative bands were identified. Informative bands, in general, include bands inside the near-infrared (NIR), and in addition, one within the blue range and one within the red range. With such combinations, the PLS regression models meet the requirement for accurate leaf chlorophyll estimation. For most PLS regression models, their accuracies decreased with the reduction of spectral resolution, but the stepwise-PLS approach could consistently estimate the chlorophyll content at different spectral resolutions (with R2 ≥ 0.77 for resolutions < 20 nm). The findings, hence, provide valuable insights for selecting informative spectral bands for PLS analysis and lay a strong foundation for retrieving foliar biochemical content using hyperspectral remote sensing data.
关键词： Band selection,partial least squares (PLS),leaf pigments,hyperspectral reflectance
更新于2025-09-09 09:28:46
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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 - Towards Weakly Pareto Optimal: An Improved Multi-Objective Based Band Selection Method for Hyperspectral Imagery
摘要： Band selection refers to finding the most representative channels from hyperspectral images. Usually, certain objective functions are designed and combined via regularization terms. Owing to the parameters independence and the optimal solutions, multi-objective based methods have presented promising performance. However, the characteristics of the hyperspectral band selection problem make its range to be discrete. In this case, recently proposed weighted Tchebycheff based multi-objective band selection methods could only reach the weakly Pareto optimal, which would result in non-unique solutions. In this paper, we improve the decomposition process of the multi-objective based band selection method via a boundary intersection approach. Compared with weighted Tchebycheff decomposition, the proposed method is able to change the shape of the contour lines between Pareto Front and the ideal point, and this approach is particularly suitable for discrete-range problems. The effectiveness of our improvement is demonstrated by comparison experiments.
关键词： band selection,Multi-objective optimization,hyperspectral imagery
更新于2025-09-09 09:28:46
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Supervised band selection in hyperspectral images using single-layer neural networks
摘要： Hyperspectral images provide fine details of the scene under analysis in terms of spectral information. This is due to the presence of contiguous bands that make possible to distinguish different objects even when they have similar colour and shape. However, neighbouring bands are highly correlated, and, besides, the high dimensionality of hyperspectral images brings a heavy burden on processing and also may cause the Hughes phenomenon. It is therefore advisable to make a band selection pre-processing prior to the classification task. Thus, this paper proposes a new supervised filter-based approach for band selection based on neural networks. For each class of the data set, a binary single-layer neural network classifier performs a classification between that class and the remainder of the data. After that, the bands related to the biggest and smallest weights are selected, so, the band selection process is class-oriented. This process iterates until the previously defined number of bands is achieved. A comparison with three state-of-the-art band selection approaches shows that the proposed method yields the best results in 43.33% of the cases even with greatly reduced training data size, whereas the competitors have achieved between 13.33% and 23.33% on the Botswana, KSC and Indian Pines datasets.
关键词： supervised learning,neural networks,Hyperspectral images,band selection,filter-based approach
更新于2025-09-04 15:30:14