- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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 - Low-Complexity Hyperspectral Image Compression Using Folded PCA and JPEG2000
摘要： Hyperspectral image compression by PCA and JPEG2000 can provide excellent rate distortion performance while preserving essential information for a successive application, e.g., classification tasks. However, for onboard applications, PCA suffers from high computational complexity and large memory requirements due to the eigen-analysis of high-dimensional covariance matrix. Therefore, a computationally more efficient analysis, namely Folded Principal Component Analysis (FPCA) is adopted to perform dimension reduction and combined with JPEG2000 for compression. In FPCA, the spectral vector of hyperspectral pixels is folded into a matrix to compute covariance matrix, by which the dimension of covariance matrix is highly reduced. As a result, both computational complexity and memory requirement in subsequent eigen-analysis is reduced. Experimental results demonstrate that the proposed FPCA+JPEG2000 based compression scheme outperforms existing PCA+JPEG2000 in terms of rate distortion and classification after de-compression.
关键词： Principal Component Analysis (PCA),Hyperspectral Images (HSI),Folded Principal Component Analysis (FPCA),Compression
更新于2025-09-10 09:29:36
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[IEEE 2018 International Conference on Machine Learning and Cybernetics (ICMLC) - Chengdu, China (2018.7.15-2018.7.18)] 2018 International Conference on Machine Learning and Cybernetics (ICMLC) - Spectral-Spatial Sparse Subspace Clustering Based On Three-Dimensional Edge-Preserving Filtering For Hyperspectral Image
摘要： Due to the 3-D property of raw HSI cubes, 3-D spectral-spatial ?lter becomes an effective way for extracting spectral and spatial signatures from HSI. In this paper, a new spectral-spatial sparse subspace clustering framework based on 3-D edge-preserving ?ltering is proposed to improve the clustering accuracy of HSI. First, the initial sparse coef?cient matrix is obtained in the s-parse representation process of the classical SSC model. Then, a 3-D edge-preserving ?ltering is conducted on the initial sparse coef?cient matrix to get a more accurate one, which is used to build the similarity graph. Finally, the clustering result of H-SI data is achieved by employing the spectral clustering algorithm to the similarity graph. Speci?cally, the ?ltered matrix can not only capture the spectral-spatial features but the inten-sity differences. Experimental results demonstrate the poten-tial of including the proposed 3-D edge-preserving ?ltering in-to the SSC framework can improve the clustering accuracy.
关键词： Hyperspectral images (HSIs),Sparse subspace clustering (SSC),3-D edge-preserving ?lters (3-D EPFs),Intensity differences
更新于2025-09-10 09:29:36
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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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Feature extraction from hyperspectral images using learned edge structures
摘要： In this letter, a novel edge-preserving filtering based approach is proposed for feature extraction of hyperspectral images, which consists of the following steps. First, the dimension of the hyperspectral image is reduced with an averaging based method. Then, the resulting features are obtained by performing edge-preserving filtering on the dimension reduced image, in which a learned edge detection map serves as one of the major cues in the filtering process. The advantage of the proposed method is that it makes full use of the learned edge information in the feature extraction process, and thus, able to improve the performance with respect to other traditional feature extraction methods. Experiments on two real hyperspectral data sets demonstrate the outstanding performance of the proposed method especially when the number of training samples is limited.
关键词： edge-preserving filtering,feature extraction,hyperspectral images,learned edge structures
更新于2025-09-09 09:28:46
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Hyperspectral Unmixing with Bandwise Generalized Bilinear Model
摘要： Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.
关键词： alternative direction method of multipliers (ADMM),bandwise generalized bilinear model (BGBM),hyperspectral images (HSIs),additive white Gaussian noise (AWGN),mixed noise
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 - Weed Classification in Hyperspectral Remote Sensing Images Via Deep Convolutional Neural Network
摘要： Automatic weed detection and mapping are critical for site-speci?c weed control in order to reduce the cost of farming as well as the impact of herbicides on human health. In this paper, we investigate patch-based weed identi?cation using hyperspectral images. Convolutional Neural Network (CNN) is evaluated and compared with the Histogram of Oriented Gradients (HoG) for this purpose. Suitable patch sizes are investigated. The limitation of RGB imagery is demonstrated. The experimental results indicate that the overall accuracy of the weed classi?cation using CNN increases with the increasing number of bands used. With more bands, CNN extracts more powerful and discriminative features and leads to improved classi?cation as compared to the traditional HoG feature extraction method. The computational load of CNN, however, is slightly increased with the increasing number of bands.
关键词： Histogram of Oriented Gradients (HoG),weed mapping,Hyperspectral images,Convolutional Neural Network (CNN)
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 - Subspace Multinomial Logistic Regression Ensemble for Classification of Hyperspectral Images
摘要： Exploiting multiple complementary classifiers in an ensemble framework has shown to be effective for improving hyperspectral image classification results, specially when the training samples are limited. With a different principle and based on this assumption that hyperspectral feature vectors effectively lie in a low-dimensional subspace, the subspace-based techniques have shown great classification performance. In this work, we propose a new ensemble method for accurate classification of hyperspectral images, which exploits the concept of subspace projection. For this purpose, we extend the subspace multinomial logistic regression classifier (MLRsub) to learn from multiple random subspaces for each class. More specifically, we impose diversity in constructing MLRsub by randomly selecting bootstrap samples from the training set and subsets of the original hyperspectral feature space, which lead to generate different class subspace features. Experimental results, conducted on two real hyperspectral datasets, indicate that the proposed method provides significant classification results in comparison with other state-of-the-art approaches.
关键词： Hyperspectral images,subspace multinomial logistic regression,ensemble-based approaches,remote sensing,classification
更新于2025-09-09 09:28:46
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Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation
摘要： Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI’s discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art.
关键词： Superpixel segmentation,Hyperspectral images,Denoising,PSSV
更新于2025-09-04 15:30:14
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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
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Comprehensive Remote Sensing || Advanced Feature Extraction for Earth Observation Data Processing
摘要： The Earth is a highly complex and dynamic network system, and in the last few hundred years, human activities have precipitated many important changes. It goes without saying that the biggest challenge that we, as scientists, are facing nowadays is to quantify, predict, and understand this system’s behavior. For example, land and vegetation monitoring has deep societal, environmental, and economical implications, especially with the rapidly growing demand of biofuels and food. We need data and models to make inferences on the system. These models should provide not only predictions but also qualitative explanations about when, where, and how much the variables impact the observations.
关键词： Earth observation,kernel methods,hyperspectral images,principal curves,remote sensing,deep learning,feature extraction
更新于2025-09-04 15:30:14