- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information
摘要： Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into account both spectral and spatial information based on mutual information. We characterise the spatial information by the texture features extracted from the grey level cooccurrence matrix (GLCM); we use Homogeneity, Contrast, Correlation and Energy. For classification, we use support vector machine (SVM). The experiments are performed on three well-known hyperspectral benchmark datasets. The proposed algorithm is compared with the state of the art methods. The obtained results of this fusion show that our method outperforms the other approaches by increasing the classification accuracy in a good timing. This method may be improved for more performance.
关键词： hyperspectral images,spectral and spatial features,classification,SVM,mutual information,GLCM,grey level cooccurrence matrix,support vector machine
更新于2025-09-23 15:21:21
-
MCK-ELM: multiple composite kernel extreme learning machine for hyperspectral images
摘要： Multiple kernel (MK) learning (MKL) methods have a significant impact on improving the classification performance. Besides that, composite kernel (CK) methods have high capability on the analysis of hyperspectral images due to making use of the contextual information. In this work, it is aimed to aggregate both CKs and MKs autonomously without the need of kernel coefficient adjustment manually. Convex combination of predefined kernel functions is implemented by using multiple kernel extreme learning machine. Thus, complex optimization processes of standard MKL are disposed of and the facility of multi-class classification is profited. Different types of kernel functions are placed into MKs in order to realize hybrid kernel scenario. The proposed methodology is performed over Pavia University, Indian Pines, and Salinas hyperspectral scenes that have ground-truth information. Multiple composite kernels are constructed using Gaussian, polynomial, and logarithmic kernel functions with various parameters, and then the obtained results are presented comparatively along with the state-of-the-art standard machine learning, MKL, and CK methods.
关键词： Multiple kernel learning,Composite kernels,Hybrid kernels,Extreme learning machines,Hyperspectral images
更新于2025-09-19 17:15:36
-
Scalable Hardware-Based On-Board Processing for Run-Time Adaptive Lossless Hyperspectral Compression
摘要： Hyperspectral data processing is a computationally intensive task that is usually performed in high-performance computing clusters. However, in remote sensing scenarios, where communications are expensive, a compression stage is required at the edge of data acquisition before transmitting information to ground stations for further processing. Moreover, hyperspectral image compressors need to meet minimum performance and energy-efficiency levels to cope with the real-time requirements imposed by the sensors and the available power budget. Hence, they are usually implemented as dedicated hardware accelerators in expensive space-grade electronic devices. In recent years though, these devices have started to coexist with low-cost commercial alternatives in which unconventional techniques, such as run-time hardware reconfiguration are evaluated within research-oriented space missions (e.g., CubeSats). In this paper, a run-time reconfigurable implementation of a low-complexity lossless hyperspectral compressor (i.e., CCSDS 123) on a commercial off-the-shelf device is presented. The proposed approach leverages an FPGA-based on-board processing architecture with a data-parallel execution model to transparently manage a configurable number of resource-efficient hardware cores, dynamically adapting both throughput and energy efficiency. The experimental results show that this solution is competitive when compared with the current state-of-the-art hyperspectral compressors and that the impact of the parallelization scheme on the compression rate is acceptable when considering the improvements in terms of performance and energy consumption. Moreover, scalability tests prove that run-time adaptation of the compression throughput and energy efficiency can be achieved by modifying the number of hardware accelerators, a feature that can be useful in space scenarios, where requirements change over time (e.g., communication bandwidth or power budget).
关键词： dynamic and partial reconfiguration,FPGAs,Data compression,high-performance embedded computing,on-board processing,hyperspectral images
更新于2025-09-19 17:15:36
-
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
-
[IEEE 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Amsterdam, Netherlands (2019.9.24-2019.9.26)] 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Knowledge Transfer via Convolution Neural Networks for Multi-Resolution Lawn Weed Classification
摘要： Weed identi?cation and classi?cation are essential and challenging tasks for site-speci?c weed control. Object-based image analysis making use of spatial information is adopted in this study for the weed classi?cation because the spectral similarity between the weeds and crop is high. With the availability of a wide range of sensors, it is likely to capture weed imagery at various altitudes and with different speci?cations of the sensor. In this paper, we propose a novel method using transfer learning to deal with multi-resolution images from various sensors via Convolutional Neural Networks (CNN). CNN trained for a typical image data set and the trained weights are transferred to other data sets of different resolutions. In this way, the new data sets can be classi?ed by ?ne-tuning the network using a small number of training samples, which reduces the need of big data to train the model. To avoid over-?tting during the ?ne-tuning, small deep learning architecture is proposed and investigated using the parameters of the initial layers of pre-trained model. The sizes of training samples are investigated for their impact on the performance of ?ne-tuning. Experiments were conducted with ?eld data, which show that the proposed method outperforms the direct training method in terms of recognition accuracy and computation cost.
关键词： Hyperspectral images,Resolution,Convolutional Neural Network (CNN),Weed Mapping,Transfer Learning
更新于2025-09-12 10:27:22
-
Hyperspectral images: a qualitative approach to evaluate the chemical profile distribution of Ca, K, Mg, Na and P in edible seeds employing laser-induced breakdown spectroscopy
摘要： In the present study, laser-induced breakdown spectroscopy (LIBS) combined with chemometric tools was used to investigate the metal composition in nine seed samples. The samples were directly analyzed, and a matrix with 9 rows and 9 columns (81 points) and 10 consecutive pulses were analyzed in each point. A total of 810 emission spectra were collected from 186 to 1042 nm from the surface and bulk of the sample. The dataset was normalized by Euclidian norm and principal component analysis (PCA) was used for the initial exploratory investigation. Calcium, Mg, Na, K and P were mainly identified in all samples; the distribution of metals in these samples is not completely homogeneous, however, i.e., composition of the elements change from one layer to another. This fact can be probably related to the absorption capability of nutrients resulting from different factors such as soil characteristics, physiology of the plant, water source composition and fertilizers which can influence the distribution of the elements in different seeds. To confirm the elements observed by LIBS, the samples were digested using microwave-assisted digestion, and Ca, K, Mg, Na and P were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES). In addition, some minor nutrients such as S and Zn were also investigated and the relationships between elements were observed through the Pearson correlation graph, and some of them, such as Mg and Na, P and Na, S and P, S and Zn, are extremely correlated; it means that, for example, when the concentration of Mg increases, that of Na also increases.
关键词： Chemometric tools,Laser-induced breakdown spectroscopy,Edible seeds,Chemical profile distribution,Hyperspectral images
更新于2025-09-12 10:27:22
-
Unsupervised Feature Extraction in Hyperspectral Images Based on Wasserstein Generative Adversarial Network
摘要： Feature extraction (FE) is a crucial research area in hyperspectral image (HSI) processing. Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral features, DL-based FE methods have shown great potentials for HSI processing. However, most of the DL-based FE methods are supervised, and the training of them suffers from the absence of labeled samples in HSIs severely. The training issue of supervised DL-based FE methods limits their application on HSI processing. To address this issue, in this paper, a novel modified generative adversarial network (GAN) is proposed to train a DL-based feature extractor without supervision. The designed GAN consists of two components, which are a generator and a discriminator. The generator can focus on the learning of real probability distributions of data sets and the discriminator can extract spatial–spectral features with superior invariance effectively. In order to learn upsampling and downsampling strategies adaptively during FE, the proposed generator and discriminator are designed based on a fully deconvolutional subnetwork and a fully convolutional subnetwork, respectively. Moreover, a novel min–max cost function is designed for training the proposed GAN in an end-to-end fashion without supervision, by utilizing the zero-sum game relationship between the generator and discriminator. Besides, the proposed modified GAN replaces the original Jensen–Shannon divergence with the Wasserstein distance, aiming to mitigate the unstability and difficulty of the training of GAN frameworks. Experimental results on three real data sets validate the effectiveness of the proposed method.
关键词： Convolutional neural network (CNN),hyperspectral images (HSIs),feature extraction (FE),generative adversarial network (GAN)
更新于2025-09-11 14:15:04
-
[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 - Active Manifold Learning for Hyperspectral Image Classification
摘要： Hyperspectral image classification via supervised approaches is often affected by the high dimensionality of the spectral signatures and the relative scarcity of training samples. Dimensionality reduction (DR) and active learning (AL) are two techniques that have been investigated independently to address these two problems. Considering the nonlinear property of the hyperspectral data and the necessity of applying AL adaptively, in this paper, we propose to integrate manifold and active learning into a unique framework to alleviate the aforementioned two issues simultaneously. In particular, supervised Isomap is adopted for DR for the training set, followed by an out-of-sample extension approach to project the large amount of unlabeled samples into previously learned embedding space. Finally, AL is performed in conjunction with k-nearest neighbor (kNN) classification in the embedded feature space. Experiments on a benchmark hyperspectral dataset illustrate the effectiveness of the proposed framework in terms of DR and the feature space refinement.
关键词： classification,manifold learning,hyperspectral images,Active learning,out-of-sample extension
更新于2025-09-10 09:29:36
-
[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 - Hyperspectral Image Classification Based on Spectral Mixture Analysis for Crop Type Determination
摘要： For the application of agricultural area, remote sensing techniques were studied and applied for its advantages for continuous and quantitative monitoring. Especially, hyperspectral images have been studied for the precise agriculture since they provide chemical and physical information of vegetation. In this study, we analyzed crop types using hyperspectral image data collected by a ground scanner. Spectral mixture analysis, which is widely used for processing hyperspectral images, was adopted for the crop discrimination. Endmember extraction algorithms used in this study were N-FINDR, Vertex Component Analysis (VCA), and Simplex Identification via variable Splitting and Augmented Lagrangian (SISAL), and classification was processed using fully constrained linear spectral unmixing (FCLSU). This study presents the application of spectral mixture analysis for hyperspectral scanner data at canopy level and optimal endmember extraction algorithms for different crop types for precise agriculture.
关键词： Hyperspectral images,crop types,classification,spectral mixture analysis
更新于2025-09-10 09:29:36
-
[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 - The Need for Multi-Source, Multi-Scale Hyperspectral Imaging to Boost Non-Invasive Mineral Exploration
摘要： The high demand for raw materials in our post-industrial societies contrasts the increasing difficulties to find new mineral deposits. In Europe, accessible and high-grade deposits are mostly exhausted or currently mined. Hence, future exploration must focus on the remaining, more remote locations or penetrate much deeper into the Earth's crust. Sustaining mining activities in Europe would allow the development of key technologies but also sustainable and ethical production of technological metals. Thus, we suggest to focus research on advances in multi-scale and multi-sensor remote sensing-based Earth integration techniques. The scale should range from satellite to air- and drone-borne systems and include ground validation. Multi-sensor downscaling methods involving SAR and optical data are particularly promising. We demonstrate that the integration with other sensors and/or measures such as geophysical/geochemical data as well as non-conventional remote sensing features such as textures and geometries are of interest. Thus, ultimately, our objective is to boost the competitiveness, growth, sustainability and attractiveness of the raw materials sector in Europe. While we focus on the raw materials sector as it is currently of strategic importance, the required methods are transferable to most environmental studies.
关键词： classification,ensemble based approaches,Mineral exploration,hyperspectral images,remote sensing
更新于2025-09-10 09:29:36