- 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
-
[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
-
[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 Land Use/Land Cover Classification Based on Feature Fusion Fusing Hyperspectral Image and Lidar Data
摘要： Hyperspectral images have been widely used in classification because of the abundant spectral information. But it can’t distinguish the objective with similar spectral character but different elevation. However, LiDAR data can obtain elevation information. Therefore, it will obtain better classification maps if fusing the two data. In recent years, CNN has attracted much attention due to its powerful ability to excavate the potential representation and features of the raw data. However, it’s difficult to distinguish the objects with different spectral information but similar surface character. Unlike CNN features, the traditional manual features, such as the normalized vegetation index (NDVI), have a certain characteristic expression significance. In order to consider both the semantic information of traditional manual features and the advanced features of CNN features, this paper proposes a fusion algorithm of hyperspectral and LiDAR fusion based on feature fusion. The proposed algorithm has achieved a good fusion classification effect on the MUUFL Gulfport Hyperspectral and LiDAR Data set.
关键词： convolutional neural network,land-use/land-cover classification,Hyperspectral,deep learning,feature fusion,LIDAR
更新于2025-09-09 09:28:46
-
[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 Mixed Denoising Via Subspace Low Rank Learning and BM4D Filtering
摘要： This paper proposes a novel mixed noise removal method via subspace low rank representation and BM4D filtering for hyperspectral imagery (HSI). The proposed method is based on the following two facts. The first one is that the spectra in each class of HSI lie in different low-rank subspace, that is, the HSI data could be decomposed into two sub-matrices with lower ranks in the framework of subspace low rank representation. The second one is that the spatial structures of HSI have the property of non-local self-similarity (NSS), and the NSS could be effectively exploited by BM4D filter with no additional parameters. The proposed model can be easily and effectively solved by splitting it into several sub-problems via the alternating direction method of multipliers (ADMM). Experimental results validate that the proposed method outperforms other state-of-the-art denoising methods for HSI.
关键词： BM4D,subspace low rank representation,iterative learning,Hyperspectral mixed denoising,ADMM
更新于2025-09-09 09:28:46
-
[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 - A Neural Network Method for Nonlinear Hyperspectral Unmixing
摘要： Because of the complex interaction of light with the Earth surface, a hyperspectral pixel can be composed of a highly nonlinear mixture of the re?ectances of the materials on the ground. When nonlinear mixing models are applied, the estimated model parameters are usually hard to interpret and to link to the actual fractional abundances. Moreover, not all spectral re?ectances in a real scene follow the same particular mixing model. In this paper, we present a supervised learning method for nonlinear spectral unmixing. In this method, a neural network is applied to learn mappings of the true spectral re?ectances to the re?ectances that would be obtained if the mixture was linear. A simple linear unmixing then reveals the actual abundance fractions. This technique is model-independent and allows for an easy interpretation of the obtained abundance fractions. We validate this method on several arti?cial datasets, a dataset obtained by ray tracing, and a real dataset.
关键词： endmembers,Hyperspectral,abundances,neural networks
更新于2025-09-09 09:28:46
-
Hyperspectral Imaging Classification based on a Convolutional Neural Network with Adaptive Windows and Filters Sizes
摘要： Image classification by the Convolutional Neural Networks (CNN) has shown its great performances in recent years, in several areas, such as image processing and pattern recognition; However, there is still some improvement to do. The main problem in CNN is the initialization of the number and size of the filters, which can obviously change the results. In this article, we assign three major contributions, based on the CNN model; (1) adaptive selection of the number of filters. (2) using an adaptive size of the windows. (3) using an adaptive size of the filters. The tests results, applied to different hyperspectral datasets (SalinasA, Pavia University, and Indian Pines), have proven that this framework is able to improve the accuracy of the HSI classification.
关键词： Adaptive Filters,Convolutional Neural Networks,Image Classification,Hyperspectral Imaging
更新于2025-09-09 09:28:46
-
Fast Active Learning for Hyperspectral Image Classification using Extreme Learning Machine
摘要： Due to undulating and complexity of the earth’s surface, obtaining the training samples for remote sensing data is time consuming and expensive. Therefore, it is highly desirable to design a model that uses as few labelled samples as possible and reducing the computational time. Several active learning (AL) algorithms have been proposed in the literature for the classification of hyperspectral images (HSI).However, its performance in term of computational time has not been focused yet. In this paper, we have proposed AL approach based on Extreme Learning Machine (ELM) that effectively decreases the computational time while maintaining the classification accuracy. Further, the effectiveness of the proposed approach has been depicted by comparing its performance with state-of-the-art AL algorithms in terms of classification accuracy and computational time as well. The ELM based active learning (ELM-AL) with different query strategies were conducted on two HSI data sets. The proposed approach achieves the classification accuracy up to 90% which is comparable to support vector machine (SVM) based AL (SVM-AL) approach but effectively reduces the computational time significantly by 1000 times. Thus proposed system shows the encouraging results with adequate classification accuracy while reducing the computation time drastically.
关键词： Uncertainty sampling,Remote Sensing Image,Extreme learning machine,Classification,Active learning,Uncertainty measure,Hyperspectral Image
更新于2025-09-09 09:28:46
-
Potential Application of Fluorescence Imaging for Assessing Fecal Contamination of Soil and Compost Maturity
摘要： Pathogenic microorganisms can lead to serious outbreaks of foodborne illnesses, particularly if fresh produce becomes contaminated and then happens to be inappropriately handled in a manner that can incubate pathogens. Pathogenic microbial contamination of produce can occur through a variety of pathways, such as from the excrement of domesticated and wild animals, biological soil amendment, agricultural water, worker health and hygiene, and ?eld tools used during growth and harvest. The use of mature manure compost and preventative control of fecal contamination from wildlife and livestock are subject to safety standards to minimize the risk of foodborne illness associated with produce. However, in a ?eld production environment, neither traces of animal feces nor the degree of maturity of manure compost can be identi?ed by the naked eye. In this study, we investigated hyperspectral ?uorescence imaging techniques to characterize fecal samples from bovine, swine, poultry, and sheep species, and to determine feasibilities for both detecting the presence of animal feces as well as identifying the species origin of the feces in mixtures of soil and feces. In addition, the imaging techniques were evaluated for assessing the maturity of manure compost. The animal feces exhibited dynamic and unique ?uorescence emission features that allowed for the detection of the presence of feces and showed that identi?cation of the species origin of fecal matter present in soil-feces mixtures is feasible. Furthermore, the results indicate that using simple single-band ?uorescence imaging at the ?uorescence emission maximum for animal feces, simpler than full-spectrum hyperspectral ?uorescence imaging, can be used to assess the maturity of manure compost.
关键词： feces,pathogenic microorganism,?uorescence imaging,compost,hyperspectral,fresh produce
更新于2025-09-09 09:28:46
-
[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 - Evaluation of Dimensional Reduction Methods on Urban Vegegation Classification Performance Using Hyperspectral Data
摘要： In the context of urban vegetation, hyperspectral imagery allows to discriminate biochemical properties of land surfaces. In this study, we test several dimension reductions to evaluate capacities of hyperspectral sensors to characterize tree families. The goal is to evaluate if a selection of differentiated and uncorrelated vegetation indices is an efficient method to reduce the dimension of hyperspectral images. This method is compared with conventional MNF and ACP approaches, and assessed on tree vegetation classifications performed using SVM classifier on two datasets at 4m and 8m spatial resolution. Results show that MNF combined with SVM classification is the better method to reduce hyperspectral dimension.
关键词： Urban vegetation,dimension reduction,SVM,hyperspectral data
更新于2025-09-09 09:28:46
-
Using Hyperspectral Data to Identify Crops in a Cultivated Agricultural Landscape - A Case Study of Taita Hills, Kenya
摘要： Recent advances in hyperspectral remote sensing techniques and technologies allow us to more accurately identify larger range of crop species from airborne measurements. This study employs hyperspectral AISA Eagle VNIR imagery acquired with 9 nm spectral and 0.6 m spatial resolutions over a spectral range of 400 nm to 1000 nm. The area of study is the Taita hills in Kenya. Various crops are grown in this region basically for food and as an economic activity. The crops addressed are: maize, bananas, avocados, and sugarcane and mango trees. The main objectives of this study were to study what crop species can be distinguished from the cultivated population crops in the agricultural landscape and what feature space discriminates most effectively the spectral signatures of different species. Spectral Angle Mapper (SAM) algorithm together with some dissimilarity concepts was applied in this work. The spectral signatures for crops were collected using accurate field plot maps. Accuracy assessment was done using independent training vector data. We achieved an overall accuracy of 77% with a kappa value of 0.67. Various crops in different locations were identified and shown.
关键词： Spectral angle mapper,Hyperspectral imaging,Spectral signatures,Spectral variation,Crop identification
更新于2025-09-09 09:28:46
-
Radiometric consistency assessment of hyperspectral infrared sounders
摘要： The radiometric and spectral consistency among the Atmospheric Infrared Sounder (AIRS), the Infrared Atmospheric Sounding Interferometer (IASI), and the Cross-track Infrared Sounder (CrIS) is fundamental for the creation of long-term infrared (IR) hyperspectral radiance benchmark data sets for both intercalibration and climate-related studies. In this study, the CrIS radiance measurements on Suomi National Polar-orbiting Partnership (SNPP) satellite are directly compared with IASI on MetOp-A and MetOp-B at the ?nest spectral scale and with AIRS on Aqua in 25 selected spectral regions through simultaneous nadir overpass (SNO) observations in 2013, to evaluate radiometric consistency of these four hyperspectral IR sounders. The spectra from different sounders are paired together through strict spatial and temporal collocation. The uniform scenes are selected by examining the collocated Visible Infrared Imaging Radiometer Suite (VIIRS) pixels. Their brightness temperature (BT) differences are then calculated by converting the spectra onto common spectral grids. The results indicate that CrIS agrees well with IASI on MetOp-A and IASI on MetOp-B at the long-wave IR (LWIR) and middle-wave IR (MWIR) bands with 0.1–0.2 K differences. There are no apparent scene-dependent patterns for BT differences between CrIS and IASI for individual spectral channels. CrIS and AIRS are compared at the 25 spectral regions for both polar and tropical SNOs. The combined global SNO data sets indicate that the CrIS–AIRS BT differences are less than or around 0.1 K among 21 of 25 spectral regions and they range from 0.15 to 0.21 K in the remaining four spectral regions. CrIS–AIRS BT differences in some comparison spectral regions show weak scene-dependent features.
关键词： hyperspectral infrared sounders,SNO observations,CrIS,radiometric consistency,IASI,brightness temperature differences,AIRS
更新于2025-09-09 09:28:46