- 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
-
Robust Hyperspectral Image Domain Adaptation With Noisy Labels
摘要： In hyperspectral image (HSI) classification, domain adaptation (DA) methods have been proved effective to address unsatisfactory classification results caused by the distribution difference between training (i.e., source domain) and testing (i.e., target domain) pixels. However, these methods rely on accurate labels in source domain, and seldom consider the performance drop resulted by noisy label, which often happens since labeling pixel in HSI is a challenging task. To improve the robustness of DA method to label noise, we propose a new unsupervised HSI DA method, which is constructed from both feature-level and classifier-level. First, a linear transformation function is learned in feature-level to align the source (domain) subspace with the target (domain) subspace. Then, a robust low-rank representation based classifier is developed to well cope with the features obtained from the aligned subspace. Since both subspace alignment and the classifier are immune to noisy labels, the proposed method obtains good classification results when confronting with noisy labels in source domain. Experimental results on two DA benchmarks demonstrate the effectiveness of the proposed method.
关键词： low-rank representation,hyperspectral image (HSI) classification,Domain adaptation,subspace alignment
更新于2025-09-23 15:22:29
-
[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Robust Nonnegative Local Coordinate Factorization for Hyperspectral Unmixing
摘要： Recently, nonnegative matrix factorization (NMF) has become increasingly popular for hyperspectral unmixing (HU). Due to the non-convex nature of the NMF theory, which is sensitive to the initial value and various noise. To obtain more accurate and robust unmixing model, in this paper, we propose a novel method called robust nonnegative local coordinate factorization (RNLCF). RNLCF adds a local coordinate constraint into the composite loss function which combing classic and Correntropy Induced Metric NMF function. To solve RNLCF, we developed a multiplicative update rules. Experimental results on synthetic and real-world data verify the effectiveness of RNLCF comparing with the representative methods.
关键词： local coordinate,Correntropy Induced Metric,hyperspectral unmixing (HU),nonnegative matrix factorization (NMF)
更新于2025-09-23 15:22:29
-
Optimized angles of the swing hyperspectral imaging system for single corn plant
摘要： During recent years, hyperspectral imaging systems have been widely applied in the greenhouses for plant phenotyping purposes. Current systems are typically designed as either top view or side view imaging mode. Top view is an ideal imaging angle for top leaves with flat leaf surfaces. However, most bottom leaves are either blocked or shaded. From side view, the entire plant structure is viewable. However, most leaf surfaces are not facing the camera, which impacts measurement quality. Besides, there could be advantages with certain tilted angle(s) between top view and side view. It’s interesting to explore the impact of different imaging angles to the phenotyping quality. For this purpose, a swing hyperspectral imaging system capable of capturing images at any angle from side view (0°) to top view (90°) by rotating the camera and the lighting source was designed. Corn plants were grown and allocated into 3 different treatments: high nitrogen (N) and well-watered (control), high N and drought-stressed, and low N and well-watered. Each plant was imaged at 7 different angles from 0° to 90° with an interval of 15°. The soil plant analysis development (SPAD) values and relative water content (RWC) ground truth measurements were used to establish treatment effects. The results showed that averaged plant-level Normalized Difference Vegetation Index (NDVI) values of plants in different treatments changed at different imaging angles. The results also indicated that for pixel-level NDVI distributions, the titled imaging angle of 75° was optimal to distinguish different water treatments, whereas, the tilted imaging angle of 15° was optimal to distinguish different N treatments. For pixel-level RWC distributions, the distribution difference between different water treatments was larger at higher imaging angles.
关键词： Pixel-level NDVI and RWC distributions,Optimal imaging angle,Swing hyperspectral imaging system,Plant phenotyping system,Tilted imaging angle
更新于2025-09-23 15:22:29
-
Analysis for the Weakly Pareto Optimum in Multiobjective-Based Hyperspectral Band Selection
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. A possible drawback of these methods is that they can only generate one solution in a single run with a given band number. To overcome this problem, multiobjective (MO)-based methods, which were able to simultaneously obtain a series of subsets with different band numbers, were investigated for band selection. However, because the range of band selection problem is discrete, recently proposed weighted Tchebycheff (WT)-based MO methods may suffer weakly Pareto optimal problem. In this case, the solutions for each band number will be nonunique and no optimal solution exists. Decision makers have to manually select a unique solution for each band number. In this paper, we provide a theoretical analysis about the weakly Pareto optimal problem in band selection, and quantitatively give the boundary conditions. Moreover, we further summarize the suggestions which will help users avoid the weakly Pareto optimal problem. According to these criteria, we develop a new adaptive-penalty-based boundary intersection (APBI) framework to improve the MO algorithm in hyperspectral band selection. APBI mainly includes two advantages: 1) avoiding weakly Pareto optimum and 2) reducing the sensibility of the penalty factor. The theoretical analysis is further validated by contrast experiments. The results demonstrate that the weakly Pareto optimal solutions really exist in WT methods, while APBI can overcome this problem.
关键词： multiobjective (MO) optimization,Band selection,weakly Pareto optimum,hyperspectral imagery (HSI)
更新于2025-09-23 15:22:29
-
Semisupervised Stacked Autoencoder With Cotraining for Hyperspectral Image Classification
摘要： Recently, deep learning (DL) is of great interest in hyperspectral image (HSI) classification. Although many effective frameworks exist in the literature, the generally limited availability of training samples poses great challenges in applying DL to HSI classification. In this paper, we present a novel DL framework, namely, semisupervised stacked autoencoders (Semi-SAEs) with cotraining, for HSI classification. First, two SAEs are pretrained based on the hyperspectral features and the spatial features, respectively. Second, fine-tuning is alternatively conducted for the two SAEs in a semisupervised cotraining fashion, where the initial training set is enlarged by designing an effective region growing method. Finally, the classification probabilities obtained by the two SAEs are fused using a Markov random field model solved by iterated conditional modes. Experimental results based on three popular hyperspectral data sets demonstrate that the proposed method outperforms other state-of-the-art DL methods.
关键词： Deep learning (DL),stacked autoencoders (SAEs),cotraining,hyperspectral image (HSI) semisupervised classification,Markov random field (MRF)
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 - The Effect of Ground Truth on Accuracy Indexes in Hyperspectral Image Classification
摘要： In this paper, the effect of ground truths on performance evaluation of hyperspectral image classification is studied. The purpose is to investigate whether the accuracies in terms of three representative accuracy indexes, i.e., the overall accuracy (OA), the average accuracy (AA), and the Kappa coefficient, can be completely responsible when the ground truth is insufficient. The major contribution of this work is designing several experiments so as to subjectively and objectively analysis the influences of ground truths on performance evaluation. Furthermore, four evaluation metrics, i.e., the Pearson linear correlation coefficient (PLCC), root mean square error (RMSE), Spearmans rank correlation coefficient (SRCC), and Kendalls rank correlation coefficient (KRCC) have been adopted to measure the robustness of different classification methods to ground truths containing different numbers of labeled pixels and the location of ground truth in the image. Based on the designed experiments, a conclusion is obtained that insufficient ground truths may affect the performance of existing accuracy indexes. This underlines that over optimistic performance evaluations may exist when the ground truth contains a small number of labeled pixels.
关键词： accuracy indexes,Hyperspectral image classification,performance evaluation
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 - Determining Uncertainty Prediction Map of Copper Concentration in Pasture from Hyperspectral Data Using Qunatile Regression Forest
摘要： Hyperspectral data has high potential to predict the biochemical components of grass with high accuracy, although the accuracy can be significantly improved when hyperspectral data is combined with environmental and topographical data. In this study, a fixed wing airborne survey was conducted using a AisaFENIX hyperspectral imager on a hill country sheep and beef farm. Pasture samples were collected across the farm to determine the copper concentration. After processing the hyperspectral imagery, the data was combined with environmental and topographical data to produce spatial prediction maps with associated uncertainties (95% prediction interval) using a new approach called Quantile Regression Forest (QRF). The results from this study suggest that QRF could provide more accurate and uncertain maps of pasture chemical properties.
关键词： quantile regression forest,pasture,Hyperspectral
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 - A Novel Effective Chlorophyll Indicator for Forest Monitoring Using Worldview-3 Multispectral Reflectance
摘要： This paper explores the feasibility of deriving multispectral-based effective chlorophyll indicators (MECIs) for foliage chlorophyll concentration (CHLS) estimation. An average fusion method was applied to simulate the multispectral reflectance of the WorldView-3 sensor using hyperspectral data. With the experimental data of CHLS and predictors derived from multispectral reflectance, a series of linear regression analyses were carried out to derive appropriate models for CHLS estimation. Accuracy measures of RMSE and PRMSE were used to evaluate the model performance. Results showed that the coastal-band based MECI (MECIc) and the blue-band based MECI (MECIb) were able to achieve an RMSE of 0.5657 mg/g and 0.5943 mg/g as well as a PRMSE of 36% and 38% respectively. Using the Red edge and Yellow reflectance based NDVI (NDVIREY) as a predictor, the model can reduce uncertainty and achieve an estimation of 0.4089 mg/g and 26% for RMSE and PRMSE respectively. The prediction error made by the CHLS-NDVIREY model and the CHLS-MECI model were 11% and 60% larger than 0.38 mg/g the RMSE of hyperspectral-based CHLS-ECI model. In summary, NDVIREY was able to achieve a better prediction at around a level of 75% accuracy (1-PRMSE) and therefore is able to be an effective indicator of CHLS for forest monitoring.
关键词： climate change,hyperspectral remote sensing,Chlorophyll indicator,multispectral remote sensing,forest health
更新于2025-09-23 15:22:29
-
High Efficient Deep Feature Extraction and Classification of Spectral-Spatial Hyperspectral Image Using Cross Domain Convolutional Neural Networks
摘要： Recently, numerous remote sensing applications highly depend on the hyperspectral image (HSI). HSI classification, as a fundamental issue, has attracted increasing attention and become a hot topic in the remote sensing community. We implemented a regularized convolutional neural network (CNN), which adopted dropout and regularization strategies to address the overfitting problem of limited training samples. Although many kinds of the literature have confirmed that it is an effective way for HSI classification to integrate spectrum with spatial context, the scaling issue is not fully exploited. In this paper, we propose a high efficient deep feature extraction and the classification method for the spectral-spatial HSI, which can make full use of multiscale spatial feature obtained by guided filter. The proposed approach is the first attempt to lean a CNN for spectral and multiscale spatial features. Compared to its counterparts, experimental results show that the proposed method can achieve 3% improvement in accuracy, according to various datasets such as Indian Pines, Pavia University, and Salinas.
关键词： Convolutional neural network (CNN),hyperspectral image (HSI) classification,guided filter,spectral-spatial fusion
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 - Dection and Health Analysis of Individual Tree in Urban Environment with Multi-Sensor Platform
摘要： With the technology enhanced, 3D mobile light detection and ranging (LiDAR) can produce more accurate 3D information for the objects. Meanwhile, hyperspectral remote sensing has more number of wavelengths and provides a higher resolution spectrum of objects. This paper proposes a multi-sensor platform to provide these two data for health detection at the individual tree level in urban environments. We firstly locate and segment the suspected tree objects by ground removal and Euclidean distance clustering. Then we take use of spectrum to remove non-tree objects, e.g., buildings, light poles. After that, we use LiDAR data to compute the geometric parameters of each tree and hyperspectral data to analyze its health situation.
关键词： point cloud,hyperspectral,spectrum,LiDAR,individual tree detection,health monitoring
更新于2025-09-23 15:22:29