- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Semisupervised Scene Classification for Remote Sensing Images: A Method Based on Convolutional Neural Networks and Ensemble Learning
摘要： The scarcity of labeled samples has been the main obstacle to the development of scene classification for remote sensing images. To alleviate this problem, the efforts have been dedicated to semisupervised classification which exploits both labeled and unlabeled samples for training classifiers. In this letter, we propose a novel semisupervised method that utilizes the effective residual convolutional neural network (ResNet) to extract preliminary image features. Moreover, the strategy of ensemble learning (EL) is adopted to establish discriminative image representations by exploring the intrinsic information of all available data. Finally, supervised learning is performed for scene classification. To verify the effectiveness of the proposed method, it is further compared with several state-of-the-art feature representation and semisupervised classification approaches. The experimental results show that by combining ResNet features with EL, the proposed method can obtain more effective image representations and achieve superior results.
关键词： remote sensing (RS) images,Semi-supervised classification,ensemble learning (EL),scene classification,Convolutional neural networks (CNNs)
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC, Canada (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Deep Transfer Learning for Hyperspectral Image Classification
摘要： Hyperspectral image (HSI) includes a vast quantities of samples, large number of bands, as well as randomly occurring redundancy. Classifying such complex data is challenging, and the classification performance generally is affected significantly by the amount of labeled training samples. Collecting such labeled training samples is labor and time consuming, motivating the idea of borrowing and reusing labeled samples from other pre-existing related images. Therefore transfer learning, which can mitigate the semantic gap between existing and new HSI, has recently drawn increasing research attention. However, existing transfer learning methods for HSI which concentrated on how to overcome the divergence among images, may neglect the high level latent features during the transfer learning process. In this paper, we present two novel ideas based on this observation. We propose constructing and connecting higher level features for the source and target HSI data, to further overcome the cross-domain disparity. Different from existing methods, no priori knowledge on the target domain is needed for the proposed classification framework, and the proposed framework works for both homogeneous and heterogenous HSI data. Experimental results on real world hyperspectral images indicate the significance of the proposed method in HSI classification.
关键词： supervised classification,salient samples,Hyperspectral image,Transfer learning
更新于2025-09-23 15:23:52
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Driver Drowsiness Detection in Facial Images
摘要： Extracting effective features of fatigue in images and videos is an open problem. This paper introduces a face image descriptor that can be used for discriminating driver fatigue in static frames. In this method, first, each facial image in the sequence is represented by a pyramid whose levels are divided into non-overlapping blocks of the same size, and hybrid image descriptor are employed to extract features in all blocks. Then the obtained descriptor is filtered out using feature selection. Finally, non-linear SVM is applied to predict the drowsiness state of the subject in the image. The proposed method was tested on the public dataset NTH Drowsy Driver Detection (NTHUDDD). This dataset includes a wide range of human subjects of different genders, poses, and illuminations in real-life fatigue conditions. Experimental results show the effectiveness of the proposed method. These results show that the proposed hand-crafted feature compare favorably with several approaches based on the use of deep Convolutional Neural Nets.
关键词： hand-crafted features,supervised classification,Drowsiness detection,deep features
更新于2025-09-23 15:22:29
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Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods
摘要： The wine-making industry generates a considerable amount of grape pomace. Grape seeds, as an important part of pomace, are rich in bioactive compounds and can be reutilized to produce useful derivatives. The nutritional properties of grape seeds are largely influenced by the cultivar, which calls for effective identification. In the present work, the spectral profiles of grape seeds belonging to three different cultivars were collected by laser-induced breakdown spectroscopy (LIBS). Three conventional supervised classification methods and a deep learning method, a one-dimensional convolutional neural network (CNN), were applied to establish discriminant models to explore the relationship between spectral responses and cultivar information. Interval partial least squares (iPLS) algorithm was successfully used to extract the spectral region (402.74–426.87 nm) relevant for elemental composition in grape seeds. By comparing the discriminant models based on the full spectra and the selected spectral regions, the CNN model based on the full spectra achieved the optimal overall performance, with classification accuracy of 100% and 96.7% for the calibration and prediction sets, respectively. This work demonstrated the reliability of LIBS as a rapid and accurate approach for identifying grape seeds and will assist in the utilization of certain genotypes with desirable nutritional properties essential for production rather than their being discarded as waste.
关键词： grape seed,laser-induced breakdown spectroscopy,supervised classification,region selection,deep learning
更新于2025-09-16 10:30:52
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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 - Semi-Supervised Scene Classification for Remote Sensing Images Based on CNN and Ensemble Learning
摘要： The special characteristic of remote sensing (RS) images being large scale while only low number of labeled samples available in practical applications has been obstacle to the development of RS image classification. In this paper, a novel semi-supervised framework is proposed. The high-capacity convolutional neural networks (CNN) are adopted to extract preliminary image features. The strategy of ensemble learning is then utilized to establish discriminative image representations by exploring intrinsic information of available data. Plain supervised learning is finally performed to obtain classification results. To verify the efficacy of our work, we compare it with mainstream feature representation and semi-supervised approaches. Experimental results show that by utilizing CNN features and ensemble learning, our framework can obtain more effective image representations and achieve superior results compared with other paradigms of semi-supervised classification.
关键词： convolutional neural network,ensemble learning,remote sensing images,Semi-supervised classification,scene classification
更新于2025-09-11 14:15:04
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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 - Classification of Hyperspectral Image Based on Hybrid Neural Networks
摘要： Convolutional neural networks (CNN), which are able to extract spatial semantic features, have achieved outstanding performance in many computer vision tasks. In this paper, hybrid neural networks (HNN) are proposed to extract both spatial and spectral features in the same deep networks. The proposed networks consist of different types of hidden layers, including spatial structure layer, spatial contextual layer, and spectral layer. All those layers work as organic networks to explore as much valuable information as possible from hyperspectral data for classification. Experimental results demonstrate competitive performance of the proposed approach over other state-of-the-art neural networks methods. Moreover, the proposed method is a new way to deal with multidimensional data with deep networks.
关键词： supervised classification,feature learning,hyperspectral image (HSI),convolutional neural networks (CNN)
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 - Semi-Supervised Remote Sensing Classification Via Associative Transfer
摘要： Images classification is an essential field in remote sensing community. However, a variety of target shapes, as well as changing conditions during multiple time periods and different areas usually result in shifts in classification. This problem can affect classification results seriously. Although the affection is significant in remote sensing classification, very few people have considered this issue, and have solved it. In this paper, we introduce the associative domain adaptation (ADA) method to address this challenge. We apply this algorithm to two public remote sensing datasets. One is famous UC Merced dataset; another is NWPU-RESISC45 dataset which has a much more variance within the class. We then build a classification model by using UC Merced training images and labels as well as using training images from NWPU-RESISC45. This semi-supervised classification performance achieves an impressive test accuracy on the NWPU-RESISC45 test dataset.
关键词： Associative Domain Adaptation,Semi-Supervised Classification,Remote Sensing
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 - A Novel Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification
摘要： For hyperspectral image classification, we present a novel graph based semi-supervised classification method that learns from similarity and dissimilarity on labeled and unlabeled data, which contain both the adjacency graph and the dissimilar graph. Since manifold learning approach is capable of exploring the manifold geometry of data, it is suitable for calculating the adjacency graph with label similarity. A manifold learning method was utilized to calculate the adjacency graph. Dissimilarity among examples probably be used to construct the dissimilar graph, which is hard to grasp. The dissimilar probability was proposed to construct the dissimilar graph, which has effectively improved the classification accuracy of hyperspectral data in experiment.
关键词： Graph,Semi-Supervised Classification,Hyperspectral Remote Sensing,Label Propagation
更新于2025-09-10 09:29:36
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Fusion of Polarimetric Features and Structural Gradient Tensors for VHR PolSAR Image Classification
摘要： This paper proposes a fast texture based supervised classification framework for fully polarimetric synthetic aperture radar (PolSAR) images with very high spatial resolution (VHR). With the development of recent polarimetric radar remote sensing technologies, the acquired images contain not only rich polarimetric characteristics but also high spatial content. Thus, the notion of geometrical structures and heterogeneous textures within VHR PolSAR data becomes more and more significant. Moreover, when the spatial resolution is increased, we need to deal with large-size image data. In this paper, our motivation is to characterize textures by incorporating (fusing) both polarimetric and structural features, and then use them for classification purpose. First, polarimetric features from the weighted coherency matrix and local geometric information based on the Di Zenzo structural tensors are extracted and fused using the covariance approach. Then, supervised classification task is performed by using Riemannian distance measure relevant for covariance-based descriptors. In order to accelerate the computational time, we propose to perform texture description and classification only on characteristic points, not all pixels from the image. Experiments conducted on the VHR F-SAR data as well as the AIRSAR Flevoland image using the proposed framework provide very promising and competitive results in terms of terrain classification and discrimination.
关键词： polarimetric synthetic aperture radar (PolSAR),gradient tensors,Covariance descriptor,textures,Riemannian distance,supervised classification
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 - MRF-Based Decision Fusion for Hyperspectral Image Classification
摘要： The high dimensionality of hyperspectral images, the limited availability of ground-truth data as well as the low spatial resolution (causing pixels to contain mixtures of materials) hinder hyperspectral image classification. In this work we propose a novel hyperspectral classification method where we combine the outcome of spectral unmixing with the outcome of a supervised classifier. In particular, we consider fractional abundances obtained from a Sparse Unmixing method along with posterior probabilities acquired from a Multinomial Logistic Regression classifier. Both sources of information are fused using a Markov Random Field framework. We conducted experiments on publicly available real hyperspectral images: Indian Pines and University of Pavia using a very limited number of training samples. Our results indicate that the proposed decision fusion approach significantly improves the classification result over using the individual sources and outperforms the state of the art methods.
关键词： MRF decision fusion,supervised classification,unmixing
更新于2025-09-09 09:28:46