AIRBORNE HYPERSPECTRAL REMOTE SENSING FOR IDENTIFICATION GRASSLAND VEGETATION

In our study we classified grassland vegetation types of an alkali landscape (Eastern Hungary), using different image classification methods for hyperspectral data. Our aim was to test the applicability of hyperspectral data in this complex system using various image classification methods. To reach the highest classification accuracy, we compared the performance of traditional image classifiers, machine learning algorithm, feature extraction (MNF-transformation) and various sizes of training dataset. Hyperspectral images were acquired by an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. We used twenty vegetation classes which were compiled based on the characteristic dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset using various training sample sizes between 10 and 30 pixels. In the case of the original bands, both SVM and RF classifiers provided high accuracy for almost all classes irrespectively of the number of the training pixels. We found that SVM and RF produced the best accuracy with the first nine MNF transformed bands. Our results suggest that in complex open landscapes, application of SVM can be a feasible solution, as this method provides higher accuracies compared to RF and MLC. SVM was not sensitive for the size of the training samples, which makes it an adequate tool for cases when the available number of training pixels are limited for some classes.

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A Double-Strategy-Check Active Learning Algorithm for Hyperspectral Image Classification

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.85.11.841 ◽

2019 ◽

Vol 85 (11) ◽

pp. 841-851

Author(s):

Ying Cui ◽

Xiaowei Ji ◽

Kai Xu ◽

Liguo Wang

Keyword(s):

Active Learning ◽

Image Classification ◽

Traditional Method ◽

Hyperspectral Image ◽

Learning Algorithm ◽

Semisupervised Learning ◽

Hyperspectral Data ◽

Data Sets ◽

Classification Methods ◽

Hyperspectral Image Classification

Applying limited labeled samples to improve classification results is a challenge in hyperspectral images. Active Learning (AL) and Semisupervised Learning (SSL) are two promising techniques to achieve this challenge. Combining AL with SSL is an excellent idea for hyperspectral image classification. The traditional method, such as the Collaborative Active and Semisupervised Learning algorithm (CASSL), may introduce many incorrect pseudolabels and shows premature convergence. To overcome these drawbacks, a novel framework named Double-Strategy-Check Collaborative Active and Semisupervised Learning (DSC-CASSL) is proposed in this paper. This framework combines two different AL algorithms and SSL in a collaborative mode. The double-strategy verification can gradually improve the pseudolabeling accuracy and facilitate SSL. We evaluate the performance of DSC-CASSL on four hyperspectral data sets and compare it with that of four hyperspectral image classification methods. Our results suggest that DSC-CASSL leads to consistent improvement for hyperspectral image classification.

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A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs12040664 ◽

2020 ◽

Vol 12 (4) ◽

pp. 664 ◽

Cited By ~ 2

Author(s):

Binge Cui ◽

Jiandi Cui ◽

Yan Lu ◽

Nannan Guo ◽

Maoguo Gong

Keyword(s):

Sparse Representation ◽

Image Classification ◽

Information Entropy ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Hyperspectral Image Classification ◽

Training Set ◽

Training Samples ◽

Labeling Method

Hyperspectral image classification methods may not achieve good performance when a limited number of training samples are provided. However, labeling sufficient samples of hyperspectral images to achieve adequate training is quite expensive and difficult. In this paper, we propose a novel sample pseudo-labeling method based on sparse representation (SRSPL) for hyperspectral image classification, in which sparse representation is used to select the purest samples to extend the training set. The proposed method consists of the following three steps. First, intrinsic image decomposition is used to obtain the reflectance components of hyperspectral images. Second, hyperspectral pixels are sparsely represented using an overcomplete dictionary composed of all training samples. Finally, information entropy is defined for the vectorized sparse representation, and then the pixels with low information entropy are selected as pseudo-labeled samples to augment the training set. The quality of the generated pseudo-labeled samples is evaluated based on classification accuracy, i.e., overall accuracy, average accuracy, and Kappa coefficient. Experimental results on four real hyperspectral data sets demonstrate excellent classification performance using the new added pseudo-labeled samples, which indicates that the generated samples are of high confidence.

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Learning with small amount of data on various task

10.31219/osf.io/4fxtn ◽

2020 ◽

Author(s):

Stefanie

Keyword(s):

Deep Neural Networks ◽

Learning Strategy ◽

Teaching Strategy ◽

Training Sample ◽

Learning To Teach ◽

Training Data ◽

Training Dataset ◽

Small Data ◽

Training Samples ◽

Teacher Model

As a student, I am learning knowledge with the help of teachers and the teacher plays a crucial role in our life. A wonderful instructor is able to teach a student with appropriate teaching materials. Therefore, in this project, I explore a teaching strategy called learning to teach (L2T) in which a teacher model could provide high-quality training samples to a student model. However, one major problem of L2T is that the teacher model will only select a subset of the training dataset as the final training data for the student. Learning to teach small-data learning strategy (L2TSDL) is proposed to solve this problem. In this strategy, the teacher model will calculate the importance score for every training sample and help students to make use of all training samples. To demonstrate the advantage of the proposed approach over L2T, I take the training of different deep neural networks (DNN) on image classification task as an exampleand show that L2TSDL could achieve good performance on both large and small dataset.

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An effective feature learning approach using genetic programming with image descriptors for image classification

10.26686/wgtn.13158320.v1 ◽

2020 ◽

Author(s):

Ying Bi ◽

Bing Xue ◽

Mengjie Zhang

Keyword(s):

Genetic Programming ◽

Image Classification ◽

Learning Algorithm ◽

Feature Learning ◽

Classification Performance ◽

Learning Approach ◽

New Approach ◽

Feature Descriptors ◽

High Level ◽

Almost All

© 2005-2012 IEEE. Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks.

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Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Discrete Dynamics in Nature and Society ◽

10.1155/2021/9998185 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Eryang Chen ◽

Ruichun Chang ◽

Kaibo Shi ◽

Ansheng Ye ◽

Fang Miao ◽

...

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Spatial Features ◽

Training Samples ◽

Semisupervised Classification ◽

Ground Object ◽

Multiscale Local Binary Pattern ◽

Noise Fraction ◽

Reduced Data

Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.

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Semi-Supervised Classification for Hyperspectral Image Based on Tri-Training

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.3644 ◽

2014 ◽

Vol 687-691 ◽

pp. 3644-3647 ◽

Cited By ~ 1

Author(s):

Li Guo Wang ◽

Yue Shuang Yang ◽

Ting Ting Lu

Keyword(s):

Supervised Classification ◽

Hyperspectral Image ◽

Learning Algorithm ◽

Hyperspectral Data ◽

Data Sets ◽

Hyperspectral Image Classification ◽

Training Set ◽

De Algorithm ◽

Training Samples ◽

Limited Training Samples

Hyperspectral image classification is difficult due to the high dimensional features but limited training samples. Tri-training learning is a widely used semi-supervised classification method that addresses the problem of lacking of labeled examples. In this paper, a novel semi-supervised learning algorithm based on tri-training method is proposed. The proposed algorithm combines margin sampling (MS) technique and differential evolution (DE) algorithm to select the most informative samples and perturb them randomly. Then the samples we obtained, which can fulfill the labeled data distribution and introduce diversity to multiple classifiers, are added to training set to train base classifiers for tri-training. The proposed algorithm is experimentally validated using real hyperspectral data sets, indicating that the combination of MS and DE can significantly reduce the need of labeled samples while achieving high accuracy compared with state-of-the-art algorithms.

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A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs11111325 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1325 ◽

Cited By ~ 6

Author(s):

Chen Chen ◽

Yi Ma ◽

Guangbo Ren

Keyword(s):

Image Classification ◽

Classification Accuracy ◽

Hyperspectral Image ◽

Training Sample ◽

Practical Applications ◽

Model Based ◽

Training Samples ◽

Proposed Model ◽

Convergence Performance ◽

Hyperspectral Classification

Deep learning models, especially the convolutional neural networks (CNNs), are very active in hyperspectral remote sensing image classification. In order to better apply the CNN model to hyperspectral classification, we propose a CNN model based on Fletcher–Reeves algorithm (F–R CNN), which uses the Fletcher–Reeves (F–R) algorithm for gradient updating to optimize the convergence performance of the model in classification. In view of the fact that there are fewer optional training samples in practical applications, we further propose a method of increasing the number of samples by adding a certain degree of perturbed samples, which can also test the anti-interference ability of classification methods. Furthermore, we analyze the anti-interference and convergence performance of the proposed model in terms of different training sample data sets, different batch training sample numbers and iteration time. In this paper, we describe the experimental process in detail and comprehensively evaluate the proposed model based on the classification of CHRIS hyperspectral imagery covering coastal wetlands, and further evaluate it on a commonly used hyperspectral image benchmark dataset. The experimental results show that the accuracy of the two models after increasing training samples and adjusting the number of batch training samples is improved. When the number of batch training samples is continuously increased to 350, the classification accuracy of the proposed method can still be maintained above 80.7%, which is 2.9% higher than the traditional one. And its time consumption is less than that of the traditional one while ensuring classification accuracy. It can be concluded that the proposed method has anti-interference ability and outperforms the traditional CNN in terms of batch computing adaptability and convergence speed.

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Adversarial Reconstruction-Classification Networks for PolSAR Image Classification

Remote Sensing ◽

10.3390/rs11040415 ◽

2019 ◽

Vol 11 (4) ◽

pp. 415 ◽

Cited By ~ 3

Author(s):

Yanqiao Chen ◽

Yangyang Li ◽

Licheng Jiao ◽

Cheng Peng ◽

Xiangrong Zhang ◽

...

Keyword(s):

Image Classification ◽

Training Sample ◽

Prediction Problem ◽

Polarimetric Synthetic Aperture Radar ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Training Samples ◽

Adversarial Training ◽

Composite Representation ◽

Supervised Image Classification

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more widely used in recent years. It is well known that PolSAR image classification is a dense prediction problem. The recently proposed fully convolutional networks (FCN) model, which is very good at dealing with the dense prediction problem, has great potential in resolving the task of PolSAR image classification. Nevertheless, for FCN, there are some problems to solve in PolSAR image classification. Fortunately, Li et al. proposed the sliding window fully convolutional networks (SFCN) model to tackle the problems of FCN in PolSAR image classification. However, only when the labeled training sample is sufficient, can SFCN achieve good classification results. To address the above mentioned problem, we propose adversarial reconstruction-classification networks (ARCN), which is based on SFCN and introduces reconstruction-classification networks (RCN) and adversarial training. The merit of our method is threefold: (i) A single composite representation that encodes information for supervised image classification and unsupervised image reconstruction can be constructed; (ii) By introducing adversarial training, the higher-order inconsistencies between the true image and reconstructed image can be detected and revised. Our method can achieve impressive performance in PolSAR image classification with fewer labeled training samples. We have validated its performance by comparing it against several state-of-the-art methods. Experimental results obtained by classifying three PolSAR images demonstrate the efficiency of the proposed method.

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Generative Adversarial Network Synthesis of Hyperspectral Vegetation Data

Remote Sensing ◽

10.3390/rs13122243 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2243

Author(s):

Andrew Hennessy ◽

Kenneth Clarke ◽

Megan Lewis

Keyword(s):

Precision Agriculture ◽

Classification Accuracy ◽

Feature Space ◽

Hyperspectral Data ◽

Network Synthesis ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Training Samples ◽

Almost All ◽

Outlying Regions

New, accurate and generalizable methods are required to transform the ever-increasing amount of raw hyperspectral data into actionable knowledge for applications such as environmental monitoring and precision agriculture. Here, we apply advances in generative deep learning models to produce realistic synthetic hyperspectral vegetation data, whilst maintaining class relationships. Specifically, a Generative Adversarial Network (GAN) is trained using the Cramér distance on two vegetation hyperspectral datasets, demonstrating the ability to approximate the distribution of the training samples. Evaluation of the synthetic spectra shows that they respect many of the statistical properties of the real spectra, conforming well to the sampled distributions of all real classes. Creation of an augmented dataset consisting of synthetic and original samples was used to train multiple classifiers, with increases in classification accuracy seen under almost all circumstances. Both datasets showed improvements in classification accuracy ranging from a modest 0.16% for the Indian Pines set and a substantial increase of 7.0% for the New Zealand vegetation. Selection of synthetic samples from sparse or outlying regions of the feature space of real spectral classes demonstrated increased discriminatory power over those from more central portions of the distributions.

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Multiple Classifiers Based Semi-Supervised Polarimetric SAR Image Classification Method

Sensors ◽

10.3390/s21093006 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3006

Author(s):

Lekun Zhu ◽

Xiaoshuang Ma ◽

Penghai Wu ◽

Jiangong Xu

Keyword(s):

Deep Learning ◽

Image Classification ◽

Majority Voting ◽

Classification Method ◽

Training Dataset ◽

Support Vector ◽

Svm Classifier ◽

Great Success ◽

Training Set ◽

Training Samples

Polarimetric synthetic aperture radar (PolSAR) image classification has played an important role in PolSAR data application. Deep learning has achieved great success in PolSAR image classification over the past years. However, when the labeled training dataset is insufficient, the classification results are usually unsatisfactory. Furthermore, the deep learning approach is based on hierarchical features, which is an approach that cannot take full advantage of the scattering characteristics in PolSAR data. Hence, it is worthwhile to make full use of scattering characteristics to obtain a high classification accuracy based on limited labeled samples. In this paper, we propose a novel semi-supervised classification method for PolSAR images, which combines the deep learning technique with the traditional scattering trait-based classifiers. Firstly, based on only a small number of training samples, the classification results of the Wishart classifier, support vector machine (SVM) classifier, and a complex-valued convolutional neural network (CV-CNN) are used to conduct majority voting, thus generating a strong dataset and a weak dataset. The strong training set are then used as pseudo-labels to reclassify the weak dataset by CV-CNN. The final classification results are obtained by combining the strong training set and the reclassification results. Experiments on two real PolSAR images on agricultural and forest areas indicate that, in most cases, significant improvements can be achieved with the proposed method, compared to the base classifiers, and the improvement is approximately 3–5%. When the number of labeled samples was small, the superiority of the proposed method is even more apparent. The improvement for built-up areas or infrastructure objects is not as significant as forests.

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