Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification

Cheng, Shijie; Lv, Zhiyong; Yang, Xiuhong; Xu, Pengfei; Bibi, Irfana

doi:10.3390/rs12061012

Cited by 16 publications

(6 citation statements)

References 48 publications

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“…In Shi et al [16], a hierarchical multi view semi supervised learning framework with CNN (HMVSSL) is presented to remote sensing image classification. Initially, a super pixel based sample enlargement technique is projected to raise the amount of trained instances in every view.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model

Althobaiti¹,

Alotaibi²,

Abdel‐Khalek³

et al. 2022

Computers, Materials &Amp; Continua

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Latest advancements in the integration of camera sensors paves a way for new Unmanned Aerial Vehicles (UAVs) applications such as analyzing geographical (spatial) variations of earth science in mitigating harmful environmental impacts and climate change. UAVs have achieved significant attention as a remote sensing environment, which captures high-resolution images from different scenes such as land, forest fire, flooding threats, road collision, landslides, and so on to enhance data analysis and decision making. Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs. This paper proposes a new multi-modal fusion based earth data classification (MMF-EDC) model. The MMF-EDC technique aims to identify the patterns that exist in the earth data and classifies them into appropriate class labels. The MMF-EDC technique involves a fusion of histogram of gradients (HOG), local binary patterns (LBP), and residual network (ResNet) models. This fusion process integrates many feature vectors and an entropy based fusion process is carried out to enhance the classification performance. In addition, the quantum artificial flora optimization (QAFO) algorithm is applied as a hyperparameter optimization technique. The AFO algorithm is inspired by the reproduction and the migration of flora helps to decide the optimal parameters of the ResNet model namely learning rate, number of hidden layers, and their number of neurons. Besides, Variational Autoencoder (VAE) based classification model is applied to assign appropriate class labels for a useful set of feature vectors. The proposed MMF-EDC model has been tested using UCM and WHU-RS datasets. The proposed MMF-EDC model attains exhibits promising classification results on the applied remote sensing images with the accuracy of 0.989 and 0.994 on the test UCM and WHU-RS dataset respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model

Althobaiti¹,

Alotaibi²,

Abdel‐Khalek³

et al. 2022

Computers, Materials &Amp; Continua

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“…The valuable detailed information in VHRRS images can interfere with the extraction of object boundaries because the boundaries of multi-scale segmentation may not completely match those of objects. Only the center pixel of each object is selected and added to the training set to avoid mislabeling samples due to the incorrect extraction of segmentation object boundaries [21]. In the following sections, we describe in detail how to assign the label to the center pixel.…”

Section: Training Sample Setmentioning

confidence: 99%

“…Therefore, it is challenging to examine the training of classifiers with limited samples for the interpretation of remote sensing images [16]. Many scholars have explored methods to reduce the dependence of models on samples, such as transfer learning [17], few-shot learning [18], semi-supervised learning [19][20][21][22], unsupervised learning [23,24], and weakly supervised learning [25,26], and have achieved favorable results. However, the performance of these methods is not comparable to that of supervised learning methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Query Strategy-Based Rank Batch-Mode Active Learning Method for High-Resolution Remote Sensing Image Classification

Luo

Zhou

et al. 2021

Remote Sensing

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An informative training set is necessary for ensuring the robust performance of the classification of very-high-resolution remote sensing (VHRRS) images, but labeling work is often difficult, expensive, and time-consuming. This makes active learning (AL) an important part of an image analysis framework. AL aims to efficiently build a representative and efficient library of training samples that are most informative for the underlying classification task, thereby minimizing the cost of obtaining labeled data. Based on ranked batch-mode active learning (RBMAL), this paper proposes a novel combined query strategy of spectral information divergence lowest confidence uncertainty sampling (SIDLC), called RBSIDLC. The base classifier of random forest (RF) is initialized by using a small initial training set, and each unlabeled sample is analyzed to obtain the classification uncertainty score. A spectral information divergence (SID) function is then used to calculate the similarity score, and according to the final score, the unlabeled samples are ranked in descending lists. The most “valuable” samples are selected according to ranked lists and then labeled by the analyst/expert (also called the oracle). Finally, these samples are added to the training set, and the RF is retrained for the next iteration. The whole procedure is iteratively implemented until a stopping criterion is met. The results indicate that RBSIDLC achieves high-precision extraction of urban land use information based on VHRRS; the accuracy of extraction for each land-use type is greater than 90%, and the overall accuracy (OA) is greater than 96%. After the SID replaces the Euclidean distance in the RBMAL algorithm, the RBSIDLC method greatly reduces the misclassification rate among different land types. Therefore, the similarity function based on SID performs better than that based on the Euclidean distance. In addition, the OA of RF classification is greater than 90%, suggesting that it is feasible to use RF to estimate the uncertainty score. Compared with the three single query strategies of other AL methods, sample labeling with the SIDLC combined query strategy yields a lower cost and higher quality, thus effectively reducing the misclassification rate of different land use types. For example, compared with the Batch_Based_Entropy (BBE) algorithm, RBSIDLC improves the precision of barren land extraction by 37% and that of vegetation by 14%. The 25 characteristics of different land use types screened by RF cross-validation (RFCV) combined with the permutation method exhibit an excellent separation degree, and the results provide the basis for VHRRS information extraction in urban land use settings based on RBSIDLC.

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“…This leads, inevitably, to a low accuracy particularly with a missed or incomplete ground truth. To overcome this problem, Shi et al propose an hierarchical multi-view learning framework based on CNNs [23]. Aydav et al use granular computing to improve classification [24].…”

Section: Related Workmentioning

confidence: 99%

Design of Multi-View Graph Embedding for Features Selection and Remotely Sensing Signal Classification

Alotaibi¹,

Alotaibi²

2020

IJACSA

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Now-a-days, signal processing remains an intensive challenging area of research. In fact, various strategies have been suggested to address semi-supervised, feature selection and unlabeled samples challenges. The most frequent achievement was dedicated to exploit a single kind of feature/view from the original data. Recently, advanced techniques aimed to explore signals from different views and to, properly, integrate divergent kinds of interdependent features. In this paper, we propose a novel design of a multi-View Graph Embedding for features selection allowing a convenient integration of complementary weighted features. The proposed framework combines the singular properties of each feature space to accomplish a physically meaningful cooperative low-dimensional selection of input data. This allows us not only to perform a semi-supervised classification, but also to propagates narrow class information to unlabeled sample when only partial labeling knowledge is available. This paper makes the following contributions: (i) a feature selection schema for data refinement; and (ii) the adaptation of a multi-view graph-based approach by a better tackling of semi-supervised and dimensionality issues. Our experimental results, conducted by using a mixture of complementary features and aerial images datasets, demonstrate the effectiveness of the proposed framework without significantly increasing computational complexity.

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Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification

Cited by 16 publications

References 48 publications

Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model

Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model

A Novel Query Strategy-Based Rank Batch-Mode Active Learning Method for High-Resolution Remote Sensing Image Classification

Design of Multi-View Graph Embedding for Features Selection and Remotely Sensing Signal Classification

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