scholarly journals Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2984
Author(s):  
Gyanendra Prasad Joshi ◽  
Fayadh Alenezi ◽  
Gopalakrishnan Thirumoorthy ◽  
Ashit Kumar Dutta ◽  
Jinsang You
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Data Augmentation ◽  
Land Cover Classification ◽  
Remote Sensing Image ◽  
Environmental Modeling ◽  
Classification Model ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.

scholarly journals Classification of Land Cover from Remote Sensing Images using Morphological Linear Contact Distributions and Rough Sets

2019 ◽  
Vol 8 (3) ◽  
pp. 676-688
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Rough Sets ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Classification Algorithm ◽  
Forest Monitoring ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Remote sensing image classification plays an essential role in computer vision and image processing to address the problems in the areas of agriculture, forest monitoring, urban development, environment protection, etc. A lot of literature is available on remote sensing image classification. But, it is still a research task even today because of the multitude of problems. RTBFCA (Rough Texture Based Features Classification Algorithm), a new classification algorithm has been proposed in this paper. This paper aims at classifying the remote sensing images into various cover types using mathematical morphology and rough sets. Morphological texture features (linear contact distributions) along with first order statistics are used to identify the pixels of various classes and the concepts of lower and upper approximations of rough sets are used for clustering the features of the pixels and then are finally classified to display the classified image. The proposed method was tested on Google Earth images and is able to classify even various crops patterns of a land cover image. The algorithm is compared with other algorithms like ”GLCM with rough sets”, ”intensity values with rough sets” and with ”linear contact distributions with rough sets”.

scholarly journals Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification

2019 ◽  
Vol 11 (2) ◽  
pp. 174 ◽  
Author(s):  
Han Liu ◽  
Jun Li ◽  
Lin He ◽  
Yu Wang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Data ◽  
Sampling Strategy ◽  
Remote Sensing Image ◽  
Fine Tuning ◽  
Spatial Dependency ◽  
Remote Sensing Images ◽  
Training Set ◽  
Remote Sensing Image Classification

Irregular spatial dependency is one of the major characteristics of remote sensing images, which brings about challenges for classification tasks. Deep supervised models such as convolutional neural networks (CNNs) have shown great capacity for remote sensing image classification. However, they generally require a huge labeled training set for the fine tuning of a deep neural network. To handle the irregular spatial dependency of remote sensing images and mitigate the conflict between limited labeled samples and training demand, we design a superpixel-guided layer-wise embedding CNN (SLE-CNN) for remote sensing image classification, which can efficiently exploit the information from both labeled and unlabeled samples. With the superpixel-guided sampling strategy for unlabeled samples, we can achieve an automatic determination of the neighborhood covering for a spatial dependency system and thus adapting to real scenes of remote sensing images. In the designed network, two types of loss costs are combined for the training of CNN, i.e., supervised cross entropy and unsupervised reconstruction cost on both labeled and unlabeled samples, respectively. Our experimental results are conducted with three types of remote sensing data, including hyperspectral, multispectral, and synthetic aperture radar (SAR) images. The designed SLE-CNN achieves excellent classification performance in all cases with a limited labeled training set, suggesting its good potential for remote sensing image classification.

scholarly journals REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

2016 ◽  
Vol XLI-B7 ◽  
pp. 419-422
Author(s):  
Xin Yu ◽  
Zongyong Wen ◽  
Zhaorong Zhu ◽  
Qiang Xia ◽  
Lan Shun
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
High Resolution ◽  
Bayesian Networks ◽  
Image Classification ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Geographical Information ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

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

2020 ◽  
Vol 12 (6) ◽  
pp. 1012 ◽  
Author(s):  
Cheng Shi ◽  
Zhiyong Lv ◽  
Xiuhong Yang ◽  
Pengfei Xu ◽  
Irfana Bibi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Supervised Learning ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Remote Sensing Images ◽  
Training Set ◽  
Remote Sensing Image Classification ◽  
Very High

Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods.

scholarly journals REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

2016 ◽  
Vol XLI-B7 ◽  
pp. 419-422 ◽  
Author(s):  
Xin Yu ◽  
Zongyong Wen ◽  
Zhaorong Zhu ◽  
Qiang Xia ◽  
Lan Shun
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
High Resolution ◽  
Bayesian Networks ◽  
Image Classification ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Geographical Information ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

scholarly journals IMAGE QUATY ASSESSMENT FOR VHR REMOTE SENSING IMAGE CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 11-16
Author(s):  
Zhipeng Li ◽  
Li Shen ◽  
Linmei Wu
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Image Classification ◽  
Great Influence ◽  
Ground Cover ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Classification Framework ◽  
Remote Sensing Image Classification ◽  
Relationship Of

The data from remote sensing images are widely used for characterizing land use and land cover at present. With the increasing availability of very high resolution (VHR) remote sensing images, the remote sensing image classification becomes more and more important for information extraction. The VHR remote sensing images are rich in details, but high within-class variance as well as low between-class variance make the classification of ground cover a difficult task. What’s more, some related studies show that the quality of VHR remote sensing images also has a great influence on the ability of the automatic image classification. Therefore, the research that how to select the appropriate VHR remote sensing images to meet the application of classification is of great significance. In this context, the factors of VHR remote sensing image classification ability are discussed and some indices are selected for describing the image quality and the image classification ability objectively. Then, we explore the relationship of the indices of image quality and image classification ability under a specific classification framework. The results of the experiments show that these image quality indices are not effective for indicating the image classification ability directly. However, according to the image quality metrics, we can still propose some suggestion for the application of classification.

scholarly journals Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5602
Author(s):  
Xudong Guan ◽  
Chong Huang ◽  
Juan Yang ◽  
Ainong Li
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Probability Distributions ◽  
Spatial Relationship ◽  
Remote Sensing Image ◽  
Spatial Relationships ◽  
Fuzzy Classifier ◽  
Land Cover Types ◽  
Remote Sensing Image Classification

Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification “smarter”. In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the “siphonic effect” produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree.

scholarly journals An Uncertainty Descriptor for Quantitative Measurement of the Uncertainty of Remote Sensing Images

2019 ◽  
Vol 11 (13) ◽  
pp. 1560 ◽  
Author(s):  
Qi Zhang ◽  
Penglin Zhang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Quantitative Measurement ◽  
Quantitative Description ◽  
Remote Sensing Image ◽  
Control Methods ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification ◽  
Reliable Classification ◽  
Uncertainty Control

Reliable image classification results are crucial for the application of remote sensing images, but the reliability of image classification has received less attention. In particular, the inherent uncertainty of remote sensing images has been disregarded. The uncertainty of a remote sensing image accumulates and propagates continuously in the classification process and ultimately affects the reliability of the classification results. Therefore, quantitative description and investigation of the inherent uncertainty of remote sensing images are crucial in achieving reliable remote sensing image classification. In this study, we analyze the sources of uncertainty of remote sensing images in detail and propose a quantitative descriptor for measuring image uncertainty comprehensively and effectively. In addition, we also design two verification schemes to verify the validity of the proposed uncertainty descriptor. Finally, the validity of the proposed uncertainty descriptor is confirmed by experimental results on three real remote sensing images. Our study on the uncertainty of remote sensing images may help the development of uncertainty control methods and reliable classification schemes of remote sensing images.

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