Deep Feature Extraction and Feature Fusion for Bi-temporal Satellite Image Classification

Asokan, Anju; Anitha, J.; Pătruţ, Bogdan; Dănciulescu, Daniela; Hemanth, D. Jude

doi:10.32604/cmc.2020.012364

Cited by 19 publications

(16 citation statements)

References 38 publications

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“…Data feature fusion is divided according to levels and can be divided into pixel-level data fusion, feature-level data fusion, and decision-level feature fusion. The pixel-level data fusion level is low, and this method can mainly improve the image quality; feature-level data fusion is a relatively high-level feature fusion, which extracts some basic features from the image for fusion, and the fused features can better perform the image analysis; decision-level feature fusion is to use different classifiers to analyze the picture, and the results obtained are subjected to decision-making fusion, and the category attributes are directly obtained [ 22 ].…”

Section: Recognition and Prediction Of Premature Ovarian Failure By U...mentioning

confidence: 99%

Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure

Zhang

Hou

Wang

et al. 2022

Journal of Healthcare Engineering

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Ultrasound imaging technology has the advantages of noninvasiveness, real-time, low price, and easy operation. It is one of the most used diagnostic tools for early detection and classification of premature ovarian failure. Although the rapid development of computer-aided diagnosis has provided a great help to the ultrasound diagnosis of premature ovarian failure, it still has many limitations and shortcomings, so this paper adopts transfer learning and feature fusion algorithms to improve the identification and prediction efficiency of premature ovarian failure. In this study, the POF group and the control group both adopted a unified scale. From the four aspects of sociological characteristics, past medical history, environmental factors, and living habits, a dedicated person asked and filled out the scale face to face. All patients participating in the experiment underwent ultrasound examinations. In this paper, the bottom-level feature fusion method is used to improve classification performance. The experiment uses 100 epochs. After each epoch training is completed, we used all the data and labels of the target domain to test. All experiments were performed five times, and the result is the average of five experiments. All the results of baseline and direct classification without migration use the average of five experimental results as the result. Migrating the features extracted by the InceptionV3 network has the best performance for predicting premature ovarian failure. Its classification accuracy is as high as 85.13%, and the F1 value is 0.78. The results show that the migration learning and feature fusion algorithms used in this paper can provide reliable predictive analysis and decision support for doctors in the diagnosis of premature ovarian failure.

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Section: Recognition and Prediction Of Premature Ovarian Failure By U...mentioning

confidence: 99%

Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure

Zhang

Hou

Wang

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…To increase the change detection accuracy of an algorithm for the aforementioned types of image features, the effective detection of changes is required, and inaccurate classification must be minimized. Map information concerning changes in height is key to improving classification precision [ 40 ]. Accordingly, satellite images should be used to enhance the community's monitoring of changes in the forests; through the utilization of readily available aerial and fixed-point photography, the community can obtain information about the distribution of forests, the conditions of sightseeing spots, and the reproductive status of various species in real time.…”

Section: Using Contemporary Scientific Methods and Equipment To Enhan...mentioning

confidence: 99%

Using Blockchain Technology and Sharing Culture to Promote Sustainable Forest Management in Tribal Communities

Peng

Huang

2022

Journal of Environmental and Public Health

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Forest protection is crucial to ensuring the balance between human beings and ecology. This study explores the key role played by communities that originally lived in forest-protected areas in implementing the traditional management of forests. The unified management mode previously used by the state power can no longer meet the demands of modern times; hence, multiple types of management systems should be implemented to enable adaption to the original ecology of forest areas. A multimodal management mode should be adopted to restore the original ecology of forest areas. The adoption of this management system can restore a forest to its original state (i.e., the state that existed prior to the entry of state power). The forest has been in a state of ecological balance involving numerous species since ancient times. However, in the modern field of science, the passive restoration of a community’s self-governance ability could be unsustainable and unstable. To improve this situation, blockchain technology can first be used to improve the community management of a forest, such that the capabilities of the original local community can be improved; second, tourism promotion benefits both the community and the forest. When a community in a forest develops the tourism industry with the support of blockchain technology, the information and resources of all parties can be widely connected with the larger world, and this considerably increases success rates; finally, the traditional spiritual culture of a community, such as the culture of sharing, should be promoted. In addition to the skillful utilization of technology, culture can improve the traditional forest management ability of tribal communities who live in native forest areas in terms of their personality traits. Overall, we conclude that: against the evolution history of the over one hundred years, the adoption of new technology for forest management is inherently a creative innovation for the tribal community’s entrepreneurial development.

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“…This section discusses the most frequently DL architectures, including Convolutional Neural Networks (CNNs) [9] , Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) [10] , Encoder-Decoders (EDs) [11] , and Generative Adversarial Networks (GANs) [12] . Numerous upgrades have been proposed in response to the sudden popularity growth of DL, including capsule networks, attentions, and deep belief networks.…”

Section: History Of Deep Learningmentioning

confidence: 99%

Deep Learning Methods Used in Remote Sensing Images: A Review

Rewehel¹,

Li²,

Hamed³

et al. 2023

j. of environ. & earth. sci.

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Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a wide range of modern applications. However, there are some challenges related to the availability of annotated data, the complex nature of data, and model parameterization, which strongly impact performance. In this article, a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures (vintage Convolutional Neural Network, CNN; Fully Convolutional Networks, FCN; encoder-decoder, recurrent networks; attention models, and generative adversarial models). The characteristics, capabilities, and limitations of current DL models were examined, and potential research directions were discussed.

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Deep Feature Extraction and Feature Fusion for Bi-temporal Satellite Image Classification

Cited by 19 publications

References 38 publications

Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure

Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure

Using Blockchain Technology and Sharing Culture to Promote Sustainable Forest Management in Tribal Communities

Deep Learning Methods Used in Remote Sensing Images: A Review

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