Improving remote sensing classification: A deep-learning-assisted model

Davydzenka, Tsimur; Tahmasebi, Pejman; Carroll, Mark J.

doi:10.1016/j.cageo.2022.105123

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…On the other hand, deep learning has obvious advantages in image feature extraction, firstly, the main feature and advantage of deep learning is that it can greatly improve the interpretation of data by learning the inherent laws and representation levels of sample data so as to achieve accurate extraction of image features [72]. Additionally, the high-efficiency algorithm replaces the previous manual calculation, which makes the extraction of image feature information simpler and more efficient [73,74].…”

Section: ) Image Feature Extraction Based On U-netmentioning

confidence: 99%

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Chen

Deng

2022

IEEE Access

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The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas.

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Section: ) Image Feature Extraction Based On U-netmentioning

confidence: 99%

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Chen

Deng

2022

IEEE Access

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“…Chamundeeswari et al (2022) classified crops using the deep convolutional neural network crop classification model, yielding results superior to other methods. Davydzenka et al (2022) markedly enhanced the remote sensing classification accuracy of machine learning by combining images to generate additional image training data sets. Employing the iterative CART algorithm, Wu et al (2019) extracted land cover types in sequence, which significantly improved the accuracy by minimizing the phenomenon of mixed division at different levels.…”

Section: Introductionmentioning

confidence: 99%

Land use classification in mine-agriculture compound area based on multi-feature random forest: a case study of Peixian

Xu,

Chen,

Zhou

et al. 2024

Front. Sustain. Food Syst.

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IntroductionLand use classification plays a critical role in analyzing land use/cover change (LUCC). Remote sensing land use classification based on machine learning algorithm is one of the hot spots in current remote sensing technology research. The diversity of surface objects and the complexity of their distribution in mixed mining and agricultural areas have brought challenges to the classification of traditional remote sensing images, and the rich information contained in remote sensing images has not been fully utilized.MethodsA quantitative difference index was proposed quantify and select the texture features of easily confused land types, and a random forest (RF) classification method with multi-feature combination classification schemes for remote sensing images was developed, and land use information of the mine-agriculture compound area of Peixian in Xuzhou, China was extracted.ResultsThe quantitative difference index proved effective in reducing the dimensionality of feature parameters and resulted in a reduction of the optimal feature scheme dimension from 57 to 22. Among the four classification methods based on the optimal feature classification scheme, the RF algorithm emerged as the most efficient with a classification accuracy of 92.38% and a Kappa coefficient of 0.90, which outperformed the support vector machine (SVM), classification and regression tree (CART), and neural network (NN) algorithm.ConclusionThe findings indicate that the quantitative differential index is a novel and effective approach for discerning distinct texture features among various land types. It plays a crucial role in the selection and optimization of texture features in multispectral remote sensing imagery. Random forest (RF) classification method, leveraging a multi-feature combination, provides a fresh method support for the precise classification of intricate ground objects within the mine-agriculture compound area.

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Large-scale land use/land cover extraction from Landsat imagery using feature relationships matrix based deep-shallow learning

Dou,

Shen,

Huang

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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Improving remote sensing classification: A deep-learning-assisted model

Cited by 4 publications

References 27 publications

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Land use classification in mine-agriculture compound area based on multi-feature random forest: a case study of Peixian

Large-scale land use/land cover extraction from Landsat imagery using feature relationships matrix based deep-shallow learning

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