Scene Classification of High-Resolution Remotely Sensed Image Based on ResNet

Wang, Mingchang; Zhang, Xinyue; Niu, Xuefeng; Wang, Fengyan; Zhang, Xuqing

doi:10.1007/s41651-019-0039-9

Cited by 75 publications

(53 citation statements)

References 12 publications

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“…This process is laborious, and the survey accuracy is subject to subjective factors. The emergence of 2 of 18 massive urban big data has created new opportunities for urban geographical computing and analysis, and has provided abundant means for the identification of urban functional regions [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data

Wang

et al. 2019

Sustainability

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Along with the rapid development of China’s economy as well as the continuing urbanization, the internal spatial and functional structures of cities within this country are also gradually changing and restructuring. The study of functional region identification of a city is of great significance to the city’s functional cognition, spatial planning, economic development, human livability, and so forth. Backed by the emerging urban Big Data, and taking the traffic community as the smallest research unit, a method is proposed to identify urban functional regions by combining floating car track data with point of interest (POI) data recorded on an electronic map. It provides a new perspective for the study of urban functional region identification. Firstly, the main functional regions of the city studied are identified through clustering analysis according to the passenger’s spatial-temporal travel characteristics derived from the floating car data. Secondly, the fine-grained identification of the functional region attributes of the traffic communities is achieved using the label information from POI data. Finally, the AND-OR operation is performed on the recognition results derived by the clustering algorithm and the Delphi method, to obtain the identification of urban functional regions. This approach is verified by applying it to the main urban zone within Chengdu’s Third Ring Road. The results show that: (1) There are fewer single functional regions and more mixed functional regions in the main urban zone of Chengdu, and the distribution of the functional regions are roughly concentric centering in the city center. (2) Using the traffic community as a research unit, combined with dynamic human activity trajectory data and static urban interest point data, complex urban functional regions can be effectively identified.

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Section: Introductionmentioning

confidence: 99%

Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data

Wang

et al. 2019

Sustainability

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“…Many researchers proposed solutions for this problem in their researches but they depend on the deep learning only or the machine learning only. Few of them are proposed hybrid classification techniques that consist of combination of the deep learning and the machine learning such as [14] and [21]. The deep learning and the machine learning combination can lead to considerable results.…”

Section: Introductionmentioning

confidence: 99%

Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images

Al-Afandy¹,

Johra²,

Lazaar³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Feature extraction is an important process in image classification for achieving an efficient accuracy for the classification learning models. One of these methods is using the convolution neural networks. The use of the trained classic deep convolution neural networks as features extraction gives a considerable results in the remote sensing images classification models. So, this paper proposes three classification approaches using the support vector machine where based on the use of the ImageNet pre-trained weights classic deep convolution neural networks as features extraction from the remote sensing images. There are three convolution models that used in this paper; the Densenet 169, the VGG 16, and the ResNet 50 models. A comparative study is done by extract features using the outputs of the mentioned ImageNet pre-trained weights convolution models after transfer learning, and then use these extracted features as input features for the support vector machine classifier. The used datasets in this paper are the UC Merced land use dataset and the SIRI-WHU dataset. The comparison is based on calculating the overall accuracy to assess the classification model performance.

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“…They fine-tuned their model parameters by using the ImageNet pre-trained VGG weights. In [19], authors proposed the use of the ResNet model to generate a ground scene semantics feature from the VHR remote sensing images, then concatenated with low level features to generate a more accurate model. In [20], authors proposed a classification method based on collaborate the 3-D separable ResNet model with cross-sensor transfer learning for hyper-spectral remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Using Classic Networks for Classifying Remote Sensing Images: Comparative Study

Al-Afandy¹,

Lazaar²,

Achhab³

2020

Adv. sci. technol. eng. syst. j.

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This paper presents a comparative study for using the deep classic convolution networks in remote sensing images classification. There are four deep convolution models that used in this comparative study; the DenseNet 196, the NASNet Mobile, the VGG 16, and the ResNet 50 models. These learning convolution models are based on the use of the ImageNet pretrained weights, transfer learning, and then adding a full connected layer that compatible with the used dataset classes. There are two datasets are used in this comparison; the UC Merced land use dataset and the SIRI-WHU dataset. This comparison is based on the inspection of the learning curves to determine how well the training model is and calculating the overall accuracy that determines the model performance. This comparison illustrates that the use of the ResNet 50 model has the highest overall accuracy and the use of the NASNet Mobile model has the lowest overall accuracy in this study. The DenseNet 169 model has little higher overall accuracy than the VGG 16 model.

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Scene Classification of High-Resolution Remotely Sensed Image Based on ResNet

Cited by 75 publications

References 12 publications

Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data

Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data

Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images

Using Classic Networks for Classifying Remote Sensing Images: Comparative Study

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