Urban Land Use Classification Using Street View Images Based on Deep Transfer Network

Yu, Yafang; Fang, Fang; Liu, Yuanyuan; Li, Shengwen; Luo, Zhiyong

doi:10.1007/978-3-030-45099-1_7

Cited by 3 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Classification method Application Texture and spatial metrics [3] Fisher linear discriminant Urban land use classification geometrical, textural, and contextual information [4] Decision tree, Neural network, Majority rule-based naive model, Urban land use classification Spectral indices (NDVI, MNDWI) [5] random forests, SVM, Extreme gradient boosting, Deep learning complex mixed-use landscape classification Object properties: shape, texture, color [6] mean-shift-based multi-scale segmentation Multi-scale image segmentation Textual features and spectral indices [9] Random Forest Land Use and Coverage Area frame Survey Semantic features extracted from Deep convolutional neural networks [12] contextual-based convolutional neural network with deep architecture and pixel-based multi-layer perceptron neural network (MLP) rule-based decision fusion approach Very fine spatial resolution (VFSR) remotely sensed imagery classification Semantic features extracted form deep convolutional neural networks [13] different CNN-based models remote sensing land use classification Transfer learning using ImageNet dataset [14] Fully connected layers classifiers street view images classification…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…CNNs have the advantages of less training parameters while providing robustness and high performance. That the features obtained by learning can transcend handcrafted feature as demonstrated in several researches and datasets [12]- [14]. AlexNet architecture includes multiple hidden layers: an input layer, five convolutional layers, first, second and fifth of which are followed by pooling layers (3 layers), three fully connected layers, and an output layer.…”

Section: Semantic Featuresmentioning

confidence: 99%

“…Using transfer learning to empower the learning process have allowed to utilize features extracted form large dataset in a source domain to classify different and related datasets in a target domain. For instance, for urban land use classification, authors proposed in [14] a classification method based on high-level features obtained by transfer learning form ImageNet dataset. Three fully connected layers were applied for the classification task.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Classification using semantic feature and machine learning: Land-use case application

Elmannai

Algarni

2021

TELKOMNIKA

View full text Add to dashboard Cite

Land cover classification has interested recent works especially for deforestation, urban are monitoring and agricultural land use. Traditional classification approaches have limited accuracy especially for nonheterogeneous land cover. Thus, using machine may improve the classification accuracy. The presented paper deals with the land-use scene recognition on very high-resolution remote sensing imagery. We proposed a new framework based on semantic features, handcrafted features and machine learning classifiers decisions. The method starts by semantic feature extraction using a convolutional neural network. Handcraft features are also extracted based on color and multi-resolution characteristics. Then, the classification stage is processed by three learning machine algorithms. The final classification result performed by majority vote algorithm. The idea behind is to take advantages from semantic features and handcrafted features. The second scope is to use the decision fusion to enhance the classification result. Experimentation results show that the proposed method provides good accuracy and trustable tool for land use image identification.

show abstract

Section: Feature Extraction Methodsmentioning

confidence: 99%

Section: Semantic Featuresmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Classification using semantic feature and machine learning: Land-use case application

Elmannai

Algarni

2021

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…The divergence measure becomes as detailed in ( 6) [21]. The resulting mapping of the sources to the observations is presented in (7). As shown in ( 8) and ( 9) detail th unknown parameters distributions.…”

Section: F(s(t)) = B Tanh(as(t) + A) + B+ N(t)mentioning

confidence: 99%

“…Table 1. Features extraction approaches for land cover classification Spatial metrics and Texture measures for land cover objects classification [1] Parcels geometrical attributes including shape, height, proximity to major roads, similarity to neighbors [2] Spectral indices (NDVI, MNDWI, NDBI) [3] Object-based feature extraction based on spatial and spectral statistics [4] High dimensional feature vector combining focal textures statistics (median, mean, and standard deviation) and Gray Level Co-occurrence Matrix derived in different kernel sizes [5] Semantic features extraction using different deep convolutional neural networks models [6] High-level semantic features extraction based on transfer learning and the Inception-ResNet-v2 model [7] Deep semantic feature extraction using different models (VGG-S , VGG-M, VGG-F, VGG-VD16, VGG-VD16) [8] Combining deep semantic features, spectral features and GLCM texture features [9] Vision-based technology have been widely used for object of interest detection. For instance, in [10] the detecting task is based on histogram equalization and morphological processings.…”

Section: Introductionmentioning

confidence: 99%

A new feature extraction approach based on non linear source separation

Elmannai

Naceur

Loghmari

et al. 2021

IJECE

View full text Add to dashboard Cite

A new feature extraction approach is proposed in this paper to improve the classification performance in remotely sensed data. The proposed method is based on a primary sources subset (PSS) obtained by nonlinear transform that provides lower space for land pattern recognition. First, the underlying sources are approximated using multilayer neural networks. Given that, Bayesian inferences update unknown sources’ knowledge and model parameters with information’s data. Then, a source dimension minimizing technique is adopted to provide more efficient land cover description. The support vector machine (SVM) scheme is developed by using feature extraction. The experimental results on real multispectral imagery demonstrates that the proposed approach ensures efficient feature extraction by using several descriptors for texture identification and multiscale analysis. In a pixel based approach, the reduced PSS space improved the overall classification accuracy by 13% and reaches 82%. Using texture and multi resolution descriptors, the overall accuracy is 75.87% for the original observations, while using the reduced source space the overall accuracy reaches 81.67% when using jointly wavelet and Gabor transform and 86.67% when using Gabor transform. Thus, the source space enhanced the feature extraction process and allow more land use discrimination than the multispectral observations.

show abstract

Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review

Yang

Zhu

et al. 2023

Sensors

View full text Add to dashboard Cite

Artificial intelligence technologies such as computer vision (CV), machine learning, Internet of Things (IoT), and robotics have advanced rapidly in recent years. The new technologies provide non-contact measurements in three areas: indoor environmental monitoring, outdoor environ-mental monitoring, and equipment monitoring. This paper summarizes the specific applications of non-contact measurement based on infrared images and visible images in the areas of personnel skin temperature, position posture, the urban physical environment, building construction safety, and equipment operation status. At the same time, the challenges and opportunities associated with the application of CV technology are anticipated.

show abstract

Urban Land Use Classification Using Street View Images Based on Deep Transfer Network

Cited by 3 publications

References 21 publications

Classification using semantic feature and machine learning: Land-use case application

Classification using semantic feature and machine learning: Land-use case application

A new feature extraction approach based on non linear source separation

Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review

Contact Info

Product

Resources

About