A Framework for Land Use Scenes Classification Based on Landscape Photos

Xu, Shiwu; Zhang, Shihui; Zeng, Jue; Li, Tingyu; Jin, Shichao

doi:10.1109/jstars.2020.3028158

Cited by 12 publications

(10 citation statements)

References 37 publications

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“…Among them, the LIDAR-based geographic mapping method obtains higher accuracy of spatial land use classification planning and is closer to the real environment, but the reconstructed effect of this method lacks texture and only reflects 3D spatial information, and the cost is higher; the reconstructed texture of RGBD camera-based method is clearer, but it is not suitable for geographically complex the large-scale mapping. In contrast, the land use spatial classification planning method of GIS based on multivision stereo matching can obtain 3D information from 2D images by simulating human binoculars and using the principle of stereo vision to adapt to complex geographic environments and has the advantages of automatic, online, noncontact detection, high flexibility, low cost, and clear texture, which can be used to build 3D land use spatial classification planning for geographically large-scale environments [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Research on Land Utilization Spatial Classification Planning Method Based on Multiocular Vision

Zhang

Wang

2022

Computational and Mathematical Methods in Medicine

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With the development of China’s social economy as well as the accelerating urbanization construction and the expanding scale of cities, the integration of land use and urban land classification based on land use spatial planning has become an important task for the sustainable development of China at present. Land use spatial classification planning is the basic basis for all kinds of development and protection construction activities, and government land use spatial planning at all levels plays an important role in implementing major national, provincial, and municipal strategies and promoting the rational and effective use of land use space. By briefly describing the spatial classification of land use and analyzing the idea of systematic integration of land use, this paper provides guidance and reference for exploring the construction of urban land use classification under land use spatial planning, aiming to improve the classification system of land use spatial planning. A neural network-based land use classification algorithm is proposed for the problems of few labeled samples of remote sensing images with high spatial resolution and feature deformation due to sensor height changes in land use spatial classification planning. By multiscale adaptive fusion of multiple convolutional layer features, the impact of feature deformation on classification accuracy is reduced. To further improve the classification accuracy, the depth features extracted from the pretraining network are used to pretrain the multiscale feature fusion part and the fully connected layer, and the whole network is fine-tuned using the augmented dataset. The experimental results show that the adaptive fusion method improves the fusion effect and effectively improves the accuracy of land use spatial classification planning.

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Section: Introductionmentioning

confidence: 99%

Research on Land Utilization Spatial Classification Planning Method Based on Multiocular Vision

Zhang

Wang

2022

Computational and Mathematical Methods in Medicine

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“…Deep filter banks were proposed to combine multicolumn stacked denoising sparse autoencoders (SDSAE) and Fisher vectors (FV) to automatically learn the representative and discriminative features in a hierarchical manner for land-use scene classification [29]. Xu et al, proposed a land-use classification framework for photos (LUCFP) and successfully applied it to the automatic verification of land surveys in China [30].Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR ortho mosaic and digital surface model (DSM). This framework exhibited excellent potential for the detailed mapping of heterogeneous urban landscapes [31].…”

Section: Land-use Classificationmentioning

confidence: 99%

A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data

Zeng

et al. 2022

Remote Sensing

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The classification of urban land-use information has become the underlying database for a variety of applications including urban planning and administration. The lack of datasets and changeable semantics of land-use make deep learning methods suffer from low precision, which prevent improvements in the effectiveness of using AI methods for applications. In this paper, we first used GIS data to produce a well-tagged and high-resolution urban land-use image dataset. Then, we proposed a combined convolutional neural network named DUA-Net for complex and diverse urban land-use classification. The DUA-Net combined U-Net and Densely connected Atrous Spatial Pyramid Pooling (DenseASPP) to extract Remote Sensing Imagers (RSIs) features in parallel. Then, channel attention was used to efficiently fuse the multi-source semantic information from the output of the double-layer network to learn the association between different land-use types. Finally, land-use classification of high-resolution urban RSIs was achieved. Experiments were performed on the dataset of this paper, the publicly available Vaihingen dataset and Potsdam dataset with overall accuracy levels reaching 75.90%, 89.71% and 89.91%, respectively. The results indicated that the complex land-use types with heterogeneous features were more difficult to extract than the single-feature land-cover types. The proposed DUA-Net method proved suitable for high-precision urban land-use classification, which will be of great value for urban planning and national land resource surveying.

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“…Recall [102] Sensitivity [101] True Positive Rate (TPR) [101] Overall Accuracy [103] Detection Probability [68] Hit Rate [104] TP TP+FN PA for positives Precision [102] Positive Predictive Value (PPV) [101] TP TP+FP UA for positives Specificity [105] True Negative Rate (TNR) [101] TN TN+FP PA for negatives Negative Predictive Value (NPV) [101] TN TN+FN UA for negatives False Positive Rate (FPR) [106] Probability of False Detection [107] False Alarm Probability [100] FP TN+FP 1− (PA for negatives) False Negative Rate (FNR) 1 Missing Detection Probability [100] Missing Alarm [108] Misidentification Score [109] FN TP+FN 1− (PA for positives) False Discovery Rate (FDR) 1 False Alarm Probability [68] Commission Error [110] FP TP+FP 1− (UA for positives) Balanced Accuracy [101] Intersection-over-Union (IoU) [99] Jaccard Index [115] Figure 3 above summarizes the frequency at which each accuracy measure is used by papers that focus on binary and multiclass classification types, as well as by scene classification, object detection, semantic segmentation, and instance segmentation applications. A comparison of the graphs indicates that some measures (for example, precision and recall) are used for all types of classification applications, although it is notable that no single measure is used by every single study, even within one category of applications (e.g., multiclass scene identification).…”

Section: Overall Accuracymentioning

confidence: 99%

“…Similarly, when studies refer to the same metric by different names in different parts of the paper (e.g., the text and tables [105]), or, in some cases, even in the same parts of the paper (e.g., within a single table [68]), communication may also be undermined. The problem is particularly acute when studies compare the ROC and P-R graphs, usually using true positive rate for the ROC, but recall for the same accuracy measure in the P-R graph [106,117,118,152]. Therefore, to the extent that it is possible, it would be preferable for studies to use the most common names in Table 6 (typically, the left column), rather than less common names.…”

Section: Clarity In Terminologymentioning

confidence: 99%

Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review

2021

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Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently developed image classification approach. With origins in the computer vision and image processing communities, the accuracy assessment methods developed for CNN-based DL use a wide range of metrics that may be unfamiliar to the remote sensing (RS) community. To explore the differences between traditional RS and DL RS methods, we surveyed a random selection of 100 papers from the RS DL literature. The results show that RS DL studies have largely abandoned traditional RS accuracy assessment terminology, though some of the accuracy measures typically used in DL papers, most notably precision and recall, have direct equivalents in traditional RS terminology. Some of the DL accuracy terms have multiple names, or are equivalent to another measure. In our sample, DL studies only rarely reported a complete confusion matrix, and when they did so, it was even more rare that the confusion matrix estimated population properties. On the other hand, some DL studies are increasingly paying attention to the role of class prevalence in designing accuracy assessment approaches. DL studies that evaluate the decision boundary threshold over a range of values tend to use the precision-recall (P-R) curve, the associated area under the curve (AUC) measures of average precision (AP) and mean average precision (mAP), rather than the traditional receiver operating characteristic (ROC) curve and its AUC. DL studies are also notable for testing the generalization of their models on entirely new datasets, including data from new areas, new acquisition times, or even new sensors.

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A Framework for Land Use Scenes Classification Based on Landscape Photos

Cited by 12 publications

References 37 publications

Research on Land Utilization Spatial Classification Planning Method Based on Multiocular Vision

Research on Land Utilization Spatial Classification Planning Method Based on Multiocular Vision

A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data

Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review

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