SO–CNN based urban functional zone fine division with VHR remote sensing image

Zhou, Wen; Ming, Dongping; Lv, Xianwei; Zhou, Kun; Bao, Hanqing; Hong, Zhaoli

doi:10.1016/j.rse.2019.111458

Cited by 122 publications

(51 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al applied multi-scale CNN and scale parameter estimation in land cover classification [21]. Zhou et al proposed So-CNN for urban functional zone fine division with VHR remote sensing images [22]. Lv et al proposed a new method for region-based majority voting CNNs for very high-resolution image classification [23].…”

Section: Introductionmentioning

confidence: 99%

BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

et al. 2020

View full text Add to dashboard Cite

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.

show abstract

Section: Introductionmentioning

confidence: 99%

BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Zhou et al used the SO-CNN method to classify urban functional areas based on remote sensing images. They found that it is highly significant in the study of small-scale functional spatial structures [29]. Cheng et al proposed discriminative CNNs (D-CNNs) to classify remote sensing image scenes, which solved the problems of within-class diversity and between-class similarity.…”

Section: Related Workmentioning

confidence: 99%

Classification of Urban Functional Areas From Remote Sensing Images and Time-Series User Behavior Data

Chen

Yan

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

“…DL models possess evident advantages in performance as high-level features of large dataset can be fully excavated, based on which non-linear relationships can be represented sufficiently. As the most mature DL framework, convolutional neural networks (CNN) have been widely used in geoscience domain, such as scene classification [36], land-cover classification [37][38][39][40], lithological facies classification [41,42], functional zone division [43] and ground target detection [44][45][46]. In recent years, CNN-based methods has been applied in landslide-related domain, especially in landslide detection [47][48][49][50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong

Chen

Ming

Xiao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

Landslide susceptibility mapping (LSM) is an effective way to predict spatial probability of landslide occurrence. Existing convolutional neural networks (CNN)-based methods apply self-built CNN with simple structure, which failed to reach CNN's full potential on high-level feature extraction, meanwhile ignored the use of numerical predisposing factors. For the purpose of exploring feature fusion based CNN models with greater reliability in LSM, this study proposes an ensemble model based on channel-expanded pre-trained CNN and traditional machine learning model (CPCNN-ML). In CPCNN-ML, pre-trained CNN with mature structure is modified to excavate high-level features of multi-channel predisposing factor layers. LSM result is generated by traditional machine learning (ML) model based on hybrid feature of high-level features and numerical predisposing factors. Lantau Island, Hong Kong is selected as study area, temporal landslide inventory is used for model training and evaluation. Experimental results show that CPCNN-ML has ability to predict landslide occurrence with high reliability, especially the CPCNN-ML based on random forest (RF). Contrast experiments with self-built CNN and traditional ML models further embody the superiority of CPCNN-ML. It is worth noting that coastal regions are newly identified landslide-prone regions compared with previous research. This finding is of great reference value for Hong Kong authorities to formulate appropriate disaster prevention and mitigation policies.

show abstract

SO–CNN based urban functional zone fine division with VHR remote sensing image

Cited by 122 publications

References 44 publications

BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

Classification of Urban Functional Areas From Remote Sensing Images and Time-Series User Behavior Data

Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong

Contact Info

Product

Resources

About