Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares

Kuo, Chiao-Ling; Tsai, Ming-Hua

doi:10.3390/ijgi10060377

Cited by 8 publications

(3 citation statements)

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“…With the rapid development of CNN, more and more feature extraction networks have shown strong feature extraction ability. Many of them can be applied to existing computer vision tasks such as object detection [41], land-cover classification [42][43][44], and image matching [45][46][47]. For change detection tasks that may be regarded as pixel-level classification problems, a fully convolutional layer rather than a fully connected layer could achieve this [48].…”

Section: Feature Extractormentioning

confidence: 99%

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

Zhang

2021

IJGI

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Change detection based on bi-temporal remote sensing images has made significant progress in recent years, aiming to identify the changed and unchanged pixels between a registered pair of images. However, most learning-based change detection methods only utilize fused high-level features from the feature encoder and thus miss the detailed representations that low-level feature pairs contain. Here we propose a multi-level change contextual refinement network (MCCRNet) to strengthen the multi-level change representations of feature pairs. To effectively capture the dependencies of feature pairs while avoiding fusing them, our atrous spatial pyramid cross attention (ASPCA) module introduces a crossed spatial attention module and a crossed channel attention module to emphasize the position importance and channel importance of each feature while simultaneously keeping the scale of input and output the same. This module can be plugged into any feature extraction layer of a Siamese change detection network. Furthermore, we propose a change contextual representations (CCR) module from the perspective of the relationship between the change pixels and the contextual representation, named change region contextual representations. The CCR module aims to correct changed pixels mistakenly predicted as unchanged by a class attention mechanism. Finally, we introduce an effective sample number adaptively weighted loss to solve the class-imbalanced problem of change detection datasets. On the whole, compared with other attention modules that only use fused features from the highest feature pairs, our method can capture the multi-level spatial, channel, and class context of change discrimination information. The experiments are performed with four public change detection datasets of various image resolutions. Compared to state-of-the-art methods, our MCCRNet achieved superior performance on all datasets (i.e., LEVIR, Season-Varying Change Detection Dataset, Google Data GZ, and DSIFN) with improvements of 0.47%, 0.11%, 2.62%, and 3.99%, respectively.

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Section: Feature Extractormentioning

confidence: 99%

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

Zhang

2021

IJGI

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“…The road is one of the most important elements in OSM, and it is the basis of numerous applications such as navigation and network analysis [23][24][25][26]. Therefore, the tag recommendation of the OSM road network is of particular importance.…”

Section: Introductionmentioning

confidence: 99%

MSC-DeepFM: OSM Road Type Prediction via Integrating Spatial Context Using DeepFM

Zhao,

Ning,

et al. 2023

Sustainability

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The quality of OpenStreetMap (OSM) has been widely concerned as a valuable source for monitoring some sustainable development goals (SDG) indicators. Improving its semantic quality is still challenging. As a kind of solution, road type prediction plays an important role. However, most existing algorithms show low accuracy, owing to data sparseness and inaccurate description. To address these problems, we propose a novel OSM road type prediction approach via integrating multiple spatial contexts with DeepFM, named MSC-DeepFM. A deep learning model DeepFM is used for dealing with data sparseness. Moreover, multiple spatial contexts (MSC), including the features of intersecting roads, surrounding buildings, and points of interest (POIs), are distilled to describe multiple types of road more accurately. The MSC combined with geometric features and restricted features are put into DeepFM, in which the low-order and high-order features fully interact. And a multivariate classifier OneVsRest is adopted to predict road types. Experiments on OSM show that the proposed model MSC-DeepFM achieves excellent performance and outperforms some state-of-the-art methods.

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“…Automatic road extraction methods represented by deep learning have been widely reported in previous studies [1,[15][16][17]. For example, Gao et al extracted the roads from optical satellite images using a refined deep residual convolutional neural network with a post-processing stage [2].…”

Section: Introductionmentioning

confidence: 99%

Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images

et al. 2022

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Road network extraction plays a critical role in data updating, urban development, and decision support. To improve the efficiency of labeling road datasets and addressing the problems of traditional methods of manually extracting road networks from high-resolution images, such as their slow speed and heavy workload, this paper proposes a semi-automatic method of road network extraction from high-resolution remote-sensing images. The proposed method needs only a few points to extract a single road in the image. After the roads are extracted one by one, the road network is generated according to the width of each road and the spatial relationships among the roads. For this purpose, we use regional growth, morphology, vector tracking, vector simplification, endpoint modification, road connections, and intersection connections to generate road networks. Experiments on four images with different terrains and different resolutions show that this method has high extraction accuracy under different image conditions. The comparisons with the semi-automatic GVF-snake method based on regional growth also showed its advantages and potentiality. The proposed method is a novel form of semi-automatic road network extraction, and it significantly increases the efficiency of road network extraction.

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Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares

Cited by 8 publications

References 50 publications

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

MSC-DeepFM: OSM Road Type Prediction via Integrating Spatial Context Using DeepFM

Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images

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