Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks

Liu, Yibo; Zhang, Zhenxin; Zhong, Rugang; Chen, Dong; Ke, Yinghai; Peethambaran, Jiju; Chen, Chuqun; Sun, Lan

doi:10.1109/jstars.2018.2866284

Cited by 42 publications

(25 citation statements)

References 52 publications

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“…However, the aforementioned models can not be directly utilized for geospatial object detection, because the properties of remote sensing images and natural images are different and the direct application of those models to remote sensing images is not optimal. Researchers have done a lot of work in applying CNN-based models to detect geospatial objects in remote sensing images and achieved remarkable consequences [4,[15][16][17][18][19][20][21][22][23][24][25]45]. For example, the work in [4] utilized a hyperregion proposal network (HRPN) and a cascade of boosted classifiers to detect vehicles in remote sensing images.…”

Section: Geospatial Object Detectionmentioning

confidence: 99%

“…Therefore, it is important for us to choose a method to extract features for object detection in remote sensing images. Currently, because of the advantage of directly generating more powerful feature representations from raw image pixels through neural networks, deep learning methods, especially CNN-based [4,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], are recognized as predominate techniques for extracting features in object detection. Therefore, we select a CNN-based approach to extract features for object detection in optical remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

Guo

et al. 2019

Remote Sensing

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Object detection in optical remote sensing images is still a challenging task because of the complexity of the images. The diversity and complexity of geospatial object appearance and the insufficient understanding of geospatial object spatial structure information are still the existing problems. In this paper, we propose a novel multi-model decision fusion framework which takes contextual information and multi-region features into account for addressing those problems. First, a contextual information fusion sub-network is designed to fuse both local contextual features and object-object relationship contextual features so as to deal with the problem of the diversity and complexity of geospatial object appearance. Second, a part-based multi-region fusion sub-network is constructed to merge multiple parts of an object for obtaining more spatial structure information about the object, which helps to handle the problem of the insufficient understanding of geospatial object spatial structure information. Finally, a decision fusion is made on all sub-networks to improve the stability and robustness of the model and achieve better detection performance. The experimental results on a publicly available ten class data set show that the proposed method is effective for geospatial object detection.

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Section: Geospatial Object Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

Guo

et al. 2019

Remote Sensing

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“…In each box and whisker plot, the x-axis denotes the cities and the y-axis stands for metric values (%). At present, tens of studies are conducted on the IAIL database [50][51][52][53][54][55][56][57][58]. Several studies [50][51][52][53][54] follow a common practice as [40] that the first 5 images of each city are used for testing.…”

Section: Algorithm Implementationmentioning

confidence: 99%

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

Wen

Jin

et al. 2019

Sensors

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The segmentation of buildings in remote-sensing (RS) images plays an important role in monitoring landscape changes. Quantification of these changes can be used to balance economic and environmental benefits and most importantly, to support the sustainable urban development. Deep learning has been upgrading the techniques for RS image analysis. However, it requires a large-scale data set for hyper-parameter optimization. To address this issue, the concept of “one view per city” is proposed and it explores the use of one RS image for parameter settings with the purpose of handling the rest images of the same city by the trained model. The proposal of this concept comes from the observation that buildings of a same city in single-source RS images demonstrate similar intensity distributions. To verify the feasibility, a proof-of-concept study is conducted and five fully convolutional networks are evaluated on five cities in the Inria Aerial Image Labeling database. Experimental results suggest that the concept can be explored to decrease the number of images for model training and it enables us to achieve competitive performance in buildings segmentation with decreased time consumption. Based on model optimization and universal image representation, it is full of potential to improve the segmentation performance, to enhance the generalization capacity, and to extend the application of the concept in RS image analysis.

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“…Remote sensing technology analysis of ground objects and their changes with time and space have great application significance in the fields of military reconnaissance, economic construction, meteorological forecasting [5][6][7][8] and earthquake disaster early warning [9], which are related to the national economy and people's livelihood. A large amount of urban geographic information contained in remote sensing images, which can be used in many fields, such as digital cities, intelligent transportation, navigation maps, and urban planning [10,11]. Therefore, automatic analysis of remote sensing images has become an important research topic for geospatial information detection.…”

Section: Introductionmentioning

confidence: 99%

MFCSNet: Multi-Scale Deep Features Fusion and Cost-Sensitive Loss Function Based Segmentation Network for Remote Sensing Images

Wang

Jiang

et al. 2019

Applied Sciences

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Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.

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Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks

Cited by 42 publications

References 52 publications

A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

MFCSNet: Multi-Scale Deep Features Fusion and Cost-Sensitive Loss Function Based Segmentation Network for Remote Sensing Images

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