M-FCN: Effective Fully Convolutional Network-Based Airplane Detection Framework

Yang, Yiding; Zhuang, Yin; Bi, Fukun; Shi, Hao; Xie, Yizhuang

doi:10.1109/lgrs.2017.2708722

Cited by 50 publications

(28 citation statements)

References 10 publications

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“…This dataset contains a total of 3775 object instances which are manually annotated with horizontal bounding boxes, including 757 airplanes, 390 baseball diamonds, 159 basketball courts, 124 bridges, 224 harbors, 163 ground track fields, 302 ships, 655 storage tanks, 524 tennis courts, and 477 vehicles. This dataset has been widely used in the earth observation community Cheng et al, 2018b;Farooq et al, 2017;Guo et al, 2018;Han et al, 2017a;Li et al, 2018;Yang et al, 2018b;Yang et al, 2017;Zhong et al, 2018). 4) VEDAI: The VEDAI (Razakarivony and Jurie, 2015) dataset is released for the task of multi-class vehicle detection in aerial images.…”

Section: Object Detection Datasets Of Optical Remote Sensing Imagesmentioning

confidence: 99%

“…Object detection plays a crucial role in image interpretation and also is very important for a wide scope of applications, such as intelligent monitoring, urban planning, precision agriculture, and geographic information system (GIS) updating. Driven by this requirement, significant efforts have been made in the past few years to develop a variety of methods for object detection in optical remote sensing images (Aksoy, 2014;Bai et al, 2014;Cheng et al, 2013a;Cheng and Han, 2016;Cheng et al, 2013b;Cheng et al, 2014;Cheng et al, 2019;Cheng et al, 2016a;Das et al, 2011;Han et al, 2015;Han et al, 2014;Li et al, 2018;Long et al, 2017;Tang et al, 2017b;Yang et al, 2017;Zhang et al, 2016;Zhang et al, 2017;Zhou et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Object detection in optical remote sensing images: A survey and a new benchmark

Wan

Cheng

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

1,243

425

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Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset contains 23463 images and 192472 instances, covering 20 object classes. The proposed DIOR dataset 1) is large-scale on the object categories, on the object instance number, and on the total image number; 2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter-and intra-class size variability across objects; 3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and 4) has high inter-class similarity and intra-class diversity. The proposed benchmark can help the researchers to develop and validate their data-driven methods. Finally, we evaluate several state-of-theart approaches on our DIOR dataset to establish a baseline for future research.

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Section: Object Detection Datasets Of Optical Remote Sensing Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Object detection in optical remote sensing images: A survey and a new benchmark

Wan

Cheng

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

1,243

425

View full text Add to dashboard Cite

show abstract

“…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%

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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“…Of these layers, convolutional layers can identify the local features of images; pooling layers compress numerous features to extract the main features; activation layers effectively express nonlinear features; and concatenation layers integrate features over multiple scales (LeCun et al 1990, 1998, 2015, Szegedy et al 2014. Through transmitting information between multiple convolutional layers, pooling layers, activation layers and concatenation layers, FCNs effectively integrate remote sensing data from multiple sources and features over multiple scales to identify objects accurately (Chen et al 2017, Yang et al 2017. Furthermore, FCNs have the ability to transfer leaning, which enables the extraction of dynamic information using parameters obtained from training samples in a single period.…”

Section: Introductionmentioning

confidence: 99%

Detecting global urban expansion over the last three decades using a fully convolutional network

Liu

Gou

et al. 2019

Environ. Res. Lett.

123

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The effective detection of global urban expansion is the basis of understanding urban sustainability. We propose a fully convolutional network (FCN) and employ it to detect global urban expansion from 1992-2016. We found that the global urban land area increased from 274.7 thousand km 2 -621.1 thousand km 2 , which is an increase of 346.4 thousand km 2 and a growth by 1.3 times. The results display a relatively high accuracy with an average kappa index of 0.5, which is 0.3 higher than those of existing global urban expansion datasets. Three major advantages of the proposed FCN contribute to the improved accuracy, including the integration of multi-source remotely sensed data, the combination of features at multiple scales, and the ability to address the lack of training samples for historical urban land. Thus, the proposed FCN has great potential to effectively detect global urban expansion. OPEN ACCESS RECEIVED

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M-FCN: Effective Fully Convolutional Network-Based Airplane Detection Framework

Cited by 50 publications

References 10 publications

Object detection in optical remote sensing images: A survey and a new benchmark

Object detection in optical remote sensing images: A survey and a new benchmark

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

Detecting global urban expansion over the last three decades using a fully convolutional network

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