Use of Salient Features for the Design of a Multistage Framework to Extract Roads From High-Resolution Multispectral Satellite Images

Das, Sukhendu; Mirnalinee, T. T.; Varghese, Koshy

doi:10.1109/tgrs.2011.2136381

Cited by 207 publications

(121 citation statements)

References 63 publications

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“…SVM method represents each vector in a high dimensional space, mapped so that the samples of the different classes are separated by a clear gap that is as wide as possible. Support vector machines have been used in a lot of applications and have shown good overall results [14][15][16][17].…”

Section: Histogram Of Oriented Gradients + Support Vector Machinementioning

confidence: 99%

Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods

Villon

Chaumont

Subsol

et al. 2016

Lecture Notes in Computer Science

112

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Section: Histogram Of Oriented Gradients + Support Vector Machinementioning

confidence: 99%

Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods

Villon

Chaumont

Subsol

et al. 2016

Lecture Notes in Computer Science

112

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“…To compare the performance of various approaches developed for object detection in remote sensing images, many datasets are available for researchers to conduct further investigations [3,[28][29][30][31]. These datasets promote the development of object detection methods in remote sensing imagery, but have obvious drawbacks.…”

Section: Dataset and Implementation Detailsmentioning

confidence: 99%

Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery

Wang

et al. 2017

Remote Sensing

108

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Convolutional neural networks (CNNs) have demonstrated their ability object detection of very high resolution remote sensing images. However, CNNs have obvious limitations for modeling geometric variations in remote sensing targets. In this paper, we introduced a CNN structure, namely deformable ConvNet, to address geometric modeling in object recognition. By adding offsets to the convolution layers, feature mapping of CNN can be applied to unfixed locations, enhancing CNNs' visual appearance understanding. In our work, a deformable region-based fully convolutional networks (R-FCN) was constructed by substituting the regular convolution layer with a deformable convolution layer. To efficiently use this deformable convolutional neural network (ConvNet), a training mechanism is developed in our work. We first set the pre-trained R-FCN natural image model as the default network parameters in deformable R-FCN. Then, this deformable ConvNet was fine-tuned on very high resolution (VHR) remote sensing images. To remedy the increase in lines like false region proposals, we developed aspect ratio constrained non maximum suppression (arcNMS). The precision of deformable ConvNet for detecting objects was then improved. An end-to-end approach was then developed by combining deformable R-FCN, a smart fine-tuning strategy and aspect ratio constrained NMS. The developed method was better than a state-of-the-art benchmark in object detection without data augmentation.

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“…Traditionally, remote sensing imagery is most often used to rapidly and economically acquire road segment and intersection data [12]. To extract centrelines accurately, Unsalan et al [13] developed a flexible combinatorial method that relied on probabilistic and graph theory to detect and extract road networks.…”

Section: Related Workmentioning

confidence: 99%

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Xing

et al. 2017

IJGI

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Many studies have used Global Navigation Satellite System (GNSS) traces to successfully extract segments of road networks because such data can be rapidly updated at a low cost. However, most studies have not focused on extracting intersections, which are indispensable parts of road networks in terms of connectivity. However, extracted intersections often present unsatisfactory precision and misleading connectivity. This study proposes a novel method for extracting road intersections from Global Position System (GPS) trace points and for capturing intersections with better accuracy. The key to improving the geometric accuracy of intersections is to identify the dominant orientations of road segments around intersections, merge similar orientations and maintain independent conflicting orientations. Extracting intersections by aligning the dominant orientations can largely reduce location offsets and road distortions. Experiments are performed to demonstrate the increased accuracy and connectivity of extracted road intersections by the proposed method.

show abstract

Use of Salient Features for the Design of a Multistage Framework to Extract Roads From High-Resolution Multispectral Satellite Images

Cited by 207 publications

References 63 publications

Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods

Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods

Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

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