Object identification and classification in a high resolution satellite data using data mining techniques for knowledge extraction

Mantrawadi, Nikhil; Nijim, Mais; Lee, Young

doi:10.1109/syscon.2013.6549967

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…He et al (He, Zhou, and Li 2011) proposed to extract roadways based on supervised classification with adaptive thresholds. Mantrawadi et al (Mantrawadi, Nijim, and Young 2013) proposed to discover vehicle objects from satellite images by saliency based mining. Chen et al (Chen et al 2013) introduced parallel branches into the deep convolutional neural network to increase the detection speed and accuracy.…”

Section: Related Workmentioning

confidence: 99%

Towards Domain Adaptive Vehicle Detection in Satellite Image by Supervised Super-Resolution Transfer

Cao

Wang

et al. 2016

AAAI

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Vehicle detection in satellite image has attracted extensive research attentions with various emerging applications.However, the detector performance has been significantly degenerated due to the low resolutions of satellite images, as well as the limited training data.In this paper, a robust domain-adaptive vehicle detection framework is proposed to bypass both problems.Our innovation is to transfer the detector learning to the high-resolution aerial image domain,where rich supervision exists and robust detectors can be trained.To this end, we first propose a super-resolution algorithm using coupled dictionary learning to ``augment'' the satellite image region being tested into the aerial domain.Notably, linear detection loss is embedded into the dictionary learning, which enforces the augmented region to be sensitive to the subsequent detector training.Second, to cope with the domain changes, we propose an instance-wised detection using Exemplar Support Vector Machines (E-SVMs), which well handles the intra-class and imaging variations like scales, rotations, and occlusions.With comprehensive experiments on large-scale satellite image collections, we demonstrate that the proposed framework can significantly boost the detection accuracy over several state-of-the-arts.

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Section: Related Workmentioning

confidence: 99%