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《Acta Geodaetica et Cartographica Sinica》 2018-06
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Salient Object Detection from Multi-spectral Remote Sensing Images with Deep Residual Network

DAI Yuchao;ZHANG Jing;Fatih PORIKLI;HE Mingyi;School of Electronics and Information,Northwestern Polytechnical University;Research School of Engineering,Australian National University;  
This paper focuses on intelligent photogrammetry deep learning:deep residual method.Salient object detection aims at identifying the visually interesting object regions that are consistent with human perception.Multispectral remote sensing images provide rich radiometric information in revealing the physical properties of the observed objects,therefore promise a great potential in salient object detection tasks.Conventional salient object detection methods often employ handcrafted features to predict saliency by evaluating the pixel-wise or superpixel-wise similarity.With the recent emergence of deep learning based approaches,in particular,fully convolutional neural networks,there has been profound progress in visual saliency detection.However,this success has not been extended to multispectral remote sensing images,and existing multispectral salient object detection methods are still mainly based on handcrafted features,essentially due to the difficulties in image acquisition and labeling.Inthispaper,we propose a novel deep residual network based on a top-down model,which is trained in an end-to-end manner to tackle the above issues in multispectral salient object detection.Our model effectively exploits the saliency cues at different levels of the deep residual network.To overcome the limited availability of remote sensing images in training of our deep residual network,we also introduce a new spectral image reconstruction model that can generate multispectral images from RGB images.Our extensive experimental evaluations using both multispectral and RGB salient object detection datasets demonstrate a significant performance improvement of more than 10%compared with the state-of-the-art methods.
【Fund】: “千人计划”青年项目;; 国家自然科学基金(61420106007;61671387);; 澳大利亚研究理事会DECRA项目(DE140100180)~~
【CateGory Index】: TP751
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