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20152023
most citedDeep learning in remote sensing: a review

3.2k citations · 6.6k across the 41 of their papers we have counts for

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Showing 2017Show all

6 papers · 1 filter

cs.CV2017★ 138 cited

DOTA: A Large-scale Dataset for Object Detection in Aerial Images

Gui-Song Xia, Xiang Bai, Jian Ding +6

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such succe…

cs.CV2017★ 3.2k cited

Deep learning in remote sensing: a review

Xiao Xiang Zhu, Devis Tuia, Lichao Mou +4

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field,…

cs.CV2017★ 35 cited

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Xin-Yi Tong, Gui-Song Xia, Fan Hu +3

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried o…

cs.CV2017★ 43 cited

Anisotropic-Scale Junction Detection and Matching for Indoor Images

Nan Xue, Gui-Song Xia, Xiang Bai +2

Junctions play an important role in the characterization of local geometric structures in images, the detection of which is a longstanding and challenging task. Existing junction d…

cs.CV2017★ 17 cited

Texture Characterization by Using Shape Co-occurrence Patterns

Gui-Song Xia, Gang Liu, Xiang Bai +1

Texture characterization is a key problem in image understanding and pattern recognition. In this paper, we present a flexible shape-based texture representation using shape co-occ…

cs.CV2017★ 119 cited

Image Stitching by Line-guided Local Warping with Global Similarity Constraint

Tian-Zhu Xiang, Gui-Song Xia, Xiang Bai +1

Low-textured image stitching remains a challenging problem. It is difficult to achieve good alignment and it is easy to break image structures due to insufficient and unreliable po…