PMID- 38067852
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231209
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 23
DP  - 2023 Nov 28
TI  - Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited 
      Satellites.
LID - 10.3390/s23239479 [doi]
LID - 9479
AB  - The existing image matching methods for remote sensing scenes are usually based 
      on local features. The most common local features like SIFT can be used to 
      extract point features. However, this kind of methods may extract too many 
      keypoints on the background, resulting in low attention to the main object in a 
      single image, increasing resource consumption and limiting their performance. To 
      address this issue, we propose a method that could be implemented well on 
      resource-limited satellites for remote sensing images ship matching by leveraging 
      line features. A keypoint extraction strategy called line feature based keypoint 
      detection (LFKD) is designed using line features to choose and filter keypoints. 
      It can strengthen the features at corners and edges of objects and also can 
      significantly reduce the number of keypoints that cause false matches. We also 
      present an end-to-end matching process dependent on a new crop patching function, 
      which helps to reduce complexity. The matching accuracy achieved by the proposed 
      method reaches 0.972 with only 313 M memory and 138 ms testing time. Compared to 
      the state-of-the-art methods in remote sensing scenes in extensive experiments, 
      our keypoint extraction method can be combined with all existing CNN models that 
      can obtain descriptors, and also improve the matching accuracy. The results show 
      that our method can achieve  approximately 50% test speed boost and  approximately 30% memory saving in our 
      created dataset and public datasets.
FAU - Li, Leyang
AU  - Li L
AUID- ORCID: 0000-0001-9566-4501
AD  - School of Computer Science and Engineering, Northeastern University, 169 
      Chuangxin Road, Shenyang 110169, China.
AD  - School of Electronic and Computer Science, University of Southampton, Highfield 
      Street, Southampton SO17 1BJ, UK.
FAU - Cao, Guixing
AU  - Cao G
AD  - School of Computer Science and Engineering, Northeastern University, 169 
      Chuangxin Road, Shenyang 110169, China.
FAU - Liu, Jun
AU  - Liu J
AD  - School of Computer Science and Engineering, Northeastern University, 169 
      Chuangxin Road, Shenyang 110169, China.
FAU - Cai, Xiaohao
AU  - Cai X
AD  - School of Electronic and Computer Science, University of Southampton, Highfield 
      Street, Southampton SO17 1BJ, UK.
LA  - eng
GR  - 62071134/National Natural Science Foundation of China/
GR  - 6167114/National Natural Science Foundation of China/
GR  - no/China Scholarship Council/
PT  - Journal Article
DEP - 20231128
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10708588
OTO - NOTNLM
OT  - image matching
OT  - line feature
OT  - remote sensing
OT  - satellite
OT  - ship
COIS- The authors declare no conflict of interest.
EDAT- 2023/12/09 10:43
MHDA- 2023/12/09 10:44
PMCR- 2023/11/28
CRDT- 2023/12/09 01:07
PHST- 2023/10/10 00:00 [received]
PHST- 2023/11/22 00:00 [revised]
PHST- 2023/11/26 00:00 [accepted]
PHST- 2023/12/09 10:44 [medline]
PHST- 2023/12/09 10:43 [pubmed]
PHST- 2023/12/09 01:07 [entrez]
PHST- 2023/11/28 00:00 [pmc-release]
AID - s23239479 [pii]
AID - sensors-23-09479 [pii]
AID - 10.3390/s23239479 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Nov 28;23(23):9479. doi: 10.3390/s23239479.