PMID- 32079156
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240328
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 20
IP  - 4
DP  - 2020 Feb 17
TI  - Object-Based Shadow Index via Illumination Intensity from High Resolution 
      Satellite Images over Urban Areas.
LID - 10.3390/s20041077 [doi]
LID - 1077
AB  - For multi-spectral remote sensing imagery, accurate shadow extraction is of great 
      significance for overcoming the information loss caused by high buildings and the 
      solar incidence angle in urban remote sensing. However, diverse solar 
      illumination conditions, similarities between shadows, and other dark land 
      features bring uncertainties and deviations to shadow extraction processes and 
      results. In this paper, we classify shadows as either strong or weak based on the 
      ratio between ambient light intensity and direct light intensity, and use the 
      fractal net evolution approach (FNEA), which is a multi-scale segmentation method 
      based on spectral and shape heterogeneity, to reduce the interference of salt and 
      pepper noise and relieve the error of misdiagnosing land covers with high 
      reflectivity in shaded regions as unshaded ones. Subsequently, an object-based 
      shadow index (OSI) is presented according to the illumination intensities of 
      different reflectance features, as well as using the normalized difference water 
      index (NDWI) and near infrared (NIR) band to highlight shadows and eliminate 
      water body interference. The data from three high-spatial-resolution 
      satellites-WorldView-2 (WV-2), WorldView-3 (WV-3), and GaoFen-2 (GF-2)-were used 
      to test the methods and verify the robustness of the OSI. The results show that 
      the OSI index performed well regarding both strong and weak shadows with the user 
      accuracy and the producer accuracy both above 90%, while the four other existing 
      indexes that were tested were not effective at diverse solar illumination 
      conditions. In addition, all the disturbances from water body were excluded well 
      when using the OSI, except for the GF-2 data in weak shadows.
FAU - Fu, Haoyang
AU  - Fu H
AUID- ORCID: 0000-0003-2280-5822
AD  - Coherent Light and Atomic and Molecular Spectroscopy Laboratory, College of 
      Physics, Jilin University, Changchun 130012, China.
FAU - Zhou, Tingting
AU  - Zhou T
AUID- ORCID: 0000-0003-0583-8956
AD  - Coherent Light and Atomic and Molecular Spectroscopy Laboratory, College of 
      Physics, Jilin University, Changchun 130012, China.
FAU - Sun, Chenglin
AU  - Sun C
AD  - Coherent Light and Atomic and Molecular Spectroscopy Laboratory, College of 
      Physics, Jilin University, Changchun 130012, China.
LA  - eng
PT  - Journal Article
DEP - 20200217
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC7070997
OTO - NOTNLM
OT  - illumination intensity
OT  - multi-scale segmentation
OT  - object-based
OT  - shadow index
OT  - shadow intensity
OT  - urban remote sensing
COIS- The authors declare no conflict of interest.
EDAT- 2020/02/23 06:00
MHDA- 2020/02/23 06:01
PMCR- 2020/02/01
CRDT- 2020/02/22 06:00
PHST- 2020/01/10 00:00 [received]
PHST- 2020/02/03 00:00 [revised]
PHST- 2020/02/13 00:00 [accepted]
PHST- 2020/02/22 06:00 [entrez]
PHST- 2020/02/23 06:00 [pubmed]
PHST- 2020/02/23 06:01 [medline]
PHST- 2020/02/01 00:00 [pmc-release]
AID - s20041077 [pii]
AID - sensors-20-01077 [pii]
AID - 10.3390/s20041077 [doi]
PST - epublish
SO  - Sensors (Basel). 2020 Feb 17;20(4):1077. doi: 10.3390/s20041077.