References
Agüera, F., & Liu, J. (2009). Automatic greenhouse delineation from QuickBird and Ikonos satellite
images. Computers and Electronics in Agricultur, 66(2), 191–200.
Aguilar, M., Nemmaoui, A., Novelli, A., Aguilar, F., & García, L. A. (2016). Object-Based
Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series.
Remote Sensing, 8(6), 513. https://doi.org/10.3390/rs8060513.
Atkinson, P. (1997). Mapping subpixel boundaries from remotely sensed images. In Innovations in
GIS 4 (pp. 166–180). London: Taylor and Francis.
Atkinson, P. (2000, August). Super-resolution target mapping from soft-classified remotely sensed
imagery. In Proceedings of the 5th International Conference on GeoComputation (pp. 23–25).
London.
Atkinson, P. (2005). Sub-pixel target mapping from soft-classified remotely sensed imagery.
Photogrammetric Engineering and Remote Sensing, 71, 839–846.
Byeungwoo, J., & Landgrebe, D. (1999). Partially supervised classification using weighted
unsupervised clustering. IEEE Transactions on Geoscience and Remote Sensing, 37(2),
1073–1079.
Carvajal, F., Crisanto, E., Aguilar, F., Aguera, F., & Aguilar, M. (2006). Greenhouses detection
using an artificial neural network with a very high resolution satellite image (ISPRS Technical
Commission II Symposium) (pp. 37–42). Vienna.
Chen, Y., Huang, C., Ticehurst, C., et al. (2013). An evaluation of MODIS daily and 8-day
composite products for floodplain and wetland inundation mapping. Wetlands 33(5), 823–835.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological
Measurement., 20(1), 37–46.
Di, L. (2004). GeoBrain-A Web Services based Geospatial Knowledge Building System. In Proceedings of NASA Earth science technology conference 2004, June 22–24, 2004. Palo Alto, CA,
USA.
Dubois, P. (1978). Plastics in agriculture (p. 176). London: Applied Science Publishers.
Espí, A., SalmerÓn, A., Fontecha, A., García, Y., & Real, A. (2006). Plastic films for agricultural
applications. Journal of Plastic Film and Sheeting, 22(2), 85–102.
Table 17.5 The comparison of SPM accuracies among ISAM, SPSAM, MSPSAM, and MSAM
(Scale factor S ¼ 8)
Accuracies
ISAM
SPSAM
MSPSAM
MSAM
Overall accuracy (OA, %)
82.44
70.22
82.13
82.25
Kappa coefficient (κ)
0.66
0.46
0.66
0.66
Table 17.6 The comparison of computational efficiency among ISAM, SPSAM, MSPSAM,
and MSAM
SAM
Type
Scale factor S ¼ 2
Scale factor S ¼ 4
Scale factor S ¼ 8
Optimization
time per step (s)
Steps
Optimization
time per step (s)
Steps
Optimization
time per step (s)
Steps
ISAM
1.18
3
5.20
4
35.30
5
SPSAM
0.90
1
1.10
1
2.6
1
MSPSAM 1.20
3
5.60
4
40.25
6
MSAM
1.25
3
6.80
7
47.10
11
382
L. Lu
Agüera, F., & Liu, J. (2009). Automatic greenhouse delineation from QuickBird and Ikonos satellite
images. Computers and Electronics in Agricultur, 66(2), 191–200.
Aguilar, M., Nemmaoui, A., Novelli, A., Aguilar, F., & García, L. A. (2016). Object-Based
Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series.
Remote Sensing, 8(6), 513. https://doi.org/10.3390/rs8060513.
Atkinson, P. (1997). Mapping subpixel boundaries from remotely sensed images. In Innovations in
GIS 4 (pp. 166–180). London: Taylor and Francis.
Atkinson, P. (2000, August). Super-resolution target mapping from soft-classified remotely sensed
imagery. In Proceedings of the 5th International Conference on GeoComputation (pp. 23–25).
London.
Atkinson, P. (2005). Sub-pixel target mapping from soft-classified remotely sensed imagery.
Photogrammetric Engineering and Remote Sensing, 71, 839–846.
Byeungwoo, J., & Landgrebe, D. (1999). Partially supervised classification using weighted
unsupervised clustering. IEEE Transactions on Geoscience and Remote Sensing, 37(2),
1073–1079.
Carvajal, F., Crisanto, E., Aguilar, F., Aguera, F., & Aguilar, M. (2006). Greenhouses detection
using an artificial neural network with a very high resolution satellite image (ISPRS Technical
Commission II Symposium) (pp. 37–42). Vienna.
Chen, Y., Huang, C., Ticehurst, C., et al. (2013). An evaluation of MODIS daily and 8-day
composite products for floodplain and wetland inundation mapping. Wetlands 33(5), 823–835.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological
Measurement., 20(1), 37–46.
Di, L. (2004). GeoBrain-A Web Services based Geospatial Knowledge Building System. In Proceedings of NASA Earth science technology conference 2004, June 22–24, 2004. Palo Alto, CA,
USA.
Dubois, P. (1978). Plastics in agriculture (p. 176). London: Applied Science Publishers.
Espí, A., SalmerÓn, A., Fontecha, A., García, Y., & Real, A. (2006). Plastic films for agricultural
applications. Journal of Plastic Film and Sheeting, 22(2), 85–102.
Table 17.5 The comparison of SPM accuracies among ISAM, SPSAM, MSPSAM, and MSAM
(Scale factor S ¼ 8)
Accuracies
ISAM
SPSAM
MSPSAM
MSAM
Overall accuracy (OA, %)
82.44
70.22
82.13
82.25
Kappa coefficient (κ)
0.66
0.46
0.66
0.66
Table 17.6 The comparison of computational efficiency among ISAM, SPSAM, MSPSAM,
and MSAM
SAM
Type
Scale factor S ¼ 2
Scale factor S ¼ 4
Scale factor S ¼ 8
Optimization
time per step (s)
Steps
Optimization
time per step (s)
Steps
Optimization
time per step (s)
Steps
ISAM
1.18
3
5.20
4
35.30
5
SPSAM
0.90
1
1.10
1
2.6
1
MSPSAM 1.20
3
5.60
4
40.25
6
MSAM
1.25
3
6.80
7
47.10
11
382
L. Lu