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Land Cover Identification of Hazelnut Fields Using WV2 Imagery
1. Land cover identification for finding hazelnut fields using WV2 imagery Kadim Ta şdemir 1 and Selcuk Reis 2 1 Monitoring Agricultural Resources Unit Institute for Environment and Sustainability European Commission Joint Research Centre 2 Aksaray University, Turkey
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10. Self-organizing maps A 2-d SOM rigid lattice Data manifold d -dimensional prototypes Voronoi polyhedra of some neighbor prototypes 1. Competition i = arg min j ||x - w j ||, j=1, … , N 2. Cooperation The neighborhood of i is activated wrt a neighborhood function h j,i(x) (t). 3. Synaptic adaptation w j (t+1) = w j (t)+ ε 0 (t) h j,i(x) (t) (x - w j (t))
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15. Study area An example representation of the study area using natural color composite For agricultural management and monitoring required in this study, 5 classes (hazelnut, woodland, agriculture, soil and urban regions) are selected.
16. Classification maps – 8 band Classification map produced when only 8 multispectral bands were used: PA_hazelnut= 85.4%; UA_hazelnut =68.6%; F_b=0.76; OA = 76.4%; Kappa = 0.64 PA: producer accuracy, UA: user accuracy, F_b: geometric mean of PA and UA of hazelnut OA:overall accuracy
17. Classification maps – 4 band Classification map produced when only 4 (R,G,B, NIR1) multispectral bands were used: PA_hazelnut= 87.8%; UA_hazelnut =70.2%; F_b=0.78; OA = 79.5%; Kappa = 0.69 PA: producer accuracy, UA: user accuracy, F_b: geometric mean of PA and UA of hazelnut OA:overall accuracy
18. Classification maps - Gabor Classification map produced when Gabor features (calculated from pan) were used: PA_hazelnut= 95.0%; UA_hazelnut =80.7%; F_b=0.87; OA = 77.9%; Kappa = 0.66 PA: producer accuracy, UA: user accuracy, F_b: geometric mean of PA and UA of hazelnut OA:overall accuracy
19. Classification maps – 8bands&Gabor Classification map produced when 8 multispectral bands and Gabor features were used: PA_hazelnut= 93.6%; UA_hazelnut =74.7%; F_b=0.83; OA = 83.2%; Kappa = 0.74 PA: producer accuracy, UA: user accuracy, F_b: geometric mean of PA and UA of hazelnut OA:overall accuracy
20. Classification maps – Merged Classification map produced when a decision rule was added to 8 bands and Gabor features PA_hazelnut= 90.6%; UA_hazelnut =85.1%; F_b=0.88; OA = 87.8%; Kappa = 0.81 PA: producer accuracy, UA: user accuracy, F_b: geometric mean of PA and UA of hazelnut OA:overall accuracy
21. Performance results of WV2 imagery WorldView 2 imagery Measure Class MS 8band MS 4 band Gabor MS+Gabor Merged PA woodland 64,68 75,51 79,82 71,73 85,8 nut 85,41 87,77 95 93,64 90,6 urban 93,24 93,19 39,46 82,64 82,64 agri 80,26 45,99 50,54 84,37 84,37 soil 75,81 84,76 46,64 93,07 93,07 UA woodland 84,05 91,05 81,34 89,56 87,97 nut 68,62 70,19 80,68 74,66 85,09 urban 77,55 84,28 88,83 93,09 93,09 agri 77,76 64,26 54,27 92,35 92,35 soil 92,75 94,1 60 92,62 92,62 Overall accuracy 76,38 79,47 77,94 83,24 87,8 F_b 0,76 0,78 0,87 0,83 0,88 Kappa 0,64 0,69 0,66 0,74 0,81 Gabor features has good PA and UA values for woodlands and hazelnut, in expense of very poor results for other classes Combined usage of 8 bands and Gabor features increases performance significantly A decision rule in addition to combined usage of 8 bands and Gabor features produces the best classification performance Confusion matrix for Merged Total Woodland Hazelnut Urban Agri. Soil PA 90072 77280 12591 127 70 4 85,80 82824 6572 75035 0 1217 0 90,60 14488 1290 0 11973 0 1225 82,64 18442 2317 561 1 15560 3 84,37 16611 388 0 761 2 15460 93,07 UA 87,97 85,09 93,09 92,35 92,62 87,80 4 bands produces better classification than 8 bands (for this study) in expense of poor performance for agriculture class (confusion with dense hazelnut fields) Low performance measures
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23. Comparison to QB performance The performance improvement achieved by using WV2 imagery, mainly due to the increased spatial resolution (0.5m versus 0.6m of QB imagery), significant to achieve an accurate land cover identification for finding hazelnut fields Imagery --> WorldView 2 imagery Quickbird imagery Measure Class MS 8band MS 4 band Gabor MS 4 band Gabor PA woodland 64,68 75,51 79,82 65,66 74,96 nut 85,41 87,77 95 81,37 92,16 urban 93,24 93,19 39,46 85,96 32,98 agri 80,26 45,99 50,54 37,65 11,85 soil 75,81 84,76 46,64 16,86 17,55 UA woodland 84,05 91,05 81,34 76,06 75,52 nut 68,62 70,19 80,68 63,8 71,3 urban 77,55 84,28 88,83 88,8 80,06 agri 77,76 64,26 54,27 35,55 28,37 soil 92,75 94,1 60 55,28 25,8 Overall accuracy 76,38 79,47 77,94 66,72 69,19 F_b (hazelnut) 0,76 0,78 0,87 0,72 0,8 Kappa 0,64 0,69 0,66 0,49 0,51 A better performance by using spectral bands (either 8-band or 4-band) of WV2 imagery A better performance by using Gabor features extracted from WV2 panchromatic imagery
An approach to determine permanent crops is interactive detection of individual orchards by analysis of panchromatic or multispectral bands using semi-automatic schemes with user-tuned parameters [1], [2], [3]. These schemes are successful to some extent but a considerable user interaction is necessary and, in case of detecting nut orchards, determination of the covered land is important for subsidies rather than extraction of individual trees. Some studies focus on textural features calculated from panchromatic band to delineate orchards, vineyards, and forest, thanks to their (ideally) discriminative spatial patterns [4], [5]. June 14, 2010 DRAFT DETECTION OF ORCHARDS BY SELF ORGANIZING MAPS, SUBMITTED TO IEEE TGRS, 14/06/2010 3 Ranchin et al. [4] uses multi-level wavelet-based textures for capturing different scales of spatial patterns for vineyard detection. Warner and Steinmaus [5] review textural features for discriminating orchards, vineyards, and other fields; and use an autocorrelation of neighbor pixels along four different directions (vertical, horizontal and diagonals), to produce a high accuracy. Trias-Sanz [6] extends the use of autocorrelation in [5] by using a variogram analysis for multispectral bands to differentiate different classes using orientations and periodicities. This approach produces a better accuracy than in [5] and estimates periodicity and orientation of regular patterns. Another method, which estimates orientations and scales of regular patterns, uses multi-scale isotropic filters and projection profiles to calculate a regularity index to accurately locate structural textures [7]. They produce accurate estimations for orientations and scales, when regular pattern exists. However, when different orchard types or forest with regular plantation exist in the scene, even though regularity features may provide additional information, spectral values are necessary to detect the orchard type. A new study briefly uses spectral information by merging a spectral feature (normalized difference vegetation index) with texture features (multiscale Gabor and granulometry) through a rule-based classifier for a successful mapping of linear woody vegetations [8]. Apart from permanent crops, a common approach for land cover identification using remote sensing images is the use of pixel based methods based on machine learning or statistical approaches. The self-organizing map (SOM), a learning paradigm that produces an adaptive vector quantization and topology preserving mapping [9], and its variants have been shown useful especially for remote sensing applications with complex cluster structures [10], [11], [12], [13]. However in clustering or classification of orchards, the aim is to find the land cover rather than focusing individual pixel memberships that can be sensitive to various factors such as shadows, relief and spectral mixing between classes. In addition, discriminative spatial pattern of orchards cannot be represented by pixel-based approach. A recent study, Image Features Map (IFM) [14], combines the advantageous properties of the SOM by spatial information, through considering spectral values within a neighborhood, without any computation of textural features. IFM is shown to work well for unsupervised clustering of rural regions with few types of homogeneous land covers.
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