Detection of leaf structures in close-range hyperspectral images using morphological fusion

Figures & data

Figure 1. Imaging system components and sample data.

Figure 2. Normalized spectral profiles for test regions.

Figure 3. RGB Image align.

Figure 4. The proposed fusion method operations.

Figure 5. Morphological profiles for a disk shaped SE of sizes [1, 2, 4].

Table

Algorithm 1 Fusion of HS and MPs data

Input: HS, RGB

1. Generate morphological profiles (MPs): For each channel of the RGB data-set, get M openings and M closings by reconstructions with different SEs (e.g. line, disk, square, octagon, dodecagon, hexadecagon). The SE size increases from 1 to M.

2. Decorrelate the original HS data by PCA, and separate image content into two different parts.

3. Spatial resolution enhancement of the first k PCs by using Equation (4).

4. Only remove noise for the remaining PCs by soft- thresholding, and enlarge their spatial sizes to the same as the RGB image using cubic interpolation.

5. Inverse PCA on results (3) and (4)

Output: Enhanced HS

Figure 6. Performances of joint bilateral filter for different parameter combinations.

Table 1. Classification results by SE shape (%).

SE Shape	MPs		Stacked		Proposed
	OA	AA	OA	AA	OA	AA
Disk	81.34	80.67	93.28	91.67	97.14	95.21
Square	88.36	87.91	93.71	92.16	97.32	95.57
Line	83.65	82.28	93.47	91.76	97.43	95.63
Octagon	89.04	90.16	93.96	92.29	97.32	95.57
Dodecagon	79.38	77.72	91.79	90.87	97.32	95.57
Hexadecagon	79.38	77.72	91.79	90.87	97.32	95.57

Figure 7. Average reflectance for linear-shaped SE, size 1–4, opening on the left side and closing on the right in each channel as is pointing out by the x-axis.

Table 2. Classification rates for each object class.

Object class	Raw	MPs	Stacked	Proposed
Dead leaf	100.00	99.71	100.00	100.00
Spot	71.67	61.67	71.67	85.00
Midrib	88.69	70.66	89.78	94.52
Dying leaf	97.01	77.78	98.50	94.23
Bent blade	76.43	74.18	90.15	97.50
Flat blade	99.76	100.00	99.90	100.00
OA	88.53	83.70	93.53	97.41
	88.92	82.30	91.85	95.65
Std	13.56	12.35	16.50	15.00

Figure 8. Test data and classification maps for different methods.