Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing
Abstract
:1. Introduction
2. Related Similarities of Spectral Vectors
2.1. SID
2.2. SAM
3. Proposed Approach
3.1. SRAI
3.2. Classical DP Algorithm
- (1)
- Connect both ends of the curve with a straight line and calculate the distances (d) from all the points on curve to the line.
- (2)
- Compare the maximum distance (dmax) and threshold D; if dmax < D, then eliminate all points on the curve; otherwise, retain the point with the maximum distance (dmax) and split the curve into two parts;
- (3)
- Repeat steps 1 and 2 until dmax < D is true for all points on the curve, and all the retained points comprise the final simplified spectral curve.
- (1)
- The threshold must be specified as it can significantly affect the performance of curve simplification. On the one hand, if the threshold becomes higher, fewer points are retained, which may destroy the pattern. On the other hand, if the threshold becomes lower, more points are retained; therefore, less simplification is achieved.
- (2)
- Knowing how many points are left using a threshold before computing is impossible; in other words, setting a proper threshold to maintain the feature of a spectral curve using the necessary points is a difficult and complex task.
- (3)
- For the effective simplification of multiple curves, particularly for hyperspectral images, different thresholds are needed for various curves; moreover, to retain enough points, every curve should set different thresholds.
3.3. IDP Algorithm
- (1)
- Connect the starting point S and ending point E, calculate the distance from each point to segment SE, and retain the point M, which has the maximum distance; this is the same as step one in the traditional DP algorithm.
- (2)
- Connect SM and ME, calculate the distance from each point to segment SM and ME, and retain the point N, which has the maximum distance.
- (3)
- Divide the curve into three parts using points M and N. Repeat step (2) until it meets the point number retaining requirement.
- (4)
- If the distances are equal in step (2), calculate the ratio of the distance to the line sector and the length of sector, the point with the lower ratio is then retained.
3.4. The IDP Algorithm under SRAI-Restriction
- (1)
- Specify the number of SRAI points and then calculate the SRAI feature points.
- (2)
- Specify the number of all the retained points and then set the SRAI points as the initial points of the IDP algorithm. Finally, run the IDP algorithm to determine the remaining points.
3.5. SRAI for the Hyperspectral Curves
- (1)
- For the simplified spectral curves A and B, for a point Ai on curve A, if a point Bj on curve B has the same band number as Ai, then Bj is matched with Ai. Repeat this procedure until all points on curve A have been processed. Let variable N denote the total number of matched points.
- (2)
- For every matched point, calculate the ED between the reflectance of every matched point, and obtain the sum of all the distances as the final distance.
4. Experiment and Analysis
4.1. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Dataset
CC | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 1 | 0.9980 | 0.9982 | 0.9979 | 0.9977 | 0.9967 | 0.9964 | 0.9969 | 0.9968 | A1, A3, A2 |
A2 | 0.9980 | 1 | 0.9960 | 0.9983 | 0.9972 | 0.9962 | 0.9969 | 0.9976 | 0.9968 | A2, B1, A1 |
A3 | 0.9982 | 0.9960 | 1 | 0.9967 | 0.9977 | 0.9973 | 0.9963 | 0.9968 | 0.9968 | A3, A1, B2 |
B1 | 0.9979 | 0.9983 | 0.9967 | 1 | 0.9983 | 0.9968 | 0.9982 | 0.9983 | 0.9982 | B1, B2, C1/C2 |
B2 | 0.9977 | 0.9972 | 0.9977 | 0.9983 | 1 | 0.9982 | 0.9971 | 0.9978 | 0.9978 | B2, B1, B3 |
B3 | 0.9967 | 0.9962 | 0.9973 | 0.9968 | 0.9982 | 1 | 0.9962 | 0.9969 | 0.9965 | B3, B2, A3 |
C1 | 0.9964 | 0.9969 | 0.9963 | 0.9982 | 0.9971 | 0.9962 | 1 | 0.9994 | 0.9993 | C1, C2, C3 |
C2 | 0.9969 | 0.9976 | 0.9968 | 0.9983 | 0.9978 | 0.9969 | 0.9994 | 1 | 0.9993 | C2, C1, C3 |
C3 | 0.9968 | 0.9968 | 0.9968 | 0.9982 | 0.9978 | 0.9965 | 0.9993 | 0.9993 | 1 | C3, C1, C2 |
ED | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 0 | 0.3739 | 0.3280 | 0.9241 | 0.8188 | 1.0386 | 1.3415 | 1.3367 | 1.2500 | A1,A3,A2 |
A2 | 0.3739 | 0 | 0.3088 | 0.6276 | 0.3471 | 0.8056 | 1.0458 | 1.0417 | 0.9627 | A2,A3,B2 |
A3 | 0.3280 | 0.3088 | 0 | 0.6942 | 0.5721 | 0.7933 | 1.0870 | 1.0815 | 0.9946 | A3,A2,A1 |
B1 | 0.9241 | 0.6276 | 0.6942 | 0 | 0.2499 | 0.4251 | 0.4784 | 0.4823 | 0.4068 | B1,B2,C3 |
B2 | 0.8188 | 0.3471 | 0.5721 | 0.2499 | 0 | 0.4570 | 0.6084 | 0.5983 | 0.4155 | B2,B1,A2 |
B3 | 1.0386 | 0.8056 | 0.7933 | 0.4251 | 0.4570 | 0 | 0.4460 | 0.5134 | 0.4949 | B3,B1,C1 |
C1 | 1.3415 | 1.0458 | 1.0870 | 0.4784 | 0.6084 | 0.4460 | 0 | 0.1618 | 0.1800 | C1,C2,C3 |
C2 | 1.3367 | 1.0417 | 1.0815 | 0.4823 | 0.5983 | 0.5134 | 0.1618 | 0 | 0.1874 | C2,C1,C3 |
C3 | 1.2500 | 0.9627 | 0.9946 | 0.4068 | 0.4155 | 0.4949 | 0.1800 | 0.1874 | 0 | C3,C1,C2 |
SID | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 0 | 0.0087 | 0.0066 | 0.0154 | 0.0183 | 0.0101 | 0.0159 | 0.0109 | 0.0147 | A1,A3,A2 |
A2 | 0.0087 | 0 | 0.0082 | 0.0085 | 0.0114 | 0.0141 | 0.0105 | 0.0064 | 0.0117 | A2,A3,C2 |
A3 | 0.0066 | 0.0082 | 0 | 0.0110 | 0.0126 | 0.0117 | 0.0112 | 0.0083 | 0.0109 | A3,A1,A2 |
B1 | 0.0154 | 0.0085 | 0.0110 | 0 | 0.0078 | 0.0210 | 0.0059 | 0.0069 | 0.0076 | B1,C1,C2 |
B2 | 0.0183 | 0.0114 | 0.0126 | 0.0078 | 0 | 0.0214 | 0.0100 | 0.0085 | 0.0075 | B2,C3,B1 |
B3 | 0.0101 | 0.0141 | 0.0117 | 0.0210 | 0.0214 | 0 | 0.0219 | 0.0150 | 0.0214 | B3,A1,A3 |
C1 | 0.0159 | 0.0105 | 0.0112 | 0.0059 | 0.0100 | 0.0219 | 0 | 0.0071 | 0.0078 | C1,B1,C2 |
C2 | 0.0109 | 0.0064 | 0.0083 | 0.0069 | 0.0085 | 0.0150 | 0.0071 | 0 | 0.0076 | C2,A2,B1 |
C3 | 0.0147 | 0.0117 | 0.0109 | 0.0076 | 0.0075 | 0.0214 | 0.0078 | 0.0076 | 0 | C3,B2,B1/C2 |
KL | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 0 | 0.2441 | 0.2080 | 0.8186 | 0.7634 | 1.3321 | 1.4262 | 1.4227 | 1.2981 | A1,A3,A2 |
A2 | 0.2441 | 0 | 0.2109 | 0.4512 | 0.2323 | 1.1203 | 0.9707 | 0.9656 | 0.9177 | A2,A3,B2 |
A3 | 0.2080 | 0.2109 | 0 | 0.4835 | 0.4373 | 0.9866 | 0.9297 | 0.9518 | 0.8453 | A3,A1,A2 |
B1 | 0.8186 | 0.4512 | 0.4835 | 0 | 0.2207 | 0.7410 | 0.2934 | 0.3687 | 0.3044 | B1,B2,C1 |
B2 | 0.7634 | 0.2323 | 0.4373 | 0.2207 | 0 | 0.8417 | 0.4877 | 0.5064 | 0.3786 | B2,B1,A2 |
B3 | 1.3321 | 1.1203 | 0.9866 | 0.7410 | 0.8417 | 0 | 0.6529 | 0.4534 | 0.6356 | B3,C1,C2 |
C1 | 1.4262 | 0.9707 | 0.9297 | 0.2934 | 0.4877 | 0.6529 | 0 | 0.1272 | 0.1388 | C1,C2,C3 |
C2 | 1.4227 | 0.9656 | 0.9518 | 0.3687 | 0.5064 | 0.4534 | 0.1272 | 0 | 0.1473 | C2,C1,C3 |
C3 | 1.2981 | 0.9177 | 0.8453 | 0.3044 | 0.3786 | 0.6356 | 0.1388 | 0.1473 | 0 | C3,C1,C2 |
SAM | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 1 | 0.9988 | 0.9992 | 0.9981 | 0.9980 | 0.9986 | 0.9972 | 0.9979 | 0.9976 | A1,A3,A2 |
A2 | 0.9988 | 1 | 0.9982 | 0.9991 | 0.9986 | 0.9977 | 0.9983 | 0.9989 | 0.9984 | A2,B1,C2 |
A3 | 0.9992 | 0.9982 | 1 | 0.9980 | 0.9985 | 0.9986 | 0.9977 | 0.9982 | 0.9981 | A3,A1,B3 |
B1 | 0.9981 | 0.9991 | 0.9980 | 1 | 0.9991 | 0.9975 | 0.9992 | 0.9993 | 0.9992 | B1,C2,C1/C3 |
B2 | 0.9980 | 0.9986 | 0.9985 | 0.9991 | 1 | 0.9977 | 0.9987 | 0.9990 | 0.9990 | B2,B1,C2/C3 |
B3 | 0.9986 | 0.9977 | 0.9986 | 0.9975 | 0.9977 | 1 | 0.9966 | 0.9975 | 0.9970 | B3,A1,A3 |
C1 | 0.9972 | 0.9983 | 0.9977 | 0.9992 | 0.9987 | 0.9966 | 1 | 0.9990 | 0.9987 | C1,B1,C2 |
C2 | 0.9979 | 0.9989 | 0.9982 | 0.9993 | 0.9990 | 0.9975 | 0.9990 | 1 | 0.9991 | C2,B1,C3 |
C3 | 0.9976 | 0.9984 | 0.9981 | 0.9992 | 0.9990 | 0.9970 | 0.9987 | 0.9991 | 1 | C3,B1,C2 |
Proposed | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 0 | 0.0637 | 0.0748 | 0.1495 | 0.1960 | 0.2151 | 0.3167 | 0.2271 | 0.2295 | A1,A2,A3 |
A2 | 0.0637 | 0 | 0.0568 | 0.1202 | 0.1343 | 0.1516 | 0.1829 | 0.1678 | 0.1838 | A2,A3,A1 |
A3 | 0.0748 | 0.0568 | 0 | 0.1797 | 0.1412 | 0.2417 | 0.1819 | 0.2116 | 0.2304 | A3,A2,A1 |
B1 | 0.1495 | 0.1202 | 0.1797 | 0 | 0.0518 | 0.0726 | 0.1101 | 0.1001 | 0.0934 | B1,B2,B3 |
B2 | 0.1960 | 0.1343 | 0.1412 | 0.0518 | 0 | 0.0883 | 0.1509 | 0.1445 | 0.1443 | B2,B1,B3 |
B3 | 0.2151 | 0.1516 | 0.2417 | 0.0726 | 0.0883 | 0 | 0.1122 | 0.1242 | 0.0966 | B3,B1,B2 |
C1 | 0.3167 | 0.1829 | 0.1819 | 0.1101 | 0.1509 | 0.1122 | 0 | 0.0293 | 0.0412 | C1,C2,C3 |
C2 | 0.2271 | 0.1678 | 0.2116 | 0.1001 | 0.1445 | 0.1242 | 0.0293 | 0 | 0.0461 | C2,C1,C3 |
C3 | 0.2295 | 0.1838 | 0.2304 | 0.0934 | 0.1443 | 0.0966 | 0.0412 | 0.0461 | 0 | C3,C1,C2 |
4.2. Reflective Optics System Imaging Spectrometer (ROSIS) Dataset
CC | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 1.0000 | 0.9775 | 0.9798 | 0.9024 | 0.9156 | 0.9054 | 0.9441 | 0.9376 | 0.9437 | A1,A3,A2 |
A2 | 0.9775 | 1.0000 | 0.9645 | 0.9570 | 0.9673 | 0.9579 | 0.9362 | 0.9327 | 0.9382 | A2,A1,B2 |
A3 | 0.9798 | 0.9645 | 1.0000 | 0.9560 | 0.9643 | 0.9593 | 0.9392 | 0.9350 | 0.9406 | A3,A1,A2 |
B1 | 0.9024 | 0.9570 | 0.9560 | 1.0000 | 0.9524 | 0.9961 | 0.9128 | 0.9152 | 0.9176 | B1,B3,A2 |
B2 | 0.9156 | 0.9673 | 0.9643 | 0.9524 | 1.0000 | 0.9937 | 0.9243 | 0.9264 | 0.9278 | B2,B3,A2 |
B3 | 0.9054 | 0.9579 | 0.9593 | 0.9961 | 0.9937 | 1.0000 | 0.9202 | 0.9223 | 0.9245 | B3,B1,B2 |
C1 | 0.9441 | 0.9362 | 0.9392 | 0.9128 | 0.9243 | 0.9202 | 1.0000 | 0.9991 | 0.9994 | C1,C3,C2 |
C2 | 0.9376 | 0.9327 | 0.9350 | 0.9152 | 0.9264 | 0.9223 | 0.9991 | 1.0000 | 0.9994 | C2,C3,C1 |
C3 | 0.9437 | 0.9382 | 0.9406 | 0.9176 | 0.9278 | 0.9245 | 0.9994 | 0.9994 | 1.0000 | C3,C1,C2 |
ED | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 0.0000 | 0.1923 | 0.4887 | 0.3429 | 0.4139 | 0.4501 | 1.5770 | 2.0089 | 1.6241 | A1,A2,B1 |
A2 | 0.1923 | 0.0000 | 0.0787 | 0.2705 | 0.3597 | 0.3867 | 1.4922 | 1.9360 | 1.5395 | A2,A3,A1 |
A3 | 0.4887 | 0.0787 | 0.0000 | 0.3112 | 0.4091 | 0.4314 | 1.5348 | 1.9820 | 1.5831 | A3,A2,B1 |
B1 | 0.3429 | 0.2705 | 0.3112 | 0.0000 | 0.3444 | 0.1491 | 1.3785 | 1.7978 | 1.4199 | B1,B3,A2 |
B2 | 0.4139 | 0.3597 | 0.4091 | 0.3444 | 0.0000 | 0.0904 | 1.3043 | 1.7101 | 1.3443 | B2,B3,B1 |
B3 | 0.4501 | 0.3867 | 0.4314 | 0.1491 | 0.0904 | 0.0000 | 1.2787 | 1.6847 | 1.3175 | B3,B2,B1 |
C1 | 1.5770 | 1.4922 | 1.5348 | 1.3785 | 1.3043 | 1.2787 | 0.0000 | 0.4871 | 0.0842 | C1,C3,C2 |
C2 | 2.0089 | 1.9360 | 1.9820 | 1.7978 | 1.7101 | 1.6847 | 0.4871 | 0.0000 | 0.4313 | C2,C3,C1 |
C3 | 1.6241 | 1.5395 | 1.5831 | 1.4199 | 1.3443 | 1.3175 | 0.0842 | 0.4313 | 0.0000 | C3,C1,C2 |
SID | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 0.0000 | 0.0165 | 0.0190 | 0.0468 | 0.0456 | 0.0594 | 0.5079 | 0.5212 | 0.5029 | A1,A2,A3 |
A2 | 0.0165 | 0.0000 | 0.0020 | 0.0166 | 0.0138 | 0.0224 | 0.3945 | 0.4035 | 0.3859 | A2,A3,B2 |
A3 | 0.0190 | 0.0020 | 0.0000 | 0.0018 | 0.0164 | 0.0234 | 0.4069 | 0.4172 | 0.3992 | A3,A2,B1 |
B1 | 0.0468 | 0.0166 | 0.0018 | 0.0000 | 0.0040 | 0.0028 | 0.3804 | 0.3839 | 0.3682 | B1,B3,B2 |
B2 | 0.0456 | 0.0138 | 0.0164 | 0.0040 | 0.0000 | 0.0046 | 0.3675 | 0.3703 | 0.3568 | B2,A2,B1 |
B3 | 0.0594 | 0.0224 | 0.0234 | 0.0028 | 0.0046 | 0.0000 | 0.3516 | 0.3542 | 0.3395 | B3,B2,B1 |
C1 | 0.5079 | 0.3945 | 0.4069 | 0.3804 | 0.3675 | 0.3516 | 0.0000 | 0.0035 | 0.0028 | C1,C3,C2 |
C2 | 0.5212 | 0.4035 | 0.4172 | 0.3839 | 0.3703 | 0.3542 | 0.0035 | 0.0000 | 0.0029 | C2,C3,C1 |
C3 | 0.5029 | 0.3859 | 0.3992 | 0.3682 | 0.3568 | 0.3395 | 0.0028 | 0.0029 | 0.0000 | C3,C1,C2 |
KL | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 0.0000 | 0.1542 | 0.1540 | 0.4020 | 0.4914 | 0.5996 | 4.7123 | 6.0548 | 4.8043 | A1,A3,A2 |
A2 | 0.1542 | 0.0000 | 0.0203 | 0.3606 | 0.0828 | 0.4234 | 3.9258 | 5.3117 | 3.9991 | A2,A3,B1 |
A3 | 0.1540 | 0.0203 | 0.0000 | 0.0955 | 0.4606 | 0.5081 | 4.1037 | 5.5636 | 4.1915 | A3,A2,A1 |
B1 | 0.4020 | 0.3606 | 0.0955 | 0.0000 | 0.0916 | 0.0542 | 3.5761 | 4.6501 | 3.5883 | B1,B3,A2 |
B2 | 0.4914 | 0.0828 | 0.4606 | 0.0916 | 0.0000 | 0.0411 | 3.4130 | 4.3212 | 3.4172 | B2,B3,B1 |
B3 | 0.5996 | 0.4234 | 0.5081 | 0.0542 | 0.0411 | 0.0000 | 3.2701 | 4.1749 | 3.2621 | B3,B2,B1 |
C1 | 4.7123 | 3.9258 | 4.1037 | 3.5761 | 3.4130 | 3.2701 | 0.0000 | 0.3048 | 0.0321 | C1,C3,C2 |
C2 | 6.0548 | 5.3117 | 5.5636 | 4.6501 | 4.3212 | 4.1749 | 0.3048 | 0.0000 | 0.2392 | C2,C3,C1 |
C3 | 4.8043 | 3.9991 | 4.1915 | 3.5883 | 3.4172 | 3.2621 | 0.0321 | 0.2392 | 0.0000 | C3,C1,C2 |
SAM | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 1.0000 | 0.9932 | 0.9947 | 0.9841 | 0.9845 | 0.9805 | 0.8631 | 0.8601 | 0.8628 | A1,A3,A2 |
A2 | 0.9932 | 1.0000 | 0.9993 | 0.9950 | 0.9954 | 0.9991 | 0.9009 | 0.8985 | 0.9011 | A2,A3,B3 |
A3 | 0.9947 | 0.9993 | 1.0000 | 0.9940 | 0.9989 | 0.9930 | 0.8967 | 0.8941 | 0.8968 | A3,A2,B2 |
B1 | 0.9841 | 0.9950 | 0.9940 | 1.0000 | 0.9948 | 0.9933 | 0.9014 | 0.9006 | 0.9025 | B1,A2,B2 |
B2 | 0.9845 | 0.9954 | 0.9989 | 0.9948 | 1.0000 | 0.9989 | 0.9063 | 0.9054 | 0.9070 | B2,B3,A3 |
B3 | 0.9805 | 0.9991 | 0.9930 | 0.9933 | 0.9989 | 1.0000 | 0.9114 | 0.9106 | 0.9124 | B3,A2,B2 |
C1 | 0.8631 | 0.9009 | 0.8967 | 0.9014 | 0.9063 | 0.9114 | 1.0000 | 0.9996 | 0.9997 | C1,C3,C2 |
C2 | 0.8601 | 0.8985 | 0.8941 | 0.9006 | 0.9054 | 0.9106 | 0.9996 | 1.0000 | 0.9997 | C2,C3,C1 |
C3 | 0.8628 | 0.9011 | 0.8968 | 0.9025 | 0.9070 | 0.9124 | 0.9997 | 0.9997 | 1.0000 | C3,C1,C2 |
Proposed | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
A1 | A2 | A3 | B1 | B2 | B3 | C1 | C2 | C3 | Rank | |
A1 | 0.0000 | 0.0829 | 0.0622 | 0.1473 | 0.1192 | 0.2132 | 0.5461 | 0.9955 | 0.6399 | A1,A3,A2 |
A2 | 0.0829 | 0.0000 | 0.0339 | 0.1180 | 0.1333 | 0.1645 | 0.4875 | 1.0929 | 0.5742 | A2,A3,A1 |
A3 | 0.0622 | 0.0339 | 0.0000 | 0.1149 | 0.1514 | 0.1349 | 0.6716 | 0.6859 | 0.4885 | A3,A2,A1 |
B1 | 0.1473 | 0.1180 | 0.1149 | 0.0000 | 0.0510 | 0.0736 | 0.6502 | 0.7601 | 0.4397 | B1,B2,B3 |
B2 | 0.1192 | 0.1333 | 0.1514 | 0.0510 | 0.0000 | 0.0501 | 0.5167 | 0.5091 | 0.5821 | B2,B3,B1 |
B3 | 0.2132 | 0.1645 | 0.1349 | 0.0736 | 0.0501 | 0.0000 | 0.4990 | 0.5677 | 0.3716 | B3,B2,B1 |
C1 | 0.5461 | 0.4875 | 0.6716 | 0.6502 | 0.5167 | 0.4990 | 0.0000 | 0.1852 | 0.0376 | C1,C3,C2 |
C2 | 0.9955 | 1.0929 | 0.6859 | 0.7601 | 0.5091 | 0.5677 | 0.1852 | 0.0000 | 0.1232 | C2,C3,C1 |
C3 | 0.6399 | 0.5742 | 0.4885 | 0.4397 | 0.5821 | 0.3716 | 0.0376 | 0.1232 | 0.0000 | C3,C1,C2 |
4.3.The Impacts of the Parameters
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Ma, D.; Liu, J.; Huang, J.; Li, H.; Liu, P.; Chen, H.; Qian, J. Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing. Sensors 2016, 16, 152. https://doi.org/10.3390/s16020152
Ma D, Liu J, Huang J, Li H, Liu P, Chen H, Qian J. Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing. Sensors. 2016; 16(2):152. https://doi.org/10.3390/s16020152
Chicago/Turabian StyleMa, Dan, Jun Liu, Junyi Huang, Huali Li, Ping Liu, Huijuan Chen, and Jing Qian. 2016. "Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing" Sensors 16, no. 2: 152. https://doi.org/10.3390/s16020152
APA StyleMa, D., Liu, J., Huang, J., Li, H., Liu, P., Chen, H., & Qian, J. (2016). Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing. Sensors, 16(2), 152. https://doi.org/10.3390/s16020152