Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method
<p>Hierarchy of land cover ontology. According to the China Fundamental Geographic Information Category, land cover classes are selected for the land cover ontology. Land cover classes have the relationship of “is-a” in the ontology hierarchy.</p> "> Figure 2
<p>Diversity of land cover objects. (<b>a</b>) shows the spectral diversity resulted from the difference of materials, e.g., the low- rise building; and (<b>b</b>) is about the shape diversity, which may be the result of image quality or segmentation algorithm, e.g., the major road may be over segmented.</p> "> Figure 3
<p>Create prototype for land cover. With the help of referenced image and land cover map, the confidential interval of each feature can be calculated. Then, all these data are stored in Protégé 3.4.7.</p> "> Figure 4
<p>Procedure of using ontology and prototype in land cover extraction. With the help of prototype, which is created from the example data, the data ranges of features are inputted into the extraction procedure, and then automatic land cover extraction for subsequent image can be done.</p> "> Figure 5
<p>Comparison of land cover extraction results. Columns in green color represent the producer’s accuracy for land cover extraction, and columns in red color represent the user’s accuracy.</p> "> Figure 6
<p>The chord chart of confusion matrix for land cover extraction. Different colors represent different land cover classes. Length of the arc for each class represents the number of pixels, the unit is thousands of pixels. The link strips between different classes mean the pixels of the objects that are incorrectly classified into the other class.</p> "> Figure 7
<p>Classification result of the study area. Left is the extraction result for study area in 2012. Right is the result in 2013. It can be seen from the results that certain areas of bare surface become roads or buildings in 2013.</p> ">
Abstract
:1. Introduction
2. Data and Methods
2.1. Data
2.2. Methods
2.2.1. Land Cover Hierarchy
2.2.2. Land Cover Properties
2.2.3. Create Land Cover Class Prototype
2.2.4. Land Cover Extraction
2.2.5. Accuracy Assessment
3. Results and Discussion
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Producer’s Accuracy (Percent) | User’s Accuracy (Percent) | Producer’s Accuracy (Pixels) | User’s Accuracy (Pixels) | |
---|---|---|---|---|
low rise building | 86.52 | 68.63 | 1,055,969/1,220,546 | 1,055,969/1,538,703 |
high rise building | 36.69 | 65.77 | 142,552/388,494 | 142,552/216,754 |
bare surface | 79.32 | 78.93 | 600,345/756,900 | 600,345/760,630 |
paddy field | 81.18 | 53.27 | 1,494,710/1,841,225 | 1,494,710/2,805,795 |
forest | 55.13 | 82.66 | 374,772/679,774 | 374,772/453,379 |
grassland | 37.94 | 53.64 | 344,088/906,900 | 344,088/641,489 |
orchard | 69.66 | 70.84 | 847,717/1,217,009 | 847,717/1,196,624 |
major road | 65.88 | 64.14 | 275,216/417,722 | 275,216/429,090 |
secondary road | 51.35 | 58.19 | 170,437/331,885 | 170,437/292,911 |
highway | 73.09 | 76.34 | 101,251/138,538 | 101,251/132,637 |
pond | 78.31 | 82.62 | 321,176/410,127 | 321,176/388,728 |
lake | 79.81 | 100 | 66,633/83,489 | 66,633/66,633 |
path | 66.77 | 81.26 | 62,265/93,256 | 62,265/76,627 |
Low-Rise Building | High-Rise Building | Bare Surface | Dry Land | Paddy Field | Forest | Grassland | Orchard | Major Road | Secondary Road | Highway | Pond | Lake | Path | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
low-rise building | 1,055,969 | 86,974 | 25,106 | 83,868 | 63,052 | 0 | 56,957 | 49,357 | 38,384 | 26,738 | 8898 | 27,893 | 0 | 15,507 |
high-rise building | 16,715 | 142,552 | 5970 | 0 | 0 | 0 | 2664 | 550 | 10,268 | 34,816 | 1126 | 1402 | 0 | 691 |
bare surface | 25,421 | 6768 | 600,345 | 0 | 0 | 0 | 37,728 | 0 | 33,920 | 55,464 | 800 | 0 | 0 | 184 |
dry land | 0 | 0 | 0 | 337,591 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
paddy field | 7895 | 0 | 0 | 0 | 1,494,710 | 237,658 | 406,394 | 274,889 | 0 | 0 | 3752 | 42,225 | 0 | 681 |
forest | 0 | 0 | 0 | 0 | 39,514 | 37,4772 | 8343 | 30,750 | 0 | 0 | 0 | 0 | 0 | 0 |
grassland | 77,049 | 55,646 | 75,471 | 30,484 | 13,411 | 0 | 344,088 | 13,728 | 13,271 | 1846 | 1963 | 14,532 | 0 | 0 |
orchard | 2494 | 0 | 0 | 46,076 | 219,525 | 67,344 | 11,508 | 847,717 | 0 | 0 | 0 | 229 | 0 | 1731 |
major road | 16,548 | 56,146 | 17,421 | 0 | 0 | 0 | 12,872 | 0 | 275,216 | 39,783 | 8505 | 2599 | 0 | 0 |
secondary road | 4398 | 33,695 | 32,587 | 0 | 0 | 0 | 1133 | 0 | 38,221 | 170,437 | 5288 | 71 | 0 | 7081 |
highway | 766 | 0 | 0 | 4225 | 0 | 0 | 14,583 | 0 | 5527 | 1169 | 101,251 | 0 | 0 | 5116 |
pond | 12,612 | 6713 | 0 | 4453 | 11,013 | 0 | 10,630 | 0 | 1789 | 63 | 3423 | 32,1176 | 16856 | 0 |
lake | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 66633 | 0 |
path | 679 | 0 | 0 | 7438 | 0 | 0 | 0 | 18 | 1126 | 1569 | 3532 | 0 | 0 | 62265 |
Initial State (2012) | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Low-Rise Building | High-Rise Building | Bare Surface | Dry Land | Paddy Field | Forest | Grassland | Orchard | Major Road | Secondary Road | Highway | Pond | Lake | Path | ||
Final State (2013) | low-rise building | 3,810,250 | 28,401 | 489,732 | 135,472 | 845,799 | 15,592 | 19,078 | 901 | 134 | 363 | 14 | 97,099 | 451 | 4568 |
high-rise building | 43,259 | 491,031 | 290,012 | 356 | 133,422 | 43,890 | 10,656 | 821 | 491 | 773 | 29 | 24,712 | 32 | 4380 | |
bare surface | 35,460 | 27,072 | 889,621 | 213 | 47,620 | 8876 | 8899 | 1101 | 8098 | 2501 | 101 | 104,231 | 5892 | 5411 | |
dry land | 49 | 23 | 245 | 1,198,031 | 7801 | 3667 | 9297 | 459 | 219 | 0 | 33 | 3871 | 678 | 54 | |
paddy field | 32 | 88 | 21 | 810 | 4,911,230 | 10,092 | 1320 | 710,472 | 41 | 0 | 9 | 9290 | 4431 | 8803 | |
forest | 0 | 97 | 7901 | 112 | 22,143 | 1,098,044 | 78,912 | 121,010 | 191 | 0 | 231 | 8451 | 65 | 763 | |
grassland | 4862 | 2450 | 17,209 | 121,936 | 53,644 | 121,449 | 1,209,092 | 54,912 | 541 | 7384 | 302 | 1190 | 99 | 8112 | |
orchard | 1132 | 551 | 3221 | 67,540 | 67,452 | 269,376 | 46,032 | 2,199,817 | 881 | 0 | 13 | 567 | 12 | 342 | |
major road | 208,754 | 19,786 | 684,218 | 23,100 | 11,846 | 1456 | 50,211 | 341 | 800,842 | 159,132 | 871 | 10,396 | 3321 | 6601 | |
secondary road | 148,883 | 34,592 | 468,902 | 450 | 10,002 | 778 | 4532 | 667 | 45,101 | 181,413 | 877 | 34,582 | 7643 | 4531 | |
highway | 3064 | 5152 | 199,802 | 16,900 | 5687 | 908 | 10,123 | 109 | 781 | 4676 | 359,901 | 50,391 | 243 | 3901 | |
pond | 16 | 887 | 490 | 17,812 | 1024 | 33 | 3451 | 0 | 0 | 252 | 45 | 978,601 | 32,321 | 51 | |
lake | 0 | 0 | 248 | 119 | 45 | 8 | 211 | 8 | 0 | 0 | 0 | 3451 | 266,532 | 0 | |
path | 3679 | 341 | 6824 | 678 | 3290 | 108 | 1199 | 890 | 41 | 6276 | 46 | 89 | 32 | 249,060 | |
Class changes | 449,190 | 119,440 | 2,168,825 | 385,498 | 1,209,775 | 476,233 | 243,921 | 891,691 | 56,519 | 181,357 | 2571 | 348,320 | 55,220 | 47,517 | |
Image difference | 1,188,414 | 433,393 | –1,913,350 | −359,102 | −464,366 | −236,357 | 150,169 | −434,572 | 1,123,514 | 580,183 | 299,166 | −291,938 | −51,130 | −24,024 |
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Luo, H.; Li, L.; Zhu, H.; Kuai, X.; Zhang, Z.; Liu, Y. Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method. ISPRS Int. J. Geo-Inf. 2016, 5, 31. https://doi.org/10.3390/ijgi5030031
Luo H, Li L, Zhu H, Kuai X, Zhang Z, Liu Y. Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method. ISPRS International Journal of Geo-Information. 2016; 5(3):31. https://doi.org/10.3390/ijgi5030031
Chicago/Turabian StyleLuo, Heng, Lin Li, Haihong Zhu, Xi Kuai, Zhijun Zhang, and Yu Liu. 2016. "Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method" ISPRS International Journal of Geo-Information 5, no. 3: 31. https://doi.org/10.3390/ijgi5030031