Discrepancy Analysis for Detecting Candidate Parcels Requiring Update of Land Category in Cadastral Map Using Hyperspectral UAV Images: A Case Study in Jeonju, South Korea
Abstract
:1. Introduction
- For generating up-to-date land category information, we combine two-dimensional (2D) and three-dimensional (3D) convolutional neural networks (CNNs) to classify hyperspectral UAV images, and hence, generate the latest land category information at specific times and intervals. Furthermore, the environmental settings for learning are demonstrated and the classification results are analyzed to understand when the proposed network was applied to hyperspectral UAV images.
- For detecting discrepancies between the new information and the existing cadastral information, the efficiency of updating the registered land category is improved by a new technique that automatically compares two sets of non-spatial information under different criteria and structures.
2. Methods
- (1)
- The hybrid CNN with 2D and 3D kernels extracts the spatial–spectral features from hyperspectral UAV images and obtains a land cover map depicting the regions covered by forests, crops, bare soils, water, roads, and buildings. The images input to the hybrid CNN are pre-processed by principle component analysis (PCA) to reduce the number of redundant spectral bands and the computational cost. The hybrid CNN then classifies images by extracting various meaningful feature maps. The resulting land cover map provides the latest land information on sites.
- (2)
- To the procedure that automatically detects inconsistent parcels, two maps are input: the existing cadastral map, which is managed by the government, and the land cover map, which is generated from hyperspectral UAV image classification. To compare the heterogeneous datasets with vector and raster structured data, the procedure adopts an encoding–decoding approach. The final output is a discrepancy map generated through query-based comparison of the mapping information in the land category items and the land cover classes in different frameworks.
2.1. Step 1: Hyperpsectral UAV Image Classification For Generating the Land Cover Map
2.2. Step 2: Inonsistency Comparison Between the Cadastral Map and Land Cover Map
3. Dataset
4. Results
4.1. Classification Results
4.2. Detecting Inconsistent Parcels
5. Discussion
5.1. Analysis of Inconsistent Parcels
5.2. Limitation and Future Work
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Input: | Land Cover Map (LC, Rraster) |
Cadastral Map (CM, Vector) | |
1: | # Encoding |
2: | R_Cate = rasterized CM by assigning values with “land category” |
3: | R_PI = rasterized CM by assigning values with “Parcel ID” |
4: | w, h = width, height (image extent) of LC |
5: | CRM (Combined Raster Map) = empty raster layer with w h |
6: | For each pixel (i,j) on CRM: |
7: | = assigned value of pixel (i,j) on R_PI |
8: | = assigned value of pixel (i,j) on R_Cate |
9: | = assigned value of pixel (i,j) on LC |
10: | Combined(i,j) = |
11: | assign value of Combined(i,j) on pixel (i,j) to generate CRM |
12: | end |
Intermediate Output: Combined Raster Map (CRM, Raster) | |
13: | # Decoding |
14: | CVM (Combined Vector Map) = Raster to Polygon (CRM) |
15: | For each polygon i on CVM: |
16: | = pixel value of polygon i |
17: | CVM(i).p_id = |
18: | CVM(i).category = |
19: | CVM(i).cover = |
20: | CVM(i).area_ia, CVM(i).area_ca = 0 |
21: | end |
Intermediate Output: Combined Vector Map (CVM, Vector) | |
22: | # Query-based comparison |
23: | Make query Q using mapping information between land category and land cover |
24: | TF = Execute query Q on CVM |
25: | If TF == true: |
26: | CVM(i).area_ia = calculate area of polygon i |
27: | else: |
28: | CVM(i).area_ca = calculate area of polygon i |
29: | end |
30: | DM (Discrepancy Map) = Dissolve (CVM) based on p_id with summation of area_ia and area_ca |
31: | For each polygon i on MDA: |
32: | DM (i).ic_ratio= area_ia(i)/area(i) |
33: | end |
Output: Discrepancy Map (DM, vector) |
F1 Score | OA (%) | ||||||
---|---|---|---|---|---|---|---|
Crop Land | Forest | Road | Building | Water | Bare Soil | ||
Site1 | 0.9998 | 0.9990 | 0.9993 | 0.9994 | 1.0000 | 0.9984 | 99.93 ± 0.1 |
Site2 | 0.9999 | 0.9935 | 1.0000 | 0.9984 | - | 0.9705 | 99.75 ± 0.1 |
Land Category | Land Cover | Site 1 | Site 2 | |
---|---|---|---|---|
Building site | Road, Building, Bare soil | 17 | 0 | |
Paddy Field | Crop land | 24 | 44 | |
Park site | Forest, Water, Bare soil | 0 | 0 | |
School site | Road, Building, Bare soil | 0 | 0 | |
Road | Road | 15 | 2 | |
Field | Crop land | 22 | 25 | |
Forest | Forest | 9 | 7 | |
Cemetery | Road, Building, Bare soil, Crop land, Forest | 0 | 0 | |
Reservoir | Water | 18 | 0 | |
Miscellaneous land | Bare soil | 2 | 0 | |
Site for Religious use | Road, Building, Bare soil | 0 | 0 | |
Gas station site | Road, Building, Bare soil | 0 | 0 | |
Parking lot | Road, Bare soil | 1 | 1 | |
Sport area | Building, Bare soil | 1 | 0 | |
Ditch | Water | 2 | 7 | |
Factory site | Road, Building, Bare soil | 0 | 0 | |
Ranch | Crop land, Forest, Bare soil | 1 | 0 | |
Total | 112 | 86 |
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Share and Cite
Park, S.; Song, A. Discrepancy Analysis for Detecting Candidate Parcels Requiring Update of Land Category in Cadastral Map Using Hyperspectral UAV Images: A Case Study in Jeonju, South Korea. Remote Sens. 2020, 12, 354. https://doi.org/10.3390/rs12030354
Park S, Song A. Discrepancy Analysis for Detecting Candidate Parcels Requiring Update of Land Category in Cadastral Map Using Hyperspectral UAV Images: A Case Study in Jeonju, South Korea. Remote Sensing. 2020; 12(3):354. https://doi.org/10.3390/rs12030354
Chicago/Turabian StylePark, Seula, and Ahram Song. 2020. "Discrepancy Analysis for Detecting Candidate Parcels Requiring Update of Land Category in Cadastral Map Using Hyperspectral UAV Images: A Case Study in Jeonju, South Korea" Remote Sensing 12, no. 3: 354. https://doi.org/10.3390/rs12030354