Region (Dataset Name) | Usage | Acquired Date | Pixel Size (Height×Width) | #Patches |
Hayward Fault Zone | Training | Oct 9, 2018 | 6440 | |
Test | May 30, 2019 | 6412 | ||
Yukon–Kuskokwim Delta | Training | Aug 28, 2018 | 5180 | |
Test | Sep 17, 2019 | 5208 |
Navigating unmanned aerial vehicles in precarious environments is of great importance. It is necessary to rely on alternative information processing techniques to attain spatial information that is required for navigation in such settings. This paper introduces a novel deep learning-based approach for navigating that exclusively relies on synthetic aperture radar (SAR) images. The proposed method utilizes deep neural networks (DNNs) for image matching, retrieval, and registration. To this end, we introduce Deep Cosine Similarity Neural Networks (DCSNNs) for mapping SAR images to a global descriptive feature vector. We also introduce a fine-tuning algorithm for DCSNNs, and DCSNNs are used to generate a database of feature vectors for SAR images that span a geographic area of interest, which, in turn, are compared against a feature vector of an inquiry image. Images similar to the inquiry are retrieved from the database by using a scalable distance measure between the feature vector outputs of DCSNN. Methods for reranking the retrieved SAR images that are used to update position coordinates of an inquiry SAR image by estimating from the best retrieved SAR image are also introduced. Numerical experiments comparing with baselines on the Polarimetric SAR (PolSAR) images are presented.
Citation: |
Figure 8. Examples of the retrieved SAR patches before and after reranking processes. First column represents examples of inquiry SAR patches. The first two rows ((a) and (b)) are from Hayward Fault Zone PolSAR and the later two rows ((c) and (d)) are from Yukon-Kuskokwim Delta PolSAR data. Having green box indicates it is correctly retrieved, whereas having red box indicates that it is incorrectly retrieved
Table 1. Descriptions of the PolSAR map from UAVSAR [1]
Region (Dataset Name) | Usage | Acquired Date | Pixel Size (Height×Width) | #Patches |
Hayward Fault Zone | Training | Oct 9, 2018 | 6440 | |
Test | May 30, 2019 | 6412 | ||
Yukon–Kuskokwim Delta | Training | Aug 28, 2018 | 5180 | |
Test | Sep 17, 2019 | 5208 |
Table 2. Mean average precision (mAP) results of DCSNN and binary hashing methods before reranking on Hayward Fault Zone PolSAR map
Methods | Feature length |
AlexNet [21] | VGG-11 [40] |
Pretrained (BH) | 0.0478 | 0.0313 | |
0.0761 | 0.0522 | ||
0.1547 | 0.1200 | ||
0.1566 | 0.1219 | ||
Pretrained (PQ+AQD) | 0.2332 | 0.1706 | |
0.3208 | 0.2055 | ||
0.3231 | 0.2676 | ||
0.3856 | 0.3161 | ||
DHN [50] | 0.1182 | 0.1255 | |
0.1642 | 0.1693 | ||
0.2274 | 0.2153 | ||
0.3202 | 0.2449 | ||
DPSH [23] | 0.0895 | 0.1220 | |
0.2825 | 0.2632 | ||
0.4545 | 0.4005 | ||
0.5213 | 0.4334 | ||
DHNN-L2 [24] | 0.0451 | 0.1147 | |
0.0683 | 0.1291 | ||
0.2044 | 0.1329 | ||
0.2190 | 0.1304 | ||
DCSNN (ours) | 0.2519 | 0.4889 | |
0.6145 | 0.6301 | ||
0.6481 | 0.5783 | ||
0.6813 | 0.5819 |
Table 3. mAP results of the DCSNN and binary hashing methods before reranking on Yukon–Kuskokwim Delta PolSAR map
Methods | Feature length |
AlexNet [21] | VGG-11 [40] |
Pretrained (BH) | 0.0566 | 0.0396 | |
0.1048 | 0.0550 | ||
0.1927 | 0.1227 | ||
0.2196 | 0.1174 | ||
Pretrained (PQ+AQD) | 0.2684 | 0.1736 | |
0.3580 | 0.2091 | ||
0.3718 | 0.2579 | ||
0.4184 | 0.2690 | ||
DHN [50] | 0.1219 | 0.1610 | |
0.2072 | 0.1910 | ||
0.2715 | 0.2447 | ||
0.3627 | 0.2239 | ||
DPSH [23] | 0.1347 | 0.1170 | |
0.2371 | 0.2516 | ||
0.3649 | 0.3281 | ||
0.4098 | 0.3331 | ||
DHNN-L2 [24] | 0.0621 | 0.1132 | |
0.1556 | 0.1812 | ||
0.2916 | 0.3166 | ||
0.3227 | 0.2822 | ||
DCSNN (ours) | 0.4393 | 0.4324 | |
0.5424 | 0.5196 | ||
0.5734 | 0.4913 | ||
0.5996 | 0.4831 |
Table 4. mAP values before and after reranking with SAR-SIFT or SIFT on Hayward Fault Zone PolSAR map
CNN backbone | Feature length |
Before reranking | After reranking (SAR-SIFT/SIFT) |
AlexNet [21] | 0.2519 | 0.4074/0.3533 | |
0.6145 | 0.7394/0.6850 | ||
0.6481 | 0.7548/0.6998 | ||
0.6813 | 0.7760/0.7252 | ||
VGG-11 [40] | 0.4889 | 0.6512/0.5813 | |
0.6301 | 0.7540/0.6923 | ||
0.5783 | 0.6799/0.6231 | ||
0.5819 | 0.6787/0.6216 |
Table 5. mAP values of the DCSNN before and after reranking with SAR-SIFT or SIFT on Yukon–Kuskokwim Delta PolSAR map
CNN backbone | Feature length |
Before reranking | After reranking (SAR-SIFT/SIFT) |
AlexNet [21] | 0.4393 | 0.5831/0.5965 | |
0.5424 | 0.6591/0.6693 | ||
0.5734 | 0.6782/0.6888 | ||
0.5996 | 0.7021/0.7123 | ||
VGG-11 [40] | 0.4324 | 0.5909/0.6030 | |
0.5196 | 0.6418/0.6521 | ||
0.4913 | 0.5939/0.6036 | ||
0.4831 | 0.5840/0.5940 |
Table 6. Positioning accuracy examples
Inquiry SAR Patch | Actual Coordinates [ |
Estimated Coordinates [ |
Error [ |
Fig. 8(a) | 38.0625, -122.2733 | 38.0625, -122.2734 | 5.7288 |
Fig. 8(b) | 37.9836, -122.3599 | 37.9836, -122.3600 | 5.7347 |
Fig. 8(c) | 61.0926, -164.1878 | 61.0926, -164.1879 | 4.2529 |
Fig. 8(d) | 61.0808, -164.1208 | 61.0808, -164.1208 | 5.0970 |
Table 7. Mean and standard deviation of positioning error results
Data Name | Success Cases Ratio [%] | Distance Error [ |
Hayward Fault Zone | 98.50 | 4.9635 |
Yukon–Kuskokwim Delta | 97.70 | 4.9522 |
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