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(a) (b) (c)Fig. 6. Visual samples from different datasets: (a) MillionAID, (b) Potsdam, |
and (c) iSAID. |
2) Implementation Detail and Experimental Setting: We |
adopt different settings for the above backbone networks by |
following the common practice. Concretely, the ResNet based |
networks are trained with the mini-batch stochastic gradient |
descent with momentum (SGDM) strategy, where the initial |
learning rate, weight decay, and momentum are separately set |
to 0.01, 0.0005, and 0.9. The learning rate is optimized by the |
polynomial scheduler: currentlr=minlr+initiallr·(1− |
iter |
max iter)power, whereminlr= 0.0001,power = 0.9. While |
the vision transformers, such as the Swin-T and ViTAEv2- |
S, are trained using the AdamW optimizer, whose learning |
rate and weight decay are 6e-5 and 0.01. The learning rate |
schedule adopts the polynomial decay policy with a power |
of 1.0 and minlrof 0. They also have a linear warming |
up stage at the first 1,500 iterations with the initial learning |
rate of 1e-6. For a fair comparison, all networks are training |
80k iterations with a batch size of 8. Following [13], [14], we |
use the UperNet [15] as the unified segmentation framework |
for all the pretrained backbones since the output stride equals |
32. All methods are implemented based on mmsegmentation |
[82]. For the convenience of training, the Potsdam and iSAID |
are separately sampled and cropped into patches with a size |
of 512 ×512 and 896 ×896 with a stride of 384 and |
512, respectively. We use the random horizontal flipping data |
augmentation strategy. Following the evaluation protocol in the |
aerial segmentation community, for the Potsdam dataset, we |
report the OA, mean F1 score (mF1), and per class F1 score. |
Note that the clutter category is regarded as the background |
and ignored during computing loss and evaluation metrics. |
While for the iSAID dataset, the intersection over union (IoU) |
of foreground categories and the average IOU of all classes |
(including background) are calculated. All evaluations are |
conducted on a single scale for a fair comparison. |
3) Experimental Results: Quantitative Results and Anal- |
yses: Table VI-VII present the segmentation results of our |
methods and other SOTA methods. It can be seen that when |
changing the backbone from ResNet-50 to Swin-T, and then |
to ViTAEv2-S, the performance is increased. The results are |
consistent with the aforementioned scene recognition results, |
showing the better representation ability of vision transform- |
ers. Although the ViTAEv2-S obtains the highest OA on the |
Potsdam dataset, its mF1 is not as well as LANet [92]. From |
Table VII, we can find that the scores of the “Car” category of |
the selected models are worse than other methods. We suspect |
that it may be because of the encoder-decoder structure and |
the rough feature fusion strategy in the UperNet, where the |
WANG et al. : EMPIRICAL STUDY OF REMOTE SENSING PRETRAINING 11 |
TABLE VI |
RESULTS OF THE UPERNET SEGMENTATION MODEL WITH DIFFERENT BACKBONES AND SOTA METHODS ON THE TESTING SET OF THE POTSDAM |
DATASET . |
Method Backbone OA mF1F1 score per category |
Imper. surf. Building Low veg. Tree Car |
FCN [83] IMP-VGG-16 85.59 87.61 88.61 93.29 83.29 79.83 93.02 |
S-RA-FCN [84] IMP-VGG-16 88.59 90.17 91.33 94.70 86.81 83.47 94.52 |
Multi-filter CNN [85] IMP-VGG-16 90.65 85.23 90.94 96.98 76.32 73.37 88.55 |
FCN [83] IMP-ResNet-50 89.42 88.66 91.46 96.63 85.99 86.94 82.28 |
DANet [86] IMP-ResNet-50 89.72 89.14 91.61 96.44 86.11 88.04 83.54 |
PSPNet [87] IMP-ResNet-50 89.45 90.51 91.61 96.30 86.41 86.84 91.38 |
DeeplabV3+ [88] IMP-ResNet-50 89.74 90.94 92.35 96.77 85.22 86.79 93.58 |
ResT [89] IMP-ResNet-50 89.13 90.89 91.14 95.11 86.30 87.27 94.63 |
EaNet [90] IMP-ResNet-50 90.15 91.73 92.87 96.30 86.16 87.99 95.30 * |
BotNet [91] IMP-ResNet-50 90.42 91.77 92.34 96.30 87.32 * 88.74 * 94.17 |
LANet [92] IMP-ResNet-50 90.84 91.95 * 93.05 97.19 * 87.30 88.04 94.19 |
UperNet IMP-ResNet-50 90.64 89.96 92.30 96.14 85.93 85.66 89.76 |
UperNet SeCo-ResNet-50 89.64 89.03 91.21 94.92 85.12 84.89 89.02 |
UperNet RSP-ResNet-50 90.61 89.94 92.42 96.15 85.75 85.49 89.87 |
UperNet IMP-Swin-T 91.17 90.60 92.94 96.66 86.54 85.87 90.98 |
UperNet RSP-Swin-T 90.78 90.03 92.65 96.35 86.02 85.39 89.75 |
UperNet IMP-ViTAEv2-S 91.60* 91.00 93.34* 96.84 87.28 86.38 91.18 |
UperNet RSP-ViTAEv2-S 91.21 90.64 93.05 96.62 86.62 85.89 91.01 |
TABLE VII |
RESULTS OF THE UPERNET SEGMENTATION MODEL WITH DIFFERENT BACKBONES AND SOTA METHODS ON THE VALIDATION SET OF THE I SAID |
DATASET . |
Method Backbone mIOUIOU per category1 |
Ship ST BD TC BC GTF Bridge LV SV HC SP RA SBF Plane Harbor |
FCN [83] IMP-VGG-16 41.7 51.7 22.9 26.4 74.8 30.2 27.9 8.2 49.3 37.0 0 30.7 51.9 52.1 62.9 42.0 |
UNet [93] - 39.2 49.0 0 36.5 78.6 22.9 5.5 7.5 49.9 35.6 0 38.0 46.5 9.7 74.7 45.6 |
DenseASPP [94] IMP-DenseNet-121 56.8 61.1 50.0 67.5 86.1 56.6 52.3 29.6 57.1 38.4 0 43.3 64.8 74.1 78.1 51.1 |
DenseUNet [95] IMP-DenseNet-121 58.7 66.1 50.4 76.1 86.2 57.7 49.5 33.9 54.7 46.2 0 45.1 65.9 71.9 82.2 54.6 |
Semantic FPN [96] IMP-ResNet-50 59.3 63.7 59.5 71.8 86.6 57.8 51.6 34.0 59.2 45.1 0 46.4 68.7 73.6 80.8 51.3 |
RefineNet [97] IMP-ResNet-50 60.2 63.8 58.6 72.3 85.3 61.1 52.8 32.6 58.2 42.4 23.0 43.4 65.6 74.4 79.9 51.1 |
PSPNet [87] IMP-ResNet-50 60.3 65.2 52.1 75.7 85.6 61.1 60.2 32.5 58.0 43.0 10.9 46.8 68.6 71.9 79.5 54.3 |
DeeplabV3 [98] IMP-ResNet-50 59.0 59.7 50.5 77.0 84.2 57.9 59.6 32.9 54.8 33.7 31.3 44.7 66.0 72.1 75.8 45.7 |
DeeplabV3+ [88] IMP-ResNet-50 60.8 63.9 52.5 72.8 84.9 56.5 58.9 32.2 59.1 42.9 31.4 46.1 67.7 72.9 79.8 52.6 |
EMANet [99] IMP-ResNet-50 55.4 63.1 68.4 66.2 82.7 56.0 18.8 42.1 58.2 41.0 33.4 38.9 46.9 46.4 78.5 47.5 |
ASP-OCNet [100] IMP-ResNet-50 40.2 47.3 40.2 44.4 65.0 24.1 29.9 27.1 46.3 13.6 10.3 34.6 37.9 41.4 68.1 38.0 |
DANet [86] IMP-ResNet-50 57.5 60.2 63.0 71.4 84.7 50.9 52.5 28.6 57.5 42.1 30.4 46.1 40.6 63.3 80.9 48.8 |
CCNet [101] IMP-ResNet-50 58.3 61.4 65.7 68.9 82.9 57.1 56.8 34.0 57.6 38.3 31.6 36.5 57.2 75.0 75.8 45.9 |
EncNet [102] IMP-ResNet-50 58.9 59.7 64.9 70.0 84.2 55.2 46.3 36.8 57.2 38.7 34.8 42.4 59.8 69.8 76.1 48.0 |
HRNet [103] IMP-HRNetW-18 61.5 65.9 68.9 74.0 86.9 59.4 61.5 33.8 62.1 46.9 14.9 44.2 52.9 75.6 81.7 52.2 |
RANet [104] IMP-ResNet-50 62.1 67.1 61.3 72.5 85.1 53.2 47.1 45.3* 60.1 49.3 38.1 41.8 70.5 58.8 83.1 55.6 |
AlignSeg [105] IMP-ResNet-50 62.1 67.4 68.9 76.2 86.2 62.1 52.0 28.7 60.7 50.3 31.2 45.7 56.2 71.2 82.9 54.8 |
OCR [106] IMP-HRNet-W48 62.6 67.8 70.7 73.6 87.9 63.4 47.7 33.1 61.4 49.6 30.4 48.4 59.5 72.8 83.3 53.3 |
HMANet [107] IMP-ResNet-50 62.6 65.4 70.9 74.7 88.7 60.5 54.6 29.0 59.7 50.3 32.6 51.4 62.9 70.2 83.8 51.9 |
FarSeg [108] IMP-ResNet-50 63.7 65.4 61.8 77.7 86.4 62.1 56.7 36.7 60.6 46.3 35.8 51.2 71.4* 72.5 82.0 53.9 |
FactSeg [109] IMP-ResNet-50 64.8 68.3 56.8 78.4* 88.9* 64.9* 54.6 36.3 62.7 49.5 42.7 51.5* 69.4 73.6 84.1 55.7 |
UperNet IMP-ResNet-50 61.9 65.9 73.9 68.1 70.7 57.3 52.5 39.2 61.2 48.8 34.3 44.5 62.1 76.8 83.8 52.2 |
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