Remote sensing semantic segmentation requires models that can jointly capture fine spatial details and high-level semantic context across complex scenes. While classical encoder-decoder architectures such as U-Net remain strong baselines, they often struggle to fully exploit global semantics and structured feature interactions. In this work, we propose HQF-Net, a hybrid quantum-classical multi-scale fusion network for remote sensing image segmentation. HQF-Net integrates multi-scale semantic guidance from a frozen DINOv3 ViT-L/16 backbone with a customized U-Net architecture through a Deformable Multiscale Cross-Attention Fusion (DMCAF) module. To enhance feature refinement, the framework further introduces quantum-enhanced skip connections (QSkip) and a Quantum bottleneck with Mixture-of-Experts (QMoE), which combines complementary local, global, and directional quantum circuits within an adaptive routing mechanism. Experiments on three remote sensing benchmarks show consistent improvements with the proposed design. HQF-Net achieves 0.8568 mIoU and 96.87% overall accuracy on LandCover.ai, 71.82% mIoU on OpenEarthMap, and 55.28% mIoU with 99.37% overall accuracy on SeasoNet. An architectural ablation study further confirms the contribution of each major component. These results show that structured hybrid quantum-classical feature processing is a promising direction for improving remote sensing semantic segmentation under near-term quantum constraints.
Figures & tables
Figure 1: Overview of the proposed HQF-Net architecture. The model integrates a frozen DINOv3 ViT-L/16 encoder with Deformable Multi-Scale Cross-Attention Fusion (DMCAF) module. The main hybrid components are Quantum Skip (QSkip) refinement blocks and a bottleneck with Quantum Mixture-of-Experts (QMoE) block that adaptively combines local, global, and diagonal quantum experts. CT2D denotes transposed convolution.
Figure 2: Overview of Quantum Circuits used in HQF-Net - a) Enrichment Multiscale Circuit, b) Local Circuit, c) Global Circuit, and d) Diagonal Circuit
Source
Model
mIoU
OA (%)
Fan et al. [ 11 ]
FQCNN
0.2000
53.26
Fan et al. [ 11 ]
MQCNN
0.1520
39.03
Kumar et al. [ 25 ]
CNN
0.1500
45.87
Kumar et al. [ 25 ]
COQCNN
0.1280
36.65
Ronneberger et al. [ 39 ]
UNet
0.6451
82.43
Zhou et al. [ 55 ]
UNet++
0.6553
70.89
Table 1: Performance comparison on the LandCover.ai test set.
Model
Backbone
mIoU
UNet [ 39 ]
—
60.4
DANet [ 12 ]
ResNet101
60.1
ClipSeg [ 30 ]
CLIP
58.6
BoTNet [ 44 ]
ResNet50
61.5
MANet [ 27 ]
ResNet101
64.0
SegFormer [ 53 ]
MiT
66.0
Table 2: Comparison on OpenEarthMap with State-of-the-Art Methods (mIoU %)
Model
Backbone
Acc.
mIoU
DeepLabv3 [ 24 ]
DenseNet121
–
47.53
DeepLabv3 PT [ 24 ]
DenseNet121
–
48.69
DeepLabv3 [ 5 ]
ResNet-50
83.52
50.79
DeepLabv3 [ 5 ]
ConvNeXt-Small
81.36
46.39
DeepLabv3 [ 5 ]
Swin-Tiny
82.75
50.81
UperNet [ 52 ]
ResNet-50
83.20
49.59
Table 3: Performance Comparison on SeasoNet (Accuracy and mIoU %)
Figure 3: Qualitative segmentation on the LandCover.ai dataset, showing input images, ground-truth masks, and predictions by HQF-Net.
Model Variant
DINOv3
DMCAF
Q-Skip
Q-MoE
mIoU
OA (%)
Baseline Fusion Methods
U-Net + DINOv3 (Multiplication)
✓
0.7335
90.24
U-Net + DINOv3 (Addition)
✓
0.7520
91.80
Proposed Hybrid Architectures
+ DMCAF (Deformable Fusion)
✓
✓
0.7815
92.10
+ DMCAF + Q-Skip
✓
✓
✓
0.8387
94.22
Table 4: Ablation study showing the impact of different fusion strategies and quantum components on the LandCover.ai dataset.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 4: Two-qubit parameterized quantum filter used as a local interaction block in HQF-Net. The unit models pairwise feature interactions and serves as a basic building block within the larger quantum circuit designs.
Figure 5: Qualitative segmentation results on the SeasoNet dataset showing original images, ground-truth masks, and comparisons with other models.
Figure 6: Qualitative segmentation results on the OpenEarthMap dataset showing original images, ground-truth masks, and comparisons with other models.
Figure 7: Qualitative segmentation results on the OpenEarthMap and SeasoNet datasets showing original images, ground-truth masks, and HQF-Net predictions.
Space Applications Centre, Indian Space Research Organisation (ISRO), India · New-Brunswick Honors College, Rutgers University, New Jersey · Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay
Space Applications Centre, Indian Space Research Organisation, Ahmedabad, India · Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India