Accurate land-cover mapping from multimodal Earth observation (EO) data remains a challenge in regions where high-resolution annotations are scarce or costly to obtain. This work introduces a quantum convolutional encoder-decoder network for pseudo-label generation using Sentinel-1 SAR and Sentinel-2 multispectral imagery, guided only by coarse MODIS-derived land-cover maps. The proposed framework uses quantum-inspired convolutional blocks to enhance feature extraction and cross-sensor fusion, enabling the model to infer a fine-scale semantic structure from heterogeneous inputs. Experiments were conducted on the DFC2020 dataset, which provides a quadruple of Sentinel-1 SAR, Sentinel-2 multispectral, low-resolution MODIS labels and high-resolution reference land-cover maps. The proposed training framework first learns a robust representation of multimodal inputs through the reconstruction branch before any supervision from low-resolution labels is applied. This self-supervised stage enables the model to capture a fine-scale spectral-spatial structure, which is then used to generate high-resolution pseudo-labels. These pseudo-labels are subsequently evaluated through classification metrics on the test data, demonstrating that the proposed approach produces more spatially coherent and class-consistent compared to classical baselines. The results highlight the potential of quantum-inspired deep learning to improve label generation in low-annotation remote sensing scenarios, offering a scalable path towards high-quality land-cover products for EO applications.
Figures & tables
Figure 1: Quantum convolutional circuit (QuantumConv) with n qubits showing staggered entanglement links.
Figure 2: Overall framework of the proposed approach for the Land Cover Segmentation with Pre-training for masked pseudo label generation.
Figure 3: Pre-training for Image Reconstruction and Raw vs Reconstructed patch when both Reconstruction and Auxiliary Classifier branch were trained
Figure 4: Pre-training for High-Resolution Pseudo Label Generation
Metrics (%)
Baseline
Qbottleneck
Proposed
Overall Accuracy (OA)
0.7993
0.8171
0.8314
Kappa Coefficient ( κ )
0.7163
0.7413
0.7612
mIoU
0.5998
0.6238
0.6434
Total No. of Parameters (approx.)
2,007,898
1,126,378
169,546
Table 1: Ablation study of the proposed approach on the selected 5 dominant classes of the DFC2020 test set with remapped labels like Forest, Grassland, Croplands, Urban and Water. Experiments used a batch size of 64 over 986 image patches of size 256×256 . Baseline - Classical Convolutional Blocks, Qbottleneck - Only Quantum-inspired bottleneck
Metrics (%)
UNet
TransUNet
Proposed
Overall Accuracy (OA)
0.8194
0.8238
0.8314
Kappa Coefficient ( κ )
0.7454
0.7500
0.7612
mIoU
0.6272
0.6154
0.6434
Total No. of Parameters (approx.)
31,392,395
26,665,867
169,546
Computational Complexity
1.64
1.10
2.12
(Training: iterations/s)
Table 2: Comparison of classification results between the proposed method and reference approaches on the selected 5 dominant classes of the DFC2020 test set with remapped labels like Forest, Grassland, Croplands, Urban and Water. Experiments used a batch size of 64 over 986 image patches of size 256×256 .
Semantic segmentation in remote sensing is commonly addressed using classical deep learning architectures such as U-Net, which require a large number of parameters to model complex spatial relationships. Quantum machine learning (QML) provides an alternative representation paradigm by mapping classical features into quantum states, but its direct application to high-dimensional images remains challenging under near-term quantum hardware constraints. In this work, we propose HQ-UNet, a hybrid quantum-classical U-Net architecture that integrates a compact parameterized quantum circuit at the bottleneck of a classical U-Net. The proposed design uses a non-pooling quantum convolutional module to enrich highly compressed encoder features before decoding, while keeping the quantum component shallow and parameter-efficient. Experiments on the LandCover.ai dataset show that HQ-UNet achieves a mean IoU of 0.8050 and an overall accuracy of 94.76%, outperforming the classical U-Net baseline. These results suggest that compact quantum bottlenecks can enhance feature representation for remote sensing image segmentation under near-term quantum constraints. This highlights the potential of hybrid quantum-classical designs as a promising direction for parameter-efficient dense prediction in Earth observation.
Md Aminur Hossain, Ayush V. Patel, Ikshwaku Vanani +1
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
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.
Md Aminur Hossain, Ayush V. Patel, Siddhant Gole +2
Space Applications Centre, ISRO, India · Indian Institute of Technology Bombay
We introduce HQ-JEPA, a hybrid quantum-classical joint-embedding predictive architecture for cross-modal remote sensing representation learning. The proposed framework extends JEPA-style masked latent prediction to paired Sentinel-1 and Sentinel-2 imagery by predicting masked target representations from visible context regions while aligning heterogeneous modality features in a shared embedding space. To improve representation quality, HQ-JEPA combines four complementary objectives: latent token prediction, cross-modal token alignment, SIGReg-based Gaussian regularization in the fused latent space, and a differentiable SWAP-test-based Fidelity Quantum Similarity (FQS) loss. Unlike pixel reconstruction methods, HQ-JEPA learns semantic representations directly in latent space and uses quantum state-overlap-based similarity as an additional regularization signal. We evaluate the pretrained encoder on GeoBench classification and segmentation tasks under linear probing and fine-tuning settings. Results show that HQ-JEPA achieves competitive and often superior performance over strong self-supervised and remote sensing foundation-model baselines, demonstrating the benefit of integrating predictive self-supervision, cross-modal geometric regularization, and quantum fidelity-based representation learning for remote sensing applications.
Md Aminur Hossain, Ayush V. Patel, Sanjay K. Singh +1
Space Applications Centre, Indian Space Research Organisation, Ahmedabad, India · Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India