A Reliable Semantic Transmission Framework for Low-Altitude Image Perception

ZHANG Zhongqiang; MIAO Yajie; MA Shuai; SHI Guangming; WANG Ye; LI Bin

doi:10.11999/JEIT260875

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ZHANG Zhongqiang, MIAO Yajie, MA Shuai, SHI Guangming, WANG Ye, LI Bin. A Reliable Semantic Transmission Framework for Low-Altitude Image Perception[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260875

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ZHANG Zhongqiang, MIAO Yajie, MA Shuai, SHI Guangming, WANG Ye, LI Bin. A Reliable Semantic Transmission Framework for Low-Altitude Image Perception[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260875

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A Reliable Semantic Transmission Framework for Low-Altitude Image Perception

doi: 10.11999/JEIT260875 cstr: 32379.14.JEIT260875

1.
Peng Cheng Laboratory, Shenzhen 518055, China
2.
Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China

Funds: The Major Key Project of Pengcheng Laboratory (PCL2025A03), the Natural Science Foundation (NSF) of China (62501333, 62471270), the Natural Science Basic Research Project of Fujian Province (2026J008339)

Received Date: 2026-06-29
Accepted Date: 2026-10-19
Rev Recd Date: 2026-10-19

Available Online: 2026-10-28

Abstract

Abstract

Objective Low-altitude intelligent platforms support aerial inspection, smart logistics, emergency search and rescue, and disaster monitoring by transmitting image observations to edge or ground receivers. Reliable scene recognition depends on preserving the information needed for these tasks over wireless links with limited bandwidth and changing channel conditions. Conventional image transmission emphasizes accurate recovery of pixels or bits, which can consume communication resources for image content that contributes little to the downstream task. Task-oriented semantic communication instead prioritizes discriminative information for scene perception. However, platform motion, occlusion, and multipath propagation can disturb the transmission link, while small input perturbations can shift the extracted semantic features and change the predicted category. Low-altitude images also contain fine local textures and complex spatial layouts, so reliable scene perception requires both local detail and global structural information. A reliable semantic transmission framework for low-altitude image perception is therefore proposed to preserve task-relevant features under dynamic channels and semantic noise. The objective is to improve classification accuracy at the receiver through the joint design of robust feature extraction, compact semantic transmission, and a shared semantic knowledge base. Methods The framework models semantic noise as a constrained task perturbation generated by the Fast Gradient Sign Method (FGSM) to increase classification loss while keeping visual changes small. The framework contains a Robust Feature Extraction Module (RFEM), a Joint Source-Channel Coding (JSCC) encoder and decoder, a shared Semantic Knowledge Base (SKB), and a scene perception module, with the transmitter placed at the low-altitude platform and the receiver at an edge or ground node (Fig. 1). RFEM combines a local multi-scale branch and a global State Space Duality (SSD) branch. The local branch uses depthwise separable convolutions with kernel sizes of 3 by 3, 5 by 5, and 7 by 7 to extract textures, edges, and local structures at different scales. The global branch employs non-causal SSD modeling to capture long-range spatial dependencies among image regions. Both branches provide 256-dimensional features, which are combined through an attention fusion block with eight heads to form a representation less sensitive to semantic noise (Fig. 2). The JSCC encoder contains five convolutional layers to compress features, while the corresponding decoder uses five deconvolutional layers to recover task-relevant semantic representations at the receiver (Fig. 3). The SKB contains trainable semantic primitive vectors shared by the transmitter and receiver. Continuous encoded features are matched to the nearest primitive vectors, and their indices are transmitted for retrieval at the receiver. During training, a straight-through estimator approximates gradients through the non-differentiable nearest-neighbor mapping. Feature-to-primitive alignment and reduced similarity between different primitive vectors are encouraged during training to strengthen discriminability and limit semantic feature confusion. The recovered representation is perceived using global average pooling, a fully connected layer, and a Softmax function (Fig. 4). The system is optimized end to end using cross-entropy classification loss and SKB-related constraints. Evaluation uses UCMerced-LandUse, containing 21 scene categories, and NWPU-RESISC45, containing 45 categories. Results are averaged over three independent runs. Results and Discussions Classification accuracy is evaluated over Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. The compared methods include Joint Photographic Experts Group (JPEG) image compression with Low-Density Parity-Check (LDPC) channel coding, DT-JSCC with discrete task representations and digital modulation, and ASE-JSCC with attention-based semantic encoding and decoding. At an SNR of 3 dB on UCMerced-LandUse without semantic noise, the proposed method achieves accuracies of 87.07% and 85.62% over AWGN and Rayleigh fading channels, respectively. These values exceed ASE-JSCC by 2.83% and 1.69%. With semantic noise, the corresponding accuracies are 83.01% and 80.68%, exceeding ASE-JSCC by 3.75% and 3.25% (Table 1). On NWPU-RESISC45 under the same channel conditions, the accuracies of our method without semantic noise are 76.12% and 73.31%, with gains of 5.28% and 3.74% over ASE-JSCC. With semantic noise, the framework achieves 72.54% and 71.42%, compared with 61.62% and 59.03% for ASE-JSCC, giving gains of 10.92% and 12.39% (Table 2). The larger gains on this dataset indicate the benefit of maintaining discriminative semantics across a wider range of scene categories. Ablation experiments at an SNR of 3 dB establish the contributions of the two principal modules. On UCMerced-LandUse, removing RFEM reduces accuracy to 77.38% and 75.79% under AWGN and Rayleigh fading, while removing SKB reduces it to 71.47% and 69.05%. Removing both modules gives 63.85% and 57.96%, compared with 87.07% and 85.62% for the complete framework (Table 3). On NWPU-RESISC45, the corresponding results are 66.27% and 62.93% without RFEM, 61.03% and 58.74% without SKB, and 52.38% and 50.44% without both modules, compared with 76.12% and 73.31% for the complete framework (Table 4). These reductions support the complementary contributions of stable feature extraction and discrete semantic representation, with removal of SKB producing the larger loss on both datasets. Across all SNRs, the framework achieves the highest classification accuracy among the compared methods on UCMerced-LandUse with and without semantic noise (Fig. 5, Fig. 6). The same advantage can be observed on NWPU-RESISC45 under both channel models (Fig. 7, Fig. 8). As semantic noise intensity increases from 0 to 0.036, classification accuracy generally decreases, but the framework retains the highest accuracy on both datasets, demonstrating robustness over different noise perturbation (Fig. 9, Fig. 10). Complexity measurements show that the proposed model contains 4.77 million parameters, lower than 11.28 million for DT-JSCC and 14.54 million for ASE-JSCC. Its computational cost is 6.45 billion Floating-Point Operations (FLOPs), and its inference latency is 6.93 ms. Although its computational cost and latency exceed those of ASE-JSCC, its parameter count is lower and its classification accuracy is higher, showing the tradeoff between model size, computation, and task reliability (Table 5). Conclusions The proposed framework improves task-oriented semantic transmission for low-altitude image perception by preserving local discriminative details and global structural information, compressing task-relevant features through JSCC, and reinforcing their representation through a shared SKB. The experimental comparisons show higher classification accuracy than JPEG+LDPC, DT-JSCC, and ASE-JSCC over the AWGN and Rayleigh fading channels with semantic noise. The ablation results substantiate the contributions of RFEM and SKB. These results demonstrate the effectiveness of the framework for reliable low-altitude scene recognition under different communication conditions.
- Semantic knowledge base,
- Reliable transmission of low-altitude images,
- Robust semantic features

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