【目的】研究芦苇(Phragmites australis)湿地土地类型变化、监测覆被特征,为湿地保护和开发提供参考。【方法】基于2017—2023年9月扎龙湿地Sentinel-2遥感影像,制作了包含湖泊、芦苇地、建筑地、耕地、盐碱地5种土地类型的遥感影像数...【目的】研究芦苇(Phragmites australis)湿地土地类型变化、监测覆被特征,为湿地保护和开发提供参考。【方法】基于2017—2023年9月扎龙湿地Sentinel-2遥感影像,制作了包含湖泊、芦苇地、建筑地、耕地、盐碱地5种土地类型的遥感影像数据集。通过视觉状态空间(visual state space model,Vmamba)联合注意力机制并结合水体指数NDWI生成水体掩膜对研究区进行信息提取,统计各土地类型的位置和面积变化信息。利用像元二分法提取植被覆盖度(fractional vegetation cover,FVC),计算叶面积指数(leaf area index,LAI)和生态质量指数(ecosystem quality index,EQI)。【结果】通过本研究提出的方法对研究区内分布信息进行提取,整体精度(overall accuracy,OA)为80.85%、平均交并比(mean intersection over union,MIoU)为71.59%,宏观平均F1值(macro-F1,MF1)为79.93%。2017—2023年,在扎龙湿地内湖泊、芦苇地的覆盖面积呈增加趋势;耕地、建筑地的覆盖面积呈减少趋势;盐碱地的覆盖面积呈波动趋势。植被覆盖度、生态质量指数先升高后降低,与中国气候公报内容基本一致。【结论】Vmamba联合注意力机制并结合水体掩膜的模型,在湿地信息提取方面效果良好,一定程度上提高土地利用分类与变化监测的精度。植被覆盖度、叶面积指数、生态质量指数的监测对湿地资源管理与可持续利用提供借鉴。展开更多
Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers.However,CNNs exhibit constrained performance in high-resolution image transmission,while Transformers incur high c...Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers.However,CNNs exhibit constrained performance in high-resolution image transmission,while Transformers incur high computational cost due to quadratic complexity.Recently,VMamba,a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities,has shown great potential in computer vision tasks.Inspired by this,we propose MNTSCC,an efficient VMamba-based nonlinear joint source-channel coding(JSCC)model for wireless image transmission.Specifically,MNTSCC comprises a VMamba-based nonlinear transform module,an MCAM entropy model,and a JSCC module.In the encoding stage,the input image is first encoded into a latent representation via the nonlinear transformation module,which is then processed by the MCAM for source distribution modeling.The JSCC module then optimizes transmission efficiency by adaptively assigning transmission rate to the latent representation according to the estimated entropy values.The proposedMCAMenhances the channel-wise autoregressive entropy model with attention mechanisms,which enables the entropy model to effectively capture both global and local information within latent features,thereby enabling more accurate entropy estimation and improved rate-distortion performance.Additionally,to further enhance the robustness of the system under varying signal-to-noise ratio(SNR)conditions,we incorporate SNR adaptive net(SAnet)into the JSCCmodule,which dynamically adjusts the encoding strategy by integrating SNRinformationwith latent features,thereby improving SNR adaptability.Experimental results across diverse resolution datasets demonstrate that the proposed method achieves superior image transmission performance compared to existing CNN-and Transformer-based semantic communication models,while maintaining competitive computational efficiency.In particular,under an Additive White Gaussian Noise(AWGN)channel with SNR=10 dB and a channel bandwidth ratio(CBR)of 1/16,MNTSCC consistently outperforms NTSCC,achieving a 1.72 dB Peak Signal-to-Noise Ratio(PSNR)gain on the Kodak24 dataset,0.79 dB on CLIC2022,and 2.54 dB on CIFAR-10,while reducing computational cost by 32.23%.The code is available at https://github.com/WanChen10/MNTSCC(accessed on 09 July 2025).展开更多
文摘【目的】研究芦苇(Phragmites australis)湿地土地类型变化、监测覆被特征,为湿地保护和开发提供参考。【方法】基于2017—2023年9月扎龙湿地Sentinel-2遥感影像,制作了包含湖泊、芦苇地、建筑地、耕地、盐碱地5种土地类型的遥感影像数据集。通过视觉状态空间(visual state space model,Vmamba)联合注意力机制并结合水体指数NDWI生成水体掩膜对研究区进行信息提取,统计各土地类型的位置和面积变化信息。利用像元二分法提取植被覆盖度(fractional vegetation cover,FVC),计算叶面积指数(leaf area index,LAI)和生态质量指数(ecosystem quality index,EQI)。【结果】通过本研究提出的方法对研究区内分布信息进行提取,整体精度(overall accuracy,OA)为80.85%、平均交并比(mean intersection over union,MIoU)为71.59%,宏观平均F1值(macro-F1,MF1)为79.93%。2017—2023年,在扎龙湿地内湖泊、芦苇地的覆盖面积呈增加趋势;耕地、建筑地的覆盖面积呈减少趋势;盐碱地的覆盖面积呈波动趋势。植被覆盖度、生态质量指数先升高后降低,与中国气候公报内容基本一致。【结论】Vmamba联合注意力机制并结合水体掩膜的模型,在湿地信息提取方面效果良好,一定程度上提高土地利用分类与变化监测的精度。植被覆盖度、叶面积指数、生态质量指数的监测对湿地资源管理与可持续利用提供借鉴。
文摘Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers.However,CNNs exhibit constrained performance in high-resolution image transmission,while Transformers incur high computational cost due to quadratic complexity.Recently,VMamba,a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities,has shown great potential in computer vision tasks.Inspired by this,we propose MNTSCC,an efficient VMamba-based nonlinear joint source-channel coding(JSCC)model for wireless image transmission.Specifically,MNTSCC comprises a VMamba-based nonlinear transform module,an MCAM entropy model,and a JSCC module.In the encoding stage,the input image is first encoded into a latent representation via the nonlinear transformation module,which is then processed by the MCAM for source distribution modeling.The JSCC module then optimizes transmission efficiency by adaptively assigning transmission rate to the latent representation according to the estimated entropy values.The proposedMCAMenhances the channel-wise autoregressive entropy model with attention mechanisms,which enables the entropy model to effectively capture both global and local information within latent features,thereby enabling more accurate entropy estimation and improved rate-distortion performance.Additionally,to further enhance the robustness of the system under varying signal-to-noise ratio(SNR)conditions,we incorporate SNR adaptive net(SAnet)into the JSCCmodule,which dynamically adjusts the encoding strategy by integrating SNRinformationwith latent features,thereby improving SNR adaptability.Experimental results across diverse resolution datasets demonstrate that the proposed method achieves superior image transmission performance compared to existing CNN-and Transformer-based semantic communication models,while maintaining competitive computational efficiency.In particular,under an Additive White Gaussian Noise(AWGN)channel with SNR=10 dB and a channel bandwidth ratio(CBR)of 1/16,MNTSCC consistently outperforms NTSCC,achieving a 1.72 dB Peak Signal-to-Noise Ratio(PSNR)gain on the Kodak24 dataset,0.79 dB on CLIC2022,and 2.54 dB on CIFAR-10,while reducing computational cost by 32.23%.The code is available at https://github.com/WanChen10/MNTSCC(accessed on 09 July 2025).
