[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2164":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":56},2164,"Plastic Greenhouse Extraction Based on Index-Guided Three-Stage Large-Factor Remote Sensing Image Super-Resolution Reconstruction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183108","Fine-scale, large-area plastic greenhouse (PG) mapping generally relies on costly high-resolution imagery with limited spatial coverage. Although freely available Sentinel-2 data offer wide swaths and frequent revisits, their insufficient spatial resolution limits PG extraction accuracy. Direct super-resolution (SR) reconstruction from 10 m or coarser imagery to the submeter level is highly ill posed because PG structures must be inferred from severely mixed pixels. To address these challenges, we propose a three-stage 12× SR framework guided by a spatially enhanced Agricultural Plastic Greenhouse Index (APGI). The framework decomposes reconstruction into successive 4×, 2×, and 1.5×-scale transformations and progressively recovers PG spatial arrangements and local boundaries through an RRDB generator and learnable upsampling operations. APGI values, directional gradients, and gradient magnitude are encoded as structural priors to modulate shallow, backbone, and upsampling features. Intermediate-scale supervision, interstage residual refinement, and tone consistency constraints are further introduced to improve the structural stability and spectral consistency of the reconstructed imagery. The model is trained using strictly co-registered Sentinel-2 and Jilin-1 image pairs, and its practical value is further evaluated through a downstream PG segmentation task. Spatially blocked five-fold cross-validation is used, with four folds for training and one fold for testing in each run, to prevent overlapping or adjacent patches from entering different subsets. Experiments conducted in Weifang, China, and Almería, Spain, demonstrate that progressive reconstruction and APGI guidance improve PG boundary continuity, reduce extraction errors, and enhance downstream segmentation accuracy. Accordingly, the framework is positioned as task-driven conditional image generation that recovers a recognition-relevant PG structure, while textures below the Sentinel-2 information limit remain learned inferences rather than direct observations.","精细尺度、大范围的塑料大棚（PG）制图通常依赖成本高昂且空间覆盖有限的高分辨率影像。尽管免费可用的Sentinel-2数据具有宽幅覆盖和高重访频率，但其空间分辨率不足限制了塑料大棚提取精度。从10 m或更粗分辨率影像直接超分辨率（SR）重建至亚米级高度不适定，因为塑料大棚结构必须从严重混合像元中推断。为应对这些挑战，我们提出了一种由空间增强型农业塑料大棚指数（APGI）引导的三阶段12×超分辨率框架。该框架将重建分解为连续的4×、2×和1.5×尺度变换，并通过RRDB生成器和可学习上采样操作逐步恢复塑料大棚空间布局和局部边界。APGI值、方向梯度和梯度幅值被编码为结构先验，以调制浅层特征、主干特征和上采样特征。进一步引入中间尺度监督、阶段间残差细化和色调一致性约束，以提高重建影像的结构稳定性和光谱一致性。该模型使用严格配准的Sentinel-2和吉林一号影像对进行训练，并通过下游塑料大棚分割任务进一步评估其实际价值。采用空间分块五折交叉验证，每次运行使用四折训练、一折测试，以防止重叠或相邻图块进入不同子集。在中国潍坊和西班牙阿尔梅里亚开展的实验表明，渐进式重建和APGI引导改善了塑料大棚边界连续性，减少了提取误差，并提升了下游分割精度。据此，该框架被定位为任务驱动的条件图像生成，即恢复与识别相关的塑料大棚结构，而低于Sentinel-2信息极限的纹理仍为学习推断而非直接观测。",null,"Remote Sensing","2026-09-10T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"提出APGI指数引导的三阶段12倍超分辨率框架，用免费Sentinel-2数据实现亚米级塑料大棚制图，方法新颖、跨区域验证可靠，对设施农业遥感监测有实用价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","设施农业","遥感","塑料大棚","图像超分辨率",0,"10.3390\u002Frs18183108",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":49,"direction":53,"ingested_from":55},"W7212170849",[36,39,42,45,47],{"name":37,"orcid":38},"Linze Bai","https:\u002F\u002Forcid.org\u002F0000-0002-1384-5630",{"name":40,"orcid":41},"Xiaocan Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1513-4103",{"name":43,"orcid":44},"Cheng Su","https:\u002F\u002Forcid.org\u002F0000-0002-3540-0309",{"name":46,"orcid":9},"Bangyan Qiu",{"name":48,"orcid":9},"Shuyang Zheng",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"提出APGI引导的三阶段12倍超分框架，从Sentinel-2影像重建塑料大棚结构并提升提取精度。","APGI指数引导三阶段超分，RRDB生成器，Sentinel-2与吉林一号配对训","渐进重建与APGI引导改善大棚边界连续性，降低提取误差，提升下游分割精度。","农业遥感与作物表型","可探索指数引导超分迁移至其他农业地物，并量化重建纹理的置信度与不确定性。","openalex","2026-09-11T23:30:29.918053Z"]