Works matching IS 10074619 AND DT 2024 AND VI 28 AND IP 9
Results: 20
简缩极化 SAR 数据支持的森林地上生物量反演.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2416, doi. 10.11834/jrs.20232363
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面向小麦种植面积反演的自监督学习光谱解混算法.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2405, doi. 10.11834/jrs.20222315
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多源植被遥感产品应用于干旱监测的不确定性分析.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2383, doi. 10.11834/jrs.20232388
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顾及左右摆扫不同影响的航空大视场高分 红外扫描仪几何外检校方法.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2372, doi. 10.11834/jrs.20242312
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基于多尺度注意力机制的 PolSAR 深度学习 超分辨率模型.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2362, doi. 10.11834/jrs.20233002
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基于 TROPOMI NO<sub>2</sub>、CO 及 HCHO 重构数据的 近地面 O<sub>3</sub> 浓度估算研究.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2348, doi. 10.11834/jrs.20233068
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联合超像素分割和显著性特征的 SAR 海洋内波检测.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2335, doi. 10.11834/jrs.20232433
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CARE 产品：云微物理和短波辐射强迫算法及应用.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2320, doi. 10.11834/jrs.20232450
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基于哨兵主被动遥感的茅尾海潮滩地表形变监测 与分析.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2306, doi. 10.11834/jrs.20232468
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改进的 DeepLabV3+模型用于震后高分遥感影像 滑坡识别.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2293, doi. 10.11834/jrs.20243393
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基于 AHP-EWM 多目标决策模型的 北京应急避难场所承载力评价.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2276, doi. 10.11834/jrs.20244025
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2021 年四川泸县 Ms6.0 地震临震长波辐射变化检测.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2265, doi. 10.11834/jrs.20232267
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基于遥感反演雪表粒径变化的山区高分辨率 融雪探测.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2252, doi. 10.11834/jrs.20243465
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利用信号强度反演多模多频 GNSS-MR 雪深.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2240, doi. 10.11834/jrs.20233041
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MODIS 亚洲高山区积雪面积比例制图.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2223, doi. 10.11834/jrs.20242483
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机载多维度 SAR 图像地物数据集构建及 融合分类方法.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2209, doi. 10.11834/jrs.20242276
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基于星地传感技术的土壤盐渍化监测进展与展望.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2187, doi. 10.11834/jrs.20233164
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基于深度学习的自监督单目动态场景深度估计综述.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2170, doi. 10.11834/jrs.20233060
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水稻遥感制图研究综述.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2144, doi. 10.11834/jrs.20233014
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植被物候遥感监测关键问题.
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- Journal of Remote Sensing, 2024, v. 28, n. 9, p. 2131, doi. 10.11834/jrs.20233088
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