Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks（worldview油棕树检测）

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Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks（worldview油棕树检测）

不是目标检测也不是语义分割,两步CNN指的是,采集的数据是一堆点,以点为中心的65*65和17*17图像范围大小来判断这个点是否是油棕树.第一步就是判断65*65的范围是否为(油棕树植被群,其他植被/空地,不透水面/云),第二步判断17*17的范围是否为(油棕树,背景,其他植被/空地,不透水面/云).两步结果都是油棕树的话就认为这个点是油棕树. 预测时对整幅影像滑动窗口,有重叠,再对最后的点结果最小距离分析,融合对同一棵树的多个预测点. 训练数据17000个点,验证数据3000个点. 最后比较了…

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[解释] tree的两个bounding boxes 都要保留,因为交并比小于0.5:car 0.73保留:pedestrain 0.98保留:motorcycle 0.58保留.一共5个. [解释] 5个anchor box, 一个anchor box 对应(1+4+20)个标签,所以output volume 是 19*19*5*25…

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