Abstract. Drawing a spectrally homogeneous region of interest in a remotely sensed image is a common task for an image analyst when performing, for instance, atmospheric correction or end-member selection. Manually selecting a homogeneous sample of pixels can be tedious and error prone due to the limits of human perception and data visualization. I present a region shrinkage method that automates the extraction of a spectrally homogeneous and spatially contiguous region from a user selected seed pixel. The proposed technique combines divisive clustering, connected component analysis, and image noise estimation to generate a series of candidate regions of increasingly smaller size until they converge to the seed pixel through similarity space. From these candidate regions, an optimal one is identified that is spectrally homogeneous, spatially contiguous, and as large as possible. Experimental results demonstrate that the proposed method achieved detection rates of up to 95%, false alarm rates below 1%, and was robust to the main user input, the seed pixel location.

中文翻译：

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通过种子区域收缩自动提取同质区域

摘要。在遥感图像中绘制光谱均匀的感兴趣区域是图像分析人员在执行例如大气校正或终端成员选择时的一项常见任务。由于人类感知和数据可视化的限制，手动选择像素的同质样本可能很乏味且容易出错。我提出了一种区域收缩方法，可以自动从用户选择的种子像素中提取光谱均匀和空间连续的区域。所提出的技术结合分裂聚类、连通分量分析和图像噪声估计来生成一系列尺寸越来越小的候选区域，直到它们通过相似空间收敛到种子像素。从这些候选区域中，识别出光谱均匀的最佳区域，空间连续，并且尽可能大。实验结果表明，所提出的方法实现了高达 95% 的检测率，低于 1% 的误报率，并且对主要用户输入，即种子像素位置具有鲁棒性。