Undercomplete learned dictionaries for land cover classification in multispectral imagery of Arctic landscapes using CoSA: clustering of sparse approximations
Undercomplete learned dictionaries for land cover classification in multispectral imagery of Arctic landscapes using CoSA: clustering of sparse approximations
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使用 CoSA 的北极景观多光谱图像中土地覆盖分类的不完整学习词典：稀疏近似的聚类
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
C. Gangodagamage
中科院分区：
文献类型：
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作者：
D. Moody;S. Brumby;J. Rowland;C. Gangodagamage
自动特征提取技术，包括受神经科学启发的机器视觉，对于支持全球气候变化科学和建模的景观表征和变化检测非常有意义。我们展示了使用最先进的自适应信号处理并结合压缩传感和机器学习技术，将机器视觉方法扩展到环境科学的持续努力的结果。我们使用赫布学习规则来构建适应数据的不完整光谱纹理字典。我们从数百万个重叠的多光谱图像块中学习字典，然后使用追踪搜索来生成分类特征。土地覆盖标签是使用我们的 CoSA 算法自动生成的：稀疏近似的无监督聚类。我们使用来自三个北极研究区域的多光谱 Worldview-2 数据演示了我们的方法：阿拉斯加巴罗；阿拉斯加塞拉维克河；以及加拿大西北部麦肯齐河三角洲附近的一个分水岭。我们的目标是开发一种强大的分类方法，允许根据植被、地表水文特性和地貌特征等属性将景观自动离散为不同的单元。为了解释土地覆盖类别并将其分配给聚类，我们评估聚类的光谱特性，并将聚类与景观属性的现场和遥感分类进行比较。我们的工作表明，基于神经科学的模型是解决遥感中实际模式识别问题的一种有前途的方法。
Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a Hebbian learning rule to build undercomplete spectral-textural dictionaries that are adapted to the data. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using our CoSA algorithm: unsupervised Clustering of Sparse Approximations. We demonstrate our method using multispectral Worldview-2 data from three Arctic study areas: Barrow, Alaska; the Selawik River, Alaska; and a watershed near the Mackenzie River delta in northwest Canada. Our goal is to develop a robust classification methodology that will allow for the automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and geomorphic characteristics. To interpret and assign land cover categories to the clusters we both evaluate the spectral properties of the clusters and compare the clusters to both field- and remote sensing-derived classifications of landscape attributes. Our work suggests that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing.
影响因子：
5.4
作者：
Blumensath, Thomas;Davies, Mike E.
通讯作者：
Davies, Mike E.