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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Toward Automated Land Cover Classification in Landsat Images Using Spectral Slopes at Different Bands
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Toward Automated Land Cover Classification in Landsat Images Using Spectral Slopes at Different Bands

机译：使用不同波段的光谱坡度对Landsat图像中的自动土地覆盖分类进行分类

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摘要

For last two decades, various techniques have been advanced for classification of satellite imageries. A vast majority of them are supervised/semisupervised requiring manual selection of samples for each class. Some of the unsupervised approaches are based on hard thresholds and particular to image acquisition modules. In this study, we propose a spectral-slope-based classification technique and subsequently summarize the changes in temporal image sets. Using the properties of spectral slopes, we propose a set of rules for selection of training samples from Landsat imageries for classifying the land cover. The images are initially classified into three classes: water, vegetation, and vegetation-void. Further, vegetation and vegetation-void regions are classified into proper vegetation and dry cropland, and urban land and bare land, respectively. The initial classification is performed by support vector machines, and the second-level classification is performed through k-means clustering and subsequent labeling of clusters to subclasses. Considering temporal images of the same scene, postclassification change summarization is carried out to quantize the land variations, both qualitatively (type of change) and quantitatively (volume of change).The approach has been used in the analysis of images acquired by different sensors operating under similar wavelength ranges.
机译：在最近的二十年中，已经对卫星图像的分类提出了各种技术。他们中的绝大多数是受监督/半监督的，需要为每个类别手动选择样本。一些无监督的方法基于硬阈值，尤其是图像采集模块。在这项研究中，我们提出了一种基于频谱斜率的分类技术，随后总结了时间图像集的变化。利用频谱斜率的属性，我们提出了一套规则，用于从Landsat影像中选择训练样本以对土地覆盖进行分类。图像最初分为三类：水，植被和无植被。此外，植被和无植被地区分别被划分为适当的植被和旱地，城市土地和裸地。初始分类由支持向量机执行，第二级分类通过k均值聚类以及随后将聚类标记为子类进行。考虑到同一场景的时间图像，进行了后分类变化汇总，以定性（变化类型）和定量（变化量）量化土地变化。该方法已用于分析由不同传感器操作获得的图像在相似的波长范围内。

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