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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >EVALUATION OF ASTER SPECTRAL BANDS FOR AGRICULTURAL LAND COVER MAPPING USING PIXEL-BASED AND OBJECT-BASED CLASSIFICATION APPROACHES
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EVALUATION OF ASTER SPECTRAL BANDS FOR AGRICULTURAL LAND COVER MAPPING USING PIXEL-BASED AND OBJECT-BASED CLASSIFICATION APPROACHES

机译：基于像素和基于物体的分类方法的农业陆地覆盖映射Aster光谱带的评价

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The ASTER sensor onboard NASA's Earth observing satellite Terra is an optical remote sensor comprised of 14 spectral bands ranging from the visible to thermal infrared region. With its multispectral bands the sensor bears the potential to provide data for both a detailed land use classification of heterogeneously vegetated areas. This study evaluates the potential of ASTER data for land use classification in a typical Bangladesh agricultural environment with its small-spaced fields. Land use in Bangladesh typically consists of small agricultural fields, complex vegetation covers, and scatteredly distributed residential areas, which have been problematic in terms of land cover mapping using satellite remote sensing data due to the complexity of the spatial structure. A pixel-based approach and a multi-scale object-based method are applied for agricultural land use classification using ASTER data in Sylhet district, Bangladesh. The supervised classification was performed using the Maximum Likelihood Classifier (MLC) with ERDAS Imagine software. On the other hand, object-based image analysis was performed through the eCognition software. The results show, that the phenological stages of the cultivars are the main factors influencing the separability of agricultural classes. To identify the best method, accuracy of each method was assessed using reference data sets derived from high resolution satellite data and ground truth field investigation data. Outcome from the classification works show that the object-based approach gave more accurate results (including higher producer's and user's accuracy for most of the land cover classes) than those achieved by pixel-based classification algorithms.
机译：烟囱传感器在NASA的地球上观察卫星Terra是一个由由可见的热红外区域可见的14个光谱带组成的光远程传感器。利用其多光谱频带，传感器具有提供有关异构植被区域的详细土地使用分类的数据的可能性。本研究评估了典型孟加拉国农业环境中土地使用分类的艾斯特数据的潜力，其小间隔的田地。孟加拉国的土地用途通常由小型农业领域，复杂的植被封面和散布分布的住宅区组成，这在利用卫星遥感数据由于空间结构的复杂性而在陆地覆盖映射方面存在问题。基于像素的方法和基于多尺度对象的方法，用于使用孟加拉国Sylhet区的Aster数据进行农业用地使用分类。使用Erdas软件使用最大似然分类器（MLC）进行监督分类。另一方面，通过认知软件执行基于对象的图像分析。结果表明，品种的酚类阶段是影响农业课程可分离性的主要因素。为了确定最佳方法，使用从高分辨率卫星数据和地理实地调查数据的参考数据集进行评估每种方法的准确性。来自分类工作的结果表明，基于对象的方法提供了比基于像素的分类算法所实现的大多数土地覆盖类别的更准确的结果（包括更高的生产者和用户的准确性）。

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