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首页> 外文期刊>International journal of image and data fusion >Multi-temporal satellite imagery and data fusion for improved land cover information extraction
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Multi-temporal satellite imagery and data fusion for improved land cover information extraction

机译：多时相卫星图像和数据融合以改善土地覆盖物信息提取

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Land use information is one of the most sought inputs for various resource and environmental management studies as well as climate models. In this study, an attempt was made to obtain land cover information from temporal data set of Advance Wide Field Sensor aboard Indian Remote Sensing satellite IRS-P6 using data mining classification technique. This study mainly focuses on the utility of visually interpreted thematic maps as an additional input for improving classification accuracies. The temporal data sets were co-registered to sub-pixel accuracy and were atmospherically corrected using modified dark pixel subtraction method. The visual thematic maps (wastelands and forest cover maps) were also co-registered to satellite data to a near pixel accuracy. Digital values were extracted for various classes and rule sets were generated using See-5 data mining software. These rule sets were ported into ERDAS Imagine Knowledge Engineer and the temporal data sets were classified. The results indicate that temporal satellite data at monthly intervals have a good potential to capture the land cover information including temporal dynamics of crop cover in agricultural lands. Addition of legacy maps obtained through monoscopic visual interpretation has helped to improve classification accuracies significantly. However, there exists a co-registration issue between visual maps as well as satellite data that have influenced the classification accuracies. The decision tree classification algorithm (See-5) used in this study is able to exploit the temporal variation in target spectral properties as well as thematic information from legacy maps satisfactorily. There has been a substantial improvement in various categories of forests as well as wastelands due to addition of visual maps. This has further reduced the misclassification of other vegetation categories, thereby improving the overall classification accuracy. Overall, kappa statistic of 0.885 was achieved with multitemporal satellite data, which was further improved to 0.932 with the addition of visual maps.
机译：土地使用信息是各种资源和环境管理研究以及气候模型最需要的输入之一。在这项研究中，尝试使用数据挖掘分类技术从印度遥感卫星IRS-P6上的Advance Wide Field Sensor的时间数据集中获取土地覆盖信息。这项研究主要侧重于将视觉解释的专题图作为改进分类准确性的附加输入。将时间数据集共注册到子像素精度，并使用改进的暗像素减法对它们进行大气校正。视觉专题图（荒地和森林覆盖图）也被共同注册到卫星数据中，精度接近像素。提取各种类别的数字值，并使用See-5数据挖掘软件生成规则集。这些规则集已移植到ERDAS Imagine Knowledge Engineer中，并对时态数据集进行了分类。结果表明，按月间隔的时空卫星数据具有捕获包括农业土地上作物覆盖时空动态在内的土地覆盖信息的良好潜力。通过单目视觉解释获得的遗留地图的添加已帮助显着提高了分类准确性。但是，视觉地图与影响分类精度的卫星数据之间存在共注册问题。本研究中使用的决策树分类算法（See-5）能够令人满意地利用目标光谱特性的时空变化以及来自传统地图的主题信息。由于添加了可视化地图，各种类别的森林和荒地有了显着改善。这进一步减少了其他植被类别的错误分类，从而提高了整体分类的准确性。总体而言，多时相卫星数据的kappa统计量达到0.885，通过添加视觉地图，kappa统计量进一步提高到0.932。

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