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首页> 外文期刊>International journal of image and data fusion >Mapping crop types in fragmented arable landscapes using AVIRIS-NG imagery and limited field data
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Mapping crop types in fragmented arable landscapes using AVIRIS-NG imagery and limited field data

机译：使用Aviris-NG Imagerery和有限的现场数据映射碎片化型景观中的裁剪类型

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The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of crop types. Recent advances in remote-sensing technologies and data mining approaches offer a viable solution to this mapping problem. We demonstrated the potential of using hyperspectral imaging and an ensemble classification approach that combines five machine-learning classifiers to map crop types in the Anand District of Gujarat, India. We derived a set of narrow/broad-band indices from the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery to represent spectral variations and identify target classes and their distribution patterns. The results showed that Maximum Entropy (MaxEnt) and Generalised Linear Model (GLM) had strong discriminatory image classification abilities with Area Under the Curve (AUC) values ranging between 0.75 and 0.93 for MaxEnt and between 0.73 and 0.92 for GLM. The ensemble model resulted in improved accuracy scores compared to individual models. We found the Photochemical Reflectance Index (PRI) and Moment Distance Ratio Right/Left (MDRRL) to be important predictors for target classes such as wheat, legumes, and eggplant. Results from the study revealed the potential of using one-class ensemble modelling approach and hyperspectral images with limited field dataset to map agricultural systems that are fragmented in nature.
机译：耕地的碎片性质和多样化的种植模式经常挫败作物类型的精确绘图。遥感技术和数据挖掘方法的最新进展为此映射问题提供了可行的解决方案。我们展示了使用高光谱成像的潜力和集合分类方法，这些方法结合了五种机器学习分类器来绘制印度古吉拉特邦Anand区的作物类型。我们从空中可见红外成像光谱仪 - 下一代（Aviris-NG）图像中派生了一组窄/宽带索引，以表示光谱变化并识别目标类别及其分发模式。结果表明，最大熵（MaxEnt）和广义线性模型（GLM）具有强大的歧视性图像分类能力，曲线下的面积（AUC）值范围为0.75和0.93，用于GLM的0.73和0.92。与各个模型相比，集合模型提高了精度分数。我们发现光化学反射率指数（PRI）和时刻距离比右/左（MDRRL）成为目标类别的重要预测因子，例如小麦，豆类和茄子。该研究的结果揭示了使用单级集合建模方法和高光谱图像的潜力，其中具有有限的现场数据集来映射自然分散的农业系统。

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