Hyperspectral Image Classification of Agricultural Tillage Practices Using Spatial-aware Collaborative Representation
Hyperspectral Image Classification of Agricultural Tillage Practices Using Spatial-aware Collaborative Representation
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DOI：
10.1109/agro-geoinformatics59224.2023.10233552
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Chiranjibi Shah;Q. Du
中科院分区：
文献类型：
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作者：
Chiranjibi Shah;Q. Du
由于土壤和衰老作物残留物之间的光谱相似性，根据高光谱图像对保护性耕作实践进行分类具有挑战性。同时利用光谱和空间信息可以大大提高分类精度。在本文中，我们研究了耕作测绘中的协作表示分类器，因为它们对于大规模图像分类具有高效的性能，这是因为此类分类器不需要训练阶段。为了更好地利用高光谱图像中除了光谱信息之外的空间信息，我们关注空间感知的协作表示分类器，它可以通过在目标函数中添加空间正则化项来在分类过程中直接合并空间信息。实验结果表明，与其他类型的分类器相比，其准确性更高，计算成本更低。
Classification of conservation tillage practices from hyperspectral imagery is challenging due to spectral similarity between soils and senescent crop residues. Using both spectral and spatial information can greatly improve the classification accuracy. In this paper, we investigate the collaborative representation classifiers in tillage mapping due to their highly efficient performance for large-scale image classification, which is because such classifiers do not require a training phase. To better utilize the spatial information in addition to the spectral information in a hyperspectral image, we focus on a spatial-aware collaborative representation classifier, which can directly incorporate the spatial information during classification by adding a spatial regularization term to the objective function. Experimental results demonstrate its higher accuracy and lower computational cost compared to other types of classifiers.