基于形态变换与空间逻辑聚合的高光谱森林树种分类

Classification of hyperspectral forest tree species based on morphological transform and spatial logical integration

基于机载及卫星平台面向地面实施反射率光谱信息采集,高光谱图像可捕获数十个甚至数百个相邻窄带,为土地利用提供丰富的判别性信息。因此,同可见光及多光谱图像相比,高光谱图像可以揭示更为精细的光谱特性,有助于实现更为准确的材质及地类识别。然而,现有分析方法大多过度关注其光谱特性,忽略了高光谱输入源的形态及空间性信息利用。在复杂对象分类任务中,针对细粒度类别(如森林树种)的类边界挖掘,形态结构差异性的捕获是极为重要的。本文分析了形态结构利用的重要性,设计了不同类型的特征提取器。在此基础上,针对细粒度树种分类提出了一种由粗到细的空间信息聚合网络MS-NET。本文方法将形态学算子与可训练的结构元素有效结合,有助于获取输入数据的特异性形态表征,提升最终分类精度。将本文方法在两组树种分类数据集中开展分类效果验证,相关结果表明本文方法相较其他类型分类器具有更好的性能。

By recording reflectance spectral information of the ground on an aircraft or satellite platform,hyperspectral imagery(HSI),occupying dozens of or even hundreds of contiguous narrow bands,possesses abundant discriminative information for land use.Compared with visible light images and multispectral images,HSI can reveal subtle spectral characteristics,which contribute to a more accurate identification of the materials and classes of land covers.However,most existing methods overly focus on spectral knowledge while neglecting the potential morphological and spatial information within the hyperspectral input.In the classification of complex objects,the capture of morphological differences is much more necessary for searching out the class boundaries of fine-grained classes,e.g.,forestry tree species.In this paper,the importance of morphological structure utilization is analyzed,and different feature extractors are designed.Specifically,focusing on fine-grained traits extraction,we propose a coarse-to-fine spatial information integration network,called MS-NET(morphological and spatial information based network),for tree species classification.The morphological operators are effectively embedded with the trainable structuring elements,which contributes to acquiring distinctive morphology representations,enhancing the classification accuracy.We evaluate the classification performance of the proposed method on two tree species datasets,and the results demonstrate that the proposed method provides superior performance when compared with other state-of-the-art classifiers.