Abstract

Hyperspectral image data provides new ideas and methods for the fine classification of wetland. Research on wetland spectral characteristics lays the foundation for wetland classification and improves classification accuracy. Based on “Zhuhai-1” hyperspectral image data, combined with the Pure Pixel Index (PPI), this paper extracted the average spectral curves of 11 typical ground objects in Poyang Lake wetland. Two mathematical transformation methods, namely, the continuum removal and the first derivative, were used to analyze the spectral characteristics of the ground objects before and after transformation, in combination with spectral characteristic parameters. The method of combining the mean distance of ground objects with the range of data error was used to select characteristic wavelengths before and after spectral transformation. Finally, the Mahalanobis distance method was used to verify the recognition effect. The results demonstrate that: (1) Spectral transformation can effectively amplify the differences between ground objects. The continuum removal led to an increase in the characteristic bands of four types of vegetation and the emergence of characteristic bands of four types of other ground objects. The first derivative method extended the characteristic bands of four vegetation from red-edge to near-infrared. (2) The method of combining the mean distance and the data error range can effectively screened out the spectral characteristic bands of typical ground objects in Poyang Lake wetland, resulting in hyperspectral dimensionality reduction. (3) The Mahalanobis distance method validates the recognition effect of the characteristic bands. In the screened characteristic bands, the Mahalanobis distance between different ground objects is larger than that between the same ground object, except between shoaly land and road. This study offers valuable insights into spectral recognition and classification of freshwater lake wetlands, and also demonstrates the potential of “Zhuhai-1” hyperspectral image data in wetland research.

摘要

高光谱影像数据为湿地精细分类提供了新的思路和方法。湿地光谱特征研究为湿地分类奠定基础，提高分类精度。基于“珠海一号”高光谱影像数据，结合纯像素指数（PPI），提取了鄱阳湖湿地11个典型地物的平均光谱曲线。采用连续统去除和一阶导数两种数学变换方法，结合光谱特征参数，分析变换前后地物的光谱特征。采用地物平均距离与数据误差范围相结合的方法选取光谱变换前后的特征波长。最后，采用马氏距离法验证识别效果。结果表明：（1）光谱变换可以有效放大地物之间的差异。连续去除导致四类植被特征带的增加和四类其他地物特征带的出现。一阶导数方法将四种植被的特征波段从红边扩展到近红外。(2)平均距离与数据误差范围相结合的方法可以有效筛选出鄱阳湖湿地典型地物的光谱特征波段，实现高光谱降维。(3)马氏距离方法验证了特征波段的识别效果。在筛选出的特征波段中，除浅滩和道路之间外，不同地物之间的马氏距离大于同一地物之间的马氏距离。该研究为淡水湖泊湿地的光谱识别和分类提供了宝贵的见解，也展示了“珠海一号”高光谱图像数据在湿地研究中的潜力。