融合光谱混合分解与面向对象的土地利用/覆被分类

错综复杂的土地利用模式和破碎的地物斑块制约了土地利用/覆被分类的精度和效率。一方面，混合像元模糊了地物的光谱信息，影响了分类精度。另一方面，如何高效利用地物的光谱、形状和纹理特征是当前土地利用/覆被分类的研究热点。为了提高基于遥感技术的土地利用/覆被分类精度，该研究基于Sentinel-2A遥感影像，开展融合光谱混合分解与面向对象的土地利用/覆被分类研究。首先，基于地物的光谱、形状和纹理特征，在3个分割尺度通过NDWI（Normalized Difference Water Index)、NDVI（Normalized Difference Vegetation Index）、SBL（Soil Background Level）等8个特征参数构建了不同地物信息的提取规则。其次，利用光谱混合分解模型提取研究区基质（SL；岩石和土壤）、植被（GV；光合作用叶片）和暗色物质（DA；阴影和水）3类通用端元。最后，尝试融合3端元光谱特征优化地物信息提取规则。研究结果表明：1）基于构建的光谱、形状和纹理的地物信息提取规则，使用模糊函数、阈值法进行土地利用/覆被分类，获得了较高的分类精度，总体精度为80.83%，Kappa系数为0.76。2）融合3端元的光谱特征的提取规则将分类精度提升至90.00%，Kappa系数提升至0.88。3）具有明确物理意义的3端元的融入增强了像元内各组分信息的差异性，弥补了传统光谱指数对植被与土壤间的亮度信息解析度不足的缺陷。该方法能充分利用影像的光谱信息，是一种由易到难、对不确定因素进行逐层剥离的土地利用/覆被信息提取技术。因此，对中高分辨率的多光谱遥感影像十分友好，在土地利用/覆被的精细化分类中有较大应用潜力。

Land use/cover classification based on combining spectral mixture analysis model and object-oriented method

Complex land use/cover and fragmented land objects have posed a great restriction on the efficiency and accuracy of classification. In traditional classification, a single pixel was often taken as the basic unit, inevitably leading to the low accuracy of mixed pixel. Thus, the low classification accuracy of land use/cover can be attributed that the mixed image pixels blur the spectral information of land objects. Meanwhile, it is necessary to efficiently utilize the spectral, shape and texture characteristics of land objects during extraction. In object-oriented model, the adjacent pixels are taken as the objects considering various attributes, such as spectrum, shape and texture, in order to weaken the interference of mix pixels to land use information extraction. However, a large number of feature parameters in the object information extraction can reduce the computational efficiency and classification accuracy. As a result, it is highly demanding for the combined technology to realize the automatic and high-precision land use/cover classification using remote sensing images. In this study, a land use/cover extraction was carried out to integrate the spectral mixture analysis and object-oriented model using the Sentinel-2A images, in order to improve the accuracy of land use/cover classification. Firstly, the rules for land object extraction were constructed by 8 characteristic parameters, such as normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and soil background level (SBL) at three optimal segmentations, according to the spectral, shape and texture of land objects. Secondly, the spectral mixing was utilized to extract the three generic endmembers in the study area, including substrate (SU, rock and soil), green vegetation (GV, photosynthetic leaves), and dark material (DA, shadow and water). Finally, an illustration was presented for the effects of spectral features of three endmembers on the optimization of extraction. The results showed that: 1) The overall accuracy of land use/cover classification was 80.83% for five land objects using the fuzzy function and threshold in different hierarchical levels, where the Kappa coefficient was 0.76. 2) The spectral extraction significantly improved the overall accuracy of land use/cover classification up to 90.00% using the fusion of three endmembers derived from spectral mixture, where the Kappa coefficient was up to 0.88. 3) The integration of three endmembers with clear physical meaning enhanced the difference of each component in the pixel, especially in cultivated land and construction land. Correspondingly, the deficiency was reduced for traditional spectral indexes in the resolution between vegetation and soil brightness, due mainly to the explicit physical meaning of three endmembers. Besides, this model was conducted from easy to difficult, thereby to decrease uncertain factors layer by layer. Thus, it is also expected to make full use of spectral features, suitable for the medium and high resolution of remote sensing images with multiple spectral bands. The finding can provide a great potential to the fine extraction for land use information.