Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales
Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales
复制标题
DOI：
10.1080/01431161.2016.1278314
复制
发表时间：
2017-01-01
影响因子：
3.4
通讯作者：
Hall-Beyer, Mryka
中科院分区：
文献类型：
--
作者：
Hall-Beyer, Mryka
纹理测量定量地描述了相邻像素的DN值之间的关系。输出是可用于进一步处理的空间信息的连续度量。空间关系不一定与给定类别的光谱数据相关，包含它们的度量可以提高分类的准确性。本研究开发了一种选择Haralick（灰度共生矩阵[GLCM]）纹理度量集的指导方针。这些准则是使用各种土地覆盖和空间尺度（窗口大小）得出的。对3幅Landsat TM和ETM+影像：中纬度农业和自然植被场景、冰川-岩石-海冰场景和沙丘和结构复杂岩石的沙漠场景，进行了8个GLCM测度的主成分分析（PCA）。分别对边长为25,13和5像素的正方形组成的邻域进行主成分分析，以证明其对不同空间尺度的鲁棒性。PCA加载结果表明，对比（Con）、差异（dissimilarity）、熵（Ent）和GLCM方差与地表覆盖斑块的视觉边缘最相关；均匀性、GLCM均值、GLCM相关性（GLCM Cor）和角秒矩与斑块内部相关。边缘高亮纹理解释了大多数数据集方差，但无法区分类别。突出斑块内部特征的特征通道依赖于GLCM均值，在某种程度上依赖于GLCM Cor。这两种纹理确实有助于区分单个类签名以用于分类目的。Ent不一致地出现在边缘或内部分组中。Ent被解释为对特定类的纹理很重要，但哪些类不能从一个场景推广到另一个场景。Con可以有效地勾勒出斑块边缘，并可用于基于地理目标的图像分析（GEOBIA）中的目标生成。出于分类目的，建议的指导方针是选择Mean，并且，当一个类补丁可能包含边缘特征时，Con. Cor是在这些情况下Mean的替代方案，同样可以使用Dis来代替con。对于更详细的纹理研究，请添加Ent。本指南不构成完整的纹理分析，但可能允许有信心地使用GLCM纹理来提高基于landsat的分类效率。
Texture measurements quantitatively describe relationships of DN values of neighbouring pixels. The output is a continuous measure of spatial information that may be used for further processing. Spatial relationships are not necessarily correlated with spectral data for a given class, and including a measure of them improves classification accuracy. This research develops a guideline for choosing among the Haralick (Grey Level Co-occurrence Matrix [GLCM]) set of texture measures. These guidelines are derived using a variety of land covers and spatial scales (window sizes).Principal component analysis (PCA) of eight GLCM measures was performed for three Landsat TM and ETM+ images: a mid-latitude agricultural and natural vegetation scene, a glacier-rock-sea ice scene, and a desert scene with dunes and structurally complex rocks. PCA was performed separately for neighbourhoods consisting of squares with 25, 13, and 5 pixels on a side to demonstrate robustness to different spatial scales. PCA loadings show that contrast (Con), dissimilarity, entropy (Ent), and GLCM variance are most commonly associated with visual edges of land-cover patches; homogeneity, GLCM mean, GLCM correlation (GLCM Cor), and angular second moment are associated with patch interiors. Edge-highlighting textures account for most dataset variance but fail to differentiate among classes. Eigenchannels highlighting patch interior characteristics rely on GLCM mean and to some extent GLCM Cor. These two textures do contribute to distinguishing individual class signatures for classification purposes. Ent does not appear consistently in edge or interior groupings. Ent is interpreted as important to the textures of particular classes, but which classes is not generalized from one scene to another. Con is effective for outlining patch edges and may serve for object formation in geographic object-based image analysis (GEOBIA).For classification purposes, the proposed guideline is a choose Mean and, where a class patch is likely to contain edge-like features within it, Con. Cor is an alternative for Mean in these situations, Dis may similarly be used in place of Con. For more detailed texture study, add Ent. This guideline does not constitute a complete texture analysis but may allow confident use of GLCM texture to enhance the efficiency of Landsat-based classification.