Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects.

纹理是遥感图像中物体识别的关键空间特征。目前，大多数纹理特征提取方法主要关注纹理基元（基本纹理单元）的重复模式，而很少考虑它们的空间排列。虽然有些方法可以在一定程度上捕捉纹理图元的空间排列，但其原理和算法复杂且难以实现。在这项研究中，我们提出了一种新的统计特征提取方法，称为距离编码多样性（DCD），它可以测量具有旋转不变特性的纹理图元的空间排列。 DCD 提取的纹理特征在三个不同尺度的大型数据集上进行了广泛测试：像素邻域尺度的 50 个数字矩阵样本、图像对象尺度的 2508 对受损建筑物前后的小块图像以及五个在区域景观尺度上覆盖不同主要土地覆盖类型（农田、建筑物、种植园、自然植被和废弃垃圾场）的大比例尺图像，并将测试结果与灰度共生矩阵熵（GLCM）和Short进行比较灰度游程长度矩阵 (GLRLM) 的游程强调 (SRE)。 DCD可以有效测量纹理图元的空间排列，其对空间排列的敏感度为98%，是SRE（敏感度值为18%）的5倍以上。基于DCD对不同土地覆盖类型的识别精度比Entropy提高了12.31%，比SRE提高了14.40%。由于算法简单、性能优越，DCD可广泛应用于物体识别和土地覆盖/用途分类，具有广阔的应用前景。