A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas
A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas
复制标题
城市地区高分辨率遥感图像分类的多指标学习方法
DOI：
10.1016/j.isprsjprs.2014.01.008
复制
发表时间：
2014-04-01
影响因子：
12.7
通讯作者：
Zhang, Liangpei
中科院分区：
文献类型：
--
作者：
Huang, Xin;Lu, Qikai;Zhang, Liangpei
近年来，人们普遍认为，由纹理、结构和基于对象的方法得出的空间特征是重要的信息源，可补充光谱特性，用于高分辨率影像的准确城市分类。然而，空间特征总是涉及一系列参数，如尺度、方向和统计量度，从而导致高维特征空间。考虑到处理高分辨率影像时巨大的存储和计算成本，高维空间几乎不具有可操作性。为此，我们提出一种新的多指标学习（MIL）方法，其中使用一组低维信息指标来表示高分辨率影像中复杂的地理空间场景。具体而言，研究中提出了两类指标：（1）原始指标（PI）：使用一组自动快速计算的基元（如建筑物/阴影/植被）来表示高分辨率城市场景；（2）变化指标（VI）：基于三维小波变换提出了几个光谱和空间变化指标，以描述联合光谱 - 空间域中的局部变化。通过这种方式，城市景观可以分解为一组低维且具有语义的指标，取代高维但低级的特征（如纹理）。然后通过多核支持向量机对信息指标进行学习。使用包括GeoEye - 1、QuickBird、WorldView - 2和ZY - 3在内的各种高分辨率影像对所提出的MIL方法进行评估，并与最先进的影像分类算法（如基于对象的分析以及基于纹理和形态特征的光谱 - 空间方法）进行详细比较。结果表明，MIL方法能够在低维特征空间中取得有前景的结果，并为处理大规模高分辨率影像提供一种实用策略。（C）2014国际摄影测量与遥感学会。由爱思唯尔出版有限公司出版。保留所有权利。
In recent years, it has been widely agreed that spatial features derived from textural, structural, and object-based methods are important information sources to complement spectral properties for accurate urban classification of high-resolution imagery. However, the spatial features always refer to a series of parameters, such as scales, directions, and statistical measures, leading to high-dimensional feature space. The high-dimensional space is almost impractical to deal with considering the huge storage and computational cost while processing high-resolution images. To this aim, we propose a novel multi-index learning (MIL) method, where a set of low-dimensional information indices is used to represent the complex geospatial scenes in high-resolution images. Specifically, two categories of indices are proposed in the study: (1) Primitive indices (PI): High-resolution urban scenes are represented using a group of primitives (e.g., building/shadow/vegetation) that are calculated automatically and rapidly; (2) Variation indices (VI): A couple of spectral and spatial variation indices are proposed based on the 3D wavelet transformation in order to describe the local variation in the joint spectral-spatial domains. In this way, urban landscapes can be decomposed into a set of low-dimensional and semantic indices replacing the high-dimensional but low-level features (e.g., textures). The information indices are then learned via the multi-kernel support vector machines. The proposed MIL method is evaluated using various high-resolution images including GeoEye-1, QuickBird, WorldView-2, and ZY-3, as well as an elaborate comparison to the state-of-the-art image classification algorithms such as object-based analysis, and spectral-spatial approaches based on textural and morphological features. It is revealed that the MIL method is able to achieve promising results with a low-dimensional feature space, and, provide a practical strategy for processing large-scale high-resolution images. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.