Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses
Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses
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DOI：
10.1016/j.isprsjprs.2008.03.003
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发表时间：
2008-11-01
影响因子：
12.7
通讯作者：
Aguilar, Manuel A.
中科院分区：
文献类型：
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作者：
Aguera, Francisco;Aguilar, Fernando J.;Aguilar, Manuel A.
近年来，塑料大棚的占地面积迅速增长，目前全球已超过50万公顷。由于需要大量的投入（水、化肥、燃料等），并产生不同的农业废物（蔬菜、塑料、化学品等），如果没有健全和可持续的土地规划，这种生产系统对环境的影响可能会很严重。为此，提供非常高分辨率图像的新一代卫星（例如 QuickBird 和 IKONOS）可能很有用。在这项研究中，使用了一张 QuickBird 和一张 IKONOS 卫星图像来覆盖类似情况下的同一区域。这项工作的目的是在土地覆盖检测中对 QuickBird 与 IKONOS 图像进行详尽的比较。在塑料温室测绘方面，设计并实施了比较测试，每个测试都有不同的目标。首先，使用结合 R、G、B、NIR 和全色波段的五种不同方法应用最大似然分类 (MLC)。所使用的频段组合显着影响了本工作中用于分类质量的一些指标。此外，QuickBird 图像的质量分类在所有情况下都高于 IKONOS 图像的质量分类。其次，将来自不同窗口大小和不同灰度级的全色图像的纹理特征作为第五波段添加到R、G、B、NIR图像中以进行MLC。在分类中包含纹理信息并没有提高分类质量。对于具有纹理信息的分类，在平均和角度二阶矩纹理参数的图像中都发现了最佳精度。这些纹理参数中的最佳窗口大小对于 IK 图像为 3 x 3，而对于 QB 图像则取决于所研究的质量指标，但最佳窗口大小约为 15 x 15。对于灰度级，最佳为 128。因此，最佳纹理参数取决于图像分类的主要目标。如果主要分类目标是最小化错误分类的像素数量，则应使用平均纹理参数，而如果主要分类目标是最小化未分类像素，则应使用角度二阶矩纹理参数。总体而言，QuickBird 和 IKONOS 图像在塑料温室分类方面都提供了有希望的结果。 (C) 2008 年国际摄影测量和遥感协会 (ISPRS)。由 Elsevier B.V. 出版。保留所有权利。
The area occupied by plastic-covered greenhouses has undergone rapid growth in recent years, currently exceeding 500,000 ha worldwide. Due to the vast amount of input (water, fertilisers, fuel, etc.) required, and output of different agricultural wastes (vegetable, plastic, chemical, etc.), the environmental impact of this type of production system can be serious if not accompanied by sound and sustainable territorial planning. For this, the new generation of satellites which provide very high resolution imagery, such as QuickBird and IKONOS can be useful. In this study, one QuickBird and one IKONOS satellite image have been used to cover the same area under similar circumstances. The aim of this work was an exhaustive comparison of QuickBird vs. IKONOS images in land-cover detection. In terms of plastic greenhouse mapping, comparative tests were designed and implemented, each with separate objectives. Firstly, the Maximum Likelihood Classification (MLC) was applied using five different approaches combining R, G, B, NIR, and panchromatic bands. The combinations of the bands used, significantly influenced some of the indexes used to classify quality in this work. Furthermore, the quality classification of the QuickBird image was higher in all cases than that of the IKONOS image. Secondly, texture features derived from the panchromatic images at different window sizes and with different grey levels were added as a fifth band to the R, G, B, NIR images to carry out the MLC. The inclusion of texture information in the classification did not improve the classification quality. For classifications with texture information, the best accuracies were found in both images for mean and angular second moment texture parameters. The optimum window size in these texture parameters was 3 x 3 for IK images, while for QB images it depended on the quality index studied, but the optimum window size was around 15 x 15. With regard to the grey level, the optimum was 128. Thus, the optimum texture parameter depended on the main objective of the image classification. If the main classification goal is to minimize the number of pixels wrongly classified, the mean texture parameter should be used, whereas if the main classification goal is to minimize the unclassified pixels the angular second moment texture parameter should be used. On the whole, both QuickBird and IKONOS images offered promising results in classifying plastic greenhouses. (C) 2008 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.