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Exploiting High Resolution Multi-Seasonal Textural Measures and Spectral Information for Reedbed Mapping

机译：利用高分辨率的多季节纹理测度和光谱信息进行边坡制图

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Reedbeds across the UK are amongst the most important habitats for rare and endangered birds, wildlife and organisms. However, over the past century, this valued wetland habitat has experienced a drastic reduction in quality and spatial coverage due to pressures from human related activities. To this end, conservation organisations across the UK have been charged with the task of conserving and expanding this threatened habitat. With this backdrop, the study aimed to develop a methodology for accurate reedbed mapping through the combined use of multi-seasonal texture measures and spectral information contained in high resolution QuickBird satellite imagery. The key objectives were to determine the most effective single-date (autumn or summer) and multi-seasonal QuickBird imagery suitable for reedbed mapping over the study area; to evaluate the effectiveness of combining multi-seasonal texture measures and spectral information for reedbed mapping using a variety of combinations; and to evaluate the most suitable classification technique for reedbed mapping from three selected classification techniques, namely maximum likelihood classifier, spectral angular mapper and artificial neural network. Using two selected grey-level co-occurrence textural measures (entropy and angular second moment), a series of experiments were conducted using varied combinations of single-date and multi-seasonal QuickBird imagery. Overall, the results indicate the multi-seasonal pansharpened multispectral bands (eight layers) combined with all eight grey level co-occurrence matrix texture measures (entropy and angular second moment computed using windows 3 × 3 and 7 × 7) produced the optimal reedbed (76.5%) and overall classification (78.1%) accuracies using the maximum likelihood classifier technique. Using the optimal 16 layer multi-seasonal pansharpened multispectral and texture combined image dataset, a total reedbed area of 9.8 hectares was successfully mapped over the three study sites. In conclusion, the study has demonstrated the value of utilizing multi-seasonal texture measures and pansharpened multispectral data for reedbed mapping.
机译：英国的芦苇床是珍稀濒危鸟类，野生动植物和生物最重要的栖息地之一。然而，在过去的一个世纪中，由于人类活动的压力，这种珍贵的湿地栖息地的质量和空间覆盖率急剧下降。为此，英国各地的保护组织都承担了保护和扩大这一受威胁栖息地的任务。在此背景下，这项研究旨在通过结合使用多个季节的纹理量度和高分辨率QuickBird卫星影像中包含的光谱信息来开发一种精确的芦苇制图方法。关键目标是确定最有效的单日期（秋季或夏季）和多季节的QuickBird影像，以适合研究区域的芦苇制图;评价使用多种组合将多季节纹理量度和光谱信息结合起来用于芦苇制图的有效性;并从三种选择的分类技术（即最大似然分类器，频谱角度映射器和人工神经网络）中评估最适合芦苇映射的分类技术。使用两个选定的灰度共现纹理度量（熵和第二角矩），使用单日期和多季节的QuickBird影像的各种组合进行了一系列实验。总体而言，结果表明，多季节全锐化多光谱带（八层）与所有八个灰度共生矩阵纹理度量（使用窗口3×3和7×7计算的熵和角秒矩）相结合，产生了最佳的芦苇床（使用最大似然分类器技术的准确度为76.5％），总体准确度为78.1％。使用最佳的16层多季节全锐化多光谱和纹理组合图像数据集，在三个研究地点成功绘制了9.8公顷的芦苇总面积。总而言之，该研究表明了利用多季节纹理度量和锐化多光谱数据进行边坡贴图的价值。

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