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首页> 外文OA文献 >LAND USE AND LAND COVER CLASSIFICATION OF MULTISPECTRAL LANDSAT-8 SATELLITE IMAGERY USING DISCRETE WAVELET TRANSFORM
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LAND USE AND LAND COVER CLASSIFICATION OF MULTISPECTRAL LANDSAT-8 SATELLITE IMAGERY USING DISCRETE WAVELET TRANSFORM

机译：利用离散小波变换的多光谱LANDSAT-8卫星图像的土地利用和土地覆盖分类

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摘要

Land use and land cover (LULC) classification of satellite imagery is an important research area and studied exclusively in remote sensing. However, accurate and appropriate land use/cover detection is still a challenge. This paper presents a wavelet transform based LULC classification using Landsat 8-OLI data. The study area for the present work is a small part of Varanasi district, Uttar Pradesh, India. The atmospheric correction of the image was performed using Quick Atmospheric Correction (QUAC) method. The image was decomposed into its approximation and detail coefficients up to eight levels using discrete wavelet transform (DWT) method. The approximation images were layer stacked with the original image. The minimum distance classifier was used for classifying the image into six LULC classes namely water, agriculture, urban, fallow land, sand, and vegetation. The classification accuracy for all decomposition levels was compared with that of classified product based on original multispectral image. The classification accuracy for multi-spectral image was found to be 75.27%. Whereas, the classification accuracies were found to improve up to 85.97%, 88.87%, 93.47%, 95.03%, 93.01, 92.32% and 90.80% for second, third, fourth, fifth, six, seventh and eight level decomposition, respectively. The significantly improved accuracy was found for images decomposed at level five. Thus, the approach of DWT for LULC classification can be used to increase the classification accuracy significantly.
机译：卫星图像的土地利用和陆地覆盖（LULC）分类是一个重要的研究区域，专门研究遥感。但是，准确和适当的土地使用/覆盖检测仍然是一个挑战。本文介绍了使用Landsat 8-Oli数据的基于小波变换的LULC分类。目前工作的研究区是瓦拉纳西区的一小部分，印度北方邦。使用快速大气校正（QUAC）方法进行图像的大气校正。使用离散小波变换（DWT）方法将图像分解为高达八个级别的近似和细节系数。近似图像与原始图像堆叠。最小距离分类器用于将图像分为六个Lulc类即水，农业，城市，休耕地，沙子和植被。将所有分解水平的分类精度与基于原始多光谱图像的分类产品进行比较。发现多光谱图像的分类精度为75.27％。然而，发现分类准确性可分别提高高达85.97％，88.87％，93.87％，93.03％，93.01，92.32％和90.80％，分别为第二，第三，第四，第五，六个，第七和八级分解。发现了在五级分解的图像的显着提高的准确度。因此，可以使用DWT对LULC分类的方法显着提高分类精度。

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