Usage of indices for extraction of built-up areas and vegetation features from landsat TM image: a case of Dar es Salaam and Kisarawe peri-urban areas, Tanzania

F. Mwakapuja, E. Liwa, J. Kashaigili
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引用次数: 19

Abstract

Abstrac t This paper address the use of Indices Co mbination with Supervision Classification methods to extract urban built-up areas, vegetation and water features fro m Landsat Thematic Mapper (TM7) imagery covering Dar es Salaam and Kisarawe peri-urban areas. The study uses three indices; Normalized Difference Bu ilt-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SA VI) to reduce the seven bands Landsat TM7 image into three thematic-oriented bands. Data correlation, spectral confusion and redundancy between original mu ltispectral bands were significantly reduced upon application of the techniques. As a result, the spectral signatures of the three urban land-use classes are mo re distinguishable in the new co mposite image than in the original seven-band image since the spectral clusters of the classes are well separated. Through a supervised classification on the newly formed image, the urban built-up areas, vegetation and water features were finally extracted effect ively; with the accuracy of 82.05 percent attained. The results show that the technique is effective and reliable and can be considered for use in other areas with similar characteristics.
利用指数从landsat TM影像提取建成区和植被特征:以坦桑尼亚达累斯萨拉姆和基萨拉维城郊地区为例
摘要本文研究了利用指数结合监督分类方法,从达累斯萨拉姆和基萨拉维的Landsat Thematic Mapper (TM7)影像中提取城市建成区、植被和水体特征。本研究使用了三个指标;利用归一化差异水体指数(NDBI)、修正归一化差异水体指数(MNDWI)和土壤调整植被指数(SA VI)将Landsat TM7影像的7个波段简化为3个主题导向波段。应用该技术可显著降低原始多光谱波段之间的数据相关性、光谱混淆和冗余度。结果表明,与原7波段图像相比,新合成图像中3个城市土地利用类别的光谱簇分离较好,使得3个城市土地利用类别的光谱特征更容易区分。通过对新生成的图像进行监督分类,最终提取出有效的城市建成区、植被和水体特征;准确率达到82.05%。结果表明,该技术是有效可靠的,可以考虑在其他具有类似特点的领域推广应用。
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