Extraction of Built-Up Area by Combining Textural Features and Spectral Indices From Landsat-8 Multispectral Image

Abstract

Remote sensing techniques provide efficient and cost-effective approach to monitor the expansion of built-up area, in comparison to other traditional approaches. For extracting built-up class, one of the common approaches is to use spectral and spatial features such as, Normalized Difference Built-up index (NDBI), GLCM texture, Gabor filters etc. However, it is observed that classes such as river soil and fallow land usually mix up with built-up class due to their close spectral similarity. Intermixing of classes have been observed in the classified image when using spectral channels. In this paper, an approach has been proposed which uses urban based spectral indices and textural features to extract built-up areas. Three well known spectral indices i.e. NDBI, Built-up Area Extraction Index (BAEI) and Normalized Difference Bareness Index (NDBai) have been used in this work. Along with spectral indices, local spatial dependency of neighborhood regions is captured using eight GLCM based textural feature, such as, Contrast, Correlation, Energy and Homogeneity etc. for each image band. All textural and spectral indices bands are combined and used for extracting built-up areas using Support Vector Machine (SVM) classifier. Results suggest 4.91% increase in overall accuracy when using texture and spectral indices in comparison with 84.38% overall accuracy achieved when using spectral data only. It is observed that built-up class are more separable in the projected spectral-spatial feature space in comparison to spectral channels. Incorporation of textural features with spectral features reduces the misclassification error and provides results with less salt and pepper noise.

Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020)

“…NIR − SWIR NIR + SWIR [57] Normalized Difference Built-up Index (NDBI) SWIR − NIR SWIR + NIR [58] Built-up Area Extraction Index (BAEI)…”

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

“…Spatial features are considered in the second-order texture metrics, such as Haralick indices, which use the grey-level co-occurrence matrix (GLCM) to compute co-occurring intensity pairs based on two grey-level pixels at a given displacement and at a defined direction [82][83][84][85][86][87]. The scale of the moving window can affect the details of the obtained texture information and the processing time [1,21,87]. The number of pixel intensity levels has an important role in the extraction of image texture; thus, a quantization method is required to reduce the intensity levels [84,85].…”

“…as an average (isotropic) matrix [1,82,[84][85][86]. Furthermore, we computed textural information derived from the run-length matrix (consecutive connected pixels of the same grey level as run, and the number of pixels in the run as length) [88,89] (Table 2).…”

“…Cities dynamically change with time regarding their area and appearance due to accelerated urbanization, in high accordance with the increasing population of the world [1]. The ratio of urban population was 55% in 2018; it has been almost doubled since 1960 [2].…”

Applying Multi-Index approach from Sentinel-2 Imagery to Extract Urban Area in dry season (Semi-Arid Land in North East Algeria)

“…This option enabled several researchers, for example (Pal and Antil, 2017;Leroux et al, 2018;Gašparović et al, 2019), to expect a better classification mapping of the land cover/use. Likewise, other researches focused on urban area detection such as the works of (Xu, 2007;Patel and Mukherjee, 2014;Bramhe et al, 2018;Lee et al, 2018;Nur Hidayati et al, 2018;Ettehadi Osgouei et al, 2019;Lynch and Blesius, 2019). However, the study of (Ettehadi Osgouei et al, 2019) applied the Normalized Difference Tillage Index (NDTI) -developed in (Deventer, 1997) and also used in (Daughtry et al, 2010;Eskandari et al, 2016) -which has used SWIR bands of the Sentinel-2 images and succeeded in differentiating bare land and built-up area classes better than the other spectral indices used in the study.…”