An index based road feature extraction from LANDSAT-8 OLI images

Abstract

Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (SSF), is proposed. Then, an iterative adaptive threshold (IAT) based online search with variational min-max and Markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RI) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

“…Therefore, multispectral images store multiple values for each pixel, captured through the amount of light in different channels of the electromagnetic spectrum. The common multispectral images are RGB, which contain three channels that correspond to the R, G and B regions of the spectrum [39], [60], [84], [88], [94]. However, throughout the study, the false colour image is used, where the three channels correspond to the NIR, R and G regions.…”

“…Feature extraction allows for a more-accurate detection of features, as a result of the image's enhancements [15], [16], [39], [89], [143]- [145], [148]. The signature of feature classes could be distinguished from complex surface textures.…”

River classification and change detection from landsat images by using a river classification toolbox

“…In addition, with high spectral resolution, main rivers can be characterized at great accuracy [6]- [10]. Nonetheless, for sub-river classification, which required greater fidelty, Landsat images are usually considered jointly with other RS data [23]- [26]. Based on imaging data, the common foundation of classification techniques is characterizing picture elements by their reflectance indices, and the most prevailing of which include NDWI, MNDWI, and AWEI.…”