Morphological path filtering at the region scale for efficient and robust road network extraction from satellite imagery

Abstract

Roads are important elements in geographic information systems and remote sensing applications. Their automatic extraction is challenging when only aerial or satellite images are used. Recently, some promising attempts have been made with (incomplete) path opening/closing, morphological filters able to deal with curvilinear structures. We propose here to apply morphological path filters not on pixels directly but rather on regions representing road segments, in order to improve both efficiency and robustness. The overall process is organized in two steps: first we map road segments by rectangular areas made of similar content, before we connect such segments into paths of segments or polylines using region-based path filtering. Robustness to occlusion is ensured through the adaptation of the incomplete path filtering strategy to the region scale, while better discrimination between road segments and other objects is achieved through an hit-or-miss transform that exploits background knowledge. Experiments conducted on several satellite images illustrate the interest of the proposed approach, and shows it outperforms pixelwise detection.

Statistical Evaluation and Analysis of Road Extraction Methodologies Using a Unique Dataset from Remote Sensing

“…Another road extraction methodology, proposed by Courtrai and Lefèvre [6], applied a pre-extraction of roads segments, filtering the image with background knowledge, which was then analyzed and connected, whenever necessary, using a region-based path closing. This last step reconstructed the unconnected road segments producing a better result.…”

“…Data not provided. Courtrai and Lefèvre [6] Directional morphological filters. 0.93 0.85 Sghaier and Lepage [2] Texture filter and beamlet transformation.…”

“…There is a significant number of studies published by the scientific literature presenting methodologies for road extraction based on the digital processing of images from remote sensing. For example, it is possible to find papers that presented road extraction methodologies based on many different approaches, such as GPS data [1], texture filters [2], mathematical morphology [3][4][5], and directional filters [6,7], among others. Additionally, due to the importance of this field to cartography, it is easy to find some research reviewing the results of road extraction methodologies [8][9][10].…”

Aerial Image Road Extraction Based on an Improved Generative Adversarial Network

“…They also exploited the self-characteristics of roads, such as spectrum and context [38][39][40][41][42][43][44]. These methods have achieved satisfactory results in complex situations [45][46][47][48][49]. However, these algorithms are not efficient.…”

Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images

“…Generally, state-of-the-art methods for road-feature extraction from VHR images fall into two categories: Automatic and semiautomatic methods. Automatic approaches require no prior information and can be executed by a series of image-processing algorithms, such as mathematical morphology [11,12], active snake model [13], dynamic programming [14], neural networks [15][16][17], probabilistic graphical models [18], filtering-based methods [19], and object-oriented methods [20]. In general, however, the unsatisfactory performance of the automatic method in road-feature extraction from images presenting complex natural road scenarios (e.g., image noise and tree and shadow occlusion) restricts its practical applications [21].…”