Automatic Traffic Monitoring from Satellite Images Using Artificial Immune System

Abstract

Abstract. Automatic and intelligence Road traffic monitoring is a new research issue for high resolution satellite imagery application in transportation. One of the results of this research was to control the traffic jam in roads and to recognize the traffic density quickly and accurately. This article presents a new approach for recognizing the vehicle and the road in satellite high-resolution images in non-urban areas. For road recognition, they used feature extraction and image processing techniques like Hough transform, Gradient, and thresholding operation and they presented an artificial immune approach to extract vehicle targets from high resolution panchromatic satellite imagery. The average of results is about 94 percent and it shows that the used procedure has the suitable efficiency.

Vehicle detection using panchromatic high-resolution satellite images as a support for urban planning. Case study of Prague’s centre

“…Similar results were also obtained in our case, mainly using the KNN and SVM methods in ENVI. Eslami and Faez (2010) presented a framework to detect vehicles from high-resolution panchromatic images (0.6m) in non-urban areas. The recommendation of the best method for vehicle detection depends on the user's preferences.…”

“…The accuracy of vehicle detection from highresolution images can currently reach more than 90%. It is conditioned by good pre-processing, including feature extraction such as Hough transformation and other methods of edge detection (Eslami & Faez 2010). Current research prefers to avoid direct extraction of features and focuses instead on advanced ML methods such as RetinaNet Architecture, Faster R-CNN or YOLO (Bin Zuraimi & Kamaru Zaman 2021; Ghosh 2021; Ma et al 2019;Stuparu et al 2020;Tan et al 2020), which can reach more than 94%.…”

Spatial modeling of micro‐scale carbon dioxide sources and sinks in urban environments: A novel approach to quantify urban impacts on global warming

“…Then, using high-resolution satellite images, the number of vehicles on each of the two tiers of roads (main and sub-streets) was calculated. 44,45 For this purpose, the time series of Google Earth images were used. Then, the number of vehicles on each class of the road per kilometer was calculated using Eqn 5 (Table 1).…”

Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images

“…Eslami and Faez presented a framework to detect vehicles and the road from high-resolution (0.6-1.0 m) panchromatic images in non-urban areas. They first used Hough transform and parallel lines detection to extract roads and then recognized traffic by using the artificial immune network concept [22]; Tuermer et al used a machine-learning method to detected vehicles at dense urban areas from airborne platform at a spatial resolution of 13 cm [23]; Eikvil, Aurdal, and Koren provided an automatic approach consisting of a segmentation step followed by object classification to detect vehicles in high-resolution satellite images with 0.6 m resolution [24]. With the development of deep learning and artificial neural network [25], some deep-learning-based methods also being used in high-resolution remote sensing-based vehicle detection in recent years [26][27][28][29].…”