Feature extraction and tracking of CNN segmentations for improved road detection from satellite imagery

International audienceRoad detection in high-resolution satellite images is an important and popular research topic in the field of image processing. In this paper, we propose a novel road extraction and tracking method based on road segmentation results from a convolutional network, providing improved road detection. The proposed method incorporates our previously proposed connected-tube marked point process (MPP) model and a post-tracking algorithm. We present experimental results on the Massachusetts roads dataset to show the performance of our method on road detection in remotely-sensed images

Searching and describing objects in satellite images on the basis of modeling reasoning

В статье представлен подход к проблеме контекстного поиска и описания объектов на растровых космоснимках, заключающийся в моделировании рассуждений на основе структурированных прецедентов. В результате обработки изображения строится граф смежности цветовых областей. Объект характеризуется цветом, атрибутами формы отрезков границы и формы объекта в целом. Структурированный прецедент представляется в виде лучевого графа, дуги которого упорядочены в соответствии с положительным обходом границ областей. С помощью алгоритма сопоставления графов в анализируемом изображении выявляются вхождения прецедентов из базы данных системы. При обнаружении вхождения применяется правило прецедентного вывода. Степень принадлежности объекта некоторому классу зависит не только от свойств самого объекта, но и от достоверности окружающих его объектов. Стратегия контекстного поиска содержит этапы рекурсии и итерации. В отличие от нейросетевых технологий, предложенный подход позволяет не только классифицировать изображенные объекты, но и получать их структурированные описания. Кроме того, выдаваемое системой классификационное решение имеет аргументированное обоснование. Результаты эксперимента показывают, что рассуждения на основе структурированных прецедентов позволяют уточнять результаты классификации и повышать достоверность распознавания объектов на космоснимках. The article presents an approach to a problem of contextual search and description of objects in raster satellite images, which consists in modeling reasoning on the basis of structured cases. As a result of image processing, an adjacency graph of color regions is constructed. The object is characterized by color, attributes of the form of segments of the border and the shape of the object as a whole. A structured case is represented in the form of a beam graph, whose arcs are ordered according to a positive bypass of the region boundaries. Using a graph matching algorithm, occurrences of cases stored in the system database are detected in the analyzed image. When the occurrence is detected, a case-based inference rule is applied. The degree to which an object belongs to a certain class depends not only on the properties of the object itself, but also on the reliability of the surrounding objects. The contextual search strategy contains stages of recursion and iteration. In contrast to neural network technologies, the proposed approach allows one not only to classify image objects, but also to form their structured descriptions. In addition, the classification decision issued by the system has a reasoned justification. The results of the experiment show that reasoning based on structured cases allows refining the results of classification and increasing the reliability of object recognition in satellite images.Работа выполнена за счёт гранта Российского научного фонда – РНФ (проект № 18-71-00109)

Dense Refinement Residual Network for Road Extraction From Aerial Imagery Data

Extraction of roads from high-resolution aerial images with a high degree of accuracy is a prerequisite in various applications. In aerial images, road pixels and background pixels are generally in the ratio of ones-to-tens, which implies a class imbalance problem. Existing semantic segmentation architectures generally do well in road-dominated cases but fail in background-dominated scenarios. This paper proposes a dense refinement residual network (DRR Net) for semantic segmentation of aerial imagery data. The proposed semantic segmentation architecture is composed of multiple DRR modules for the extraction of diversified roads alleviating the class imbalance problem. Each module of the proposed architecture utilizes dense convolutions at various scales only in the encoder for feature learning. Residual connections in each module of the proposed architecture provide the guided learning path by propagating the combined features to subsequent DRR modules. Segmentation maps undergo various levels of refinement based on the number of DRR modules utilized in the architecture. To emphasize more on small object instances, the proposed architecture has been trained with a composite loss function. The qualitative and quantitative results are reported by utilizing the Massachusetts roads dataset. The experimental results report that the proposed architecture provides better results as compared to other recent architectures