# Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks Source: [Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:40.828Z Authors: Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot, William R. Schwartz, Jefersson A. dos Santos Published: 2018-04-11T14:32:15.000Z ## Abstract Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Towards such goal, Convolutional Networks can learn specific and adaptable features based on the data. However, these networks are not capable of processing a whole remote sensing image, given its huge size. To overcome such limitation, the image is processed using fixed size patches. The definition of the input patch size is usually performed empirically (evaluating several sizes) or imposed (by network constraint). Both strategies suffer from drawbacks and could not lead to the best patch size. To alleviate this problem, several works exploited multi-context information by combining networks or layers. This process increases the number of parameters resulting in a more difficult model to train. In this work, we propose a novel technique to perform semantic segmentation of remote sensing images that exploits a multi-context paradigm without increasing the number of parameters while defining, in training time, the best patch size. The main idea is to train a dilated network with distinct patch sizes, allowing it to capture multi-context characteristics from heterogeneous contexts. While processing these varying patches, the network provides a score for each patch size, helping in the definition of the best size for the current scenario. A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties. Our results show that the proposed algorithm provides improvements in pixelwise classification accuracy when compared to state-of-the-art methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on Potsdam (test)](): Semantic segmentation; Potsdam (test); mIoU: not recorded. ## Original sources and implementation [Original paper]() [PDF]()