5,481 research outputs found
Learning Semantic Segmentation with Query Points Supervision on Aerial Images
Semantic segmentation is crucial in remote sensing, where high-resolution
satellite images are segmented into meaningful regions. Recent advancements in
deep learning have significantly improved satellite image segmentation.
However, most of these methods are typically trained in fully supervised
settings that require high-quality pixel-level annotations, which are expensive
and time-consuming to obtain. In this work, we present a weakly supervised
learning algorithm to train semantic segmentation algorithms that only rely on
query point annotations instead of full mask labels. Our proposed approach
performs accurate semantic segmentation and improves efficiency by
significantly reducing the cost and time required for manual annotation.
Specifically, we generate superpixels and extend the query point labels into
those superpixels that group similar meaningful semantics. Then, we train
semantic segmentation models, supervised with images partially labeled with the
superpixels pseudo-labels. We benchmark our weakly supervised training approach
on an aerial image dataset and different semantic segmentation architectures,
showing that we can reach competitive performance compared to fully supervised
training while reducing the annotation effort.Comment: Paper presented at the LXCV workshop at ICCV 202
Improving a Satellite Mission System by means of a Semantic Grid Architecture
The use of a semantic grid architecture can make easier the
deployment of complex applications, in which several organizations are involved and diverse resources are shared. This paper presents the application of the architecture defined in the Ontogrid project (S-OGSA) into a scenario for the analysis of the quality of the products of satellite missions
Weakly Supervised Semantic Segmentation of Satellite Images
International audienceWhen one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. If he works with aerial or satellite images, which are usually very large, it is even worse. With that in mind, we investigate how to use image-level annotations in order to perform semantic segmentation. Image-level annotations are much less expensive to acquire than pixel-level annotations, but we lose a lot of information for the training of the model. From the annotations of the images, the model must find by itself how to classify the different regions of the image. In this work, we use the method proposed by Anh and Kwak [1] to produce pixel-level annotation from image level annotation. We compare the overall quality of our generated dataset with the original dataset. In addition, we propose an adaptation of the AffinityNet that allows us to directly perform a semantic segmentation. Our results show that the generated labels lead to the same performances for the training of several segmentation networks. Also, the quality of semantic segmentation performed directly by the AffinityNet and the Random Walk is close to the one of the best fully-supervised approaches
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