9,831 research outputs found
Learning Aerial Image Segmentation from Online Maps
This study deals with semantic segmentation of high-resolution (aerial)
images where a semantic class label is assigned to each pixel via supervised
classification as a basis for automatic map generation. Recently, deep
convolutional neural networks (CNNs) have shown impressive performance and have
quickly become the de-facto standard for semantic segmentation, with the added
benefit that task-specific feature design is no longer necessary. However, a
major downside of deep learning methods is that they are extremely data-hungry,
thus aggravating the perennial bottleneck of supervised classification, to
obtain enough annotated training data. On the other hand, it has been observed
that they are rather robust against noise in the training labels. This opens up
the intriguing possibility to avoid annotating huge amounts of training data,
and instead train the classifier from existing legacy data or crowd-sourced
maps which can exhibit high levels of noise. The question addressed in this
paper is: can training with large-scale, publicly available labels replace a
substantial part of the manual labeling effort and still achieve sufficient
performance? Such data will inevitably contain a significant portion of errors,
but in return virtually unlimited quantities of it are available in larger
parts of the world. We adapt a state-of-the-art CNN architecture for semantic
segmentation of buildings and roads in aerial images, and compare its
performance when using different training data sets, ranging from manually
labeled, pixel-accurate ground truth of the same city to automatic training
data derived from OpenStreetMap data from distant locations. We report our
results that indicate that satisfying performance can be obtained with
significantly less manual annotation effort, by exploiting noisy large-scale
training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure
DEEP FULLY RESIDUAL CONVOLUTIONAL NEURAL NETWORK FOR SEMANTIC IMAGE SEGMENTATION
Department of Computer Science and EngineeringThe goal of semantic image segmentation is to partition the pixels of an image into semantically meaningful parts and classifying those parts according to a predefined label set. Although object recognition
models achieved remarkable performance recently and they even surpass human???s ability to recognize
objects, but semantic segmentation models are still behind. One of the reason that makes semantic
segmentation relatively a hard problem is the image understanding at pixel level by considering global
context as oppose to object recognition. One other challenge is transferring the knowledge of an object
recognition model for the task of semantic segmentation. In this thesis, we are delineating some of the
main challenges we faced approaching semantic image segmentation with machine learning algorithms.
Our main focus was how we can use deep learning algorithms for this task since they require the
least amount of feature engineering and also it was shown that such models can be applied to large scale
datasets and exhibit remarkable performance. More precisely, we worked on a variation of convolutional
neural networks (CNN) suitable for the semantic segmentation task. We proposed a model called deep
fully residual convolutional networks (DFRCN) to tackle this problem. Utilizing residual learning makes
training of deep models feasible which ultimately leads to having a rich powerful visual representation.
Our model also benefits from skip-connections which ease the propagation of information from the
encoder module to the decoder module. This would enable our model to have less parameters in the
decoder module while it also achieves better performance. We also benchmarked the effective variation
of the proposed model on a semantic segmentation benchmark.
We first make a thorough review of current high-performance models and the problems one might
face when trying to replicate such models which mainly arose from the lack of sufficient provided
information. Then, we describe our own novel method which we called deep fully residual convolutional
network (DFRCN). We showed that our method exhibits state of the art performance on a challenging
benchmark for aerial image segmentation.clos
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