2,422 research outputs found
Exploring Context with Deep Structured models for Semantic Segmentation
State-of-the-art semantic image segmentation methods are mostly based on
training deep convolutional neural networks (CNNs). In this work, we proffer to
improve semantic segmentation with the use of contextual information. In
particular, we explore `patch-patch' context and `patch-background' context in
deep CNNs. We formulate deep structured models by combining CNNs and
Conditional Random Fields (CRFs) for learning the patch-patch context between
image regions. Specifically, we formulate CNN-based pairwise potential
functions to capture semantic correlations between neighboring patches.
Efficient piecewise training of the proposed deep structured model is then
applied in order to avoid repeated expensive CRF inference during the course of
back propagation. For capturing the patch-background context, we show that a
network design with traditional multi-scale image inputs and sliding pyramid
pooling is very effective for improving performance. We perform comprehensive
evaluation of the proposed method. We achieve new state-of-the-art performance
on a number of challenging semantic segmentation datasets including ,
-, , -, -,
-, and datasets. Particularly, we report an
intersection-over-union score of on the - dataset.Comment: 16 pages. Accepted to IEEE T. Pattern Analysis & Machine
Intelligence, 2017. Extended version of arXiv:1504.0101
Learning representations in the hyperspectral domain in aerial imagery
We establish two new datasets with baselines and network architectures for the task of hyperspectral image analysis. The first dataset, AeroRIT, is a moving camera static scene captured from a flight and contains per pixel labeling across five categories for the task of semantic segmentation. The second dataset, RooftopHSI, helps design and interpret learnt features on hyperspectral object detection on scenes captured from an university rooftop. This dataset accounts for static camera, moving scene hyperspectral imagery. We further broaden the scope of our understanding of neural networks with the development of two novel algorithms - S4AL and S4AL+. We develop these frameworks on natural (color) imagery, by combining semi-supervised learning and active learning, and display promising results for learning with limited amount of labeled data, which can be extended to hyperspectral imagery. In this dissertation, we curated two new datasets for hyperspectral image analysis, significantly larger than existing datasets and broader in terms of categories for classification. We then adapt existing neural network architectures to function on the increased channel information, in a smart manner, to leverage all hyperspectral information. We also develop novel active learning algorithms on natural (color) imagery, and discuss the hope for expanding their functionality to hyperspectral imagery
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
Semantic labeling (or pixel-level land-cover classification) in ultra-high
resolution imagery (< 10cm) requires statistical models able to learn high
level concepts from spatial data, with large appearance variations.
Convolutional Neural Networks (CNNs) achieve this goal by learning
discriminatively a hierarchy of representations of increasing abstraction.
In this paper we present a CNN-based system relying on an
downsample-then-upsample architecture. Specifically, it first learns a rough
spatial map of high-level representations by means of convolutions and then
learns to upsample them back to the original resolution by deconvolutions. By
doing so, the CNN learns to densely label every pixel at the original
resolution of the image. This results in many advantages, including i)
state-of-the-art numerical accuracy, ii) improved geometric accuracy of
predictions and iii) high efficiency at inference time.
We test the proposed system on the Vaihingen and Potsdam sub-decimeter
resolution datasets, involving semantic labeling of aerial images of 9cm and
5cm resolution, respectively. These datasets are composed by many large and
fully annotated tiles allowing an unbiased evaluation of models making use of
spatial information. We do so by comparing two standard CNN architectures to
the proposed one: standard patch classification, prediction of local label
patches by employing only convolutions and full patch labeling by employing
deconvolutions. All the systems compare favorably or outperform a
state-of-the-art baseline relying on superpixels and powerful appearance
descriptors. The proposed full patch labeling CNN outperforms these models by a
large margin, also showing a very appealing inference time.Comment: Accepted in IEEE Transactions on Geoscience and Remote Sensing, 201
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