2,143 research outputs found
Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models
In remote sensing images, the absolute orientation of objects is arbitrary.
Depending on an object's orientation and on a sensor's flight path, objects of
the same semantic class can be observed in different orientations in the same
image. Equivariance to rotation, in this context understood as responding with
a rotated semantic label map when subject to a rotation of the input image, is
therefore a very desirable feature, in particular for high capacity models,
such as Convolutional Neural Networks (CNNs). If rotation equivariance is
encoded in the network, the model is confronted with a simpler task and does
not need to learn specific (and redundant) weights to address rotated versions
of the same object class. In this work we propose a CNN architecture called
Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation
equivariance in the network itself. By using rotating convolutions as building
blocks and passing only the the values corresponding to the maximally
activating orientation throughout the network in the form of orientation
encoding vector fields, RotEqNet treats rotated versions of the same object
with the same filter bank and therefore achieves state-of-the-art performances
even when using very small architectures trained from scratch. We test RotEqNet
in two challenging sub-decimeter resolution semantic labeling problems, and
show that we can perform better than a standard CNN while requiring one order
of magnitude less parameters
Fine-Grained Property Value Assessment using Probabilistic Disaggregation
The monetary value of a given piece of real estate, a parcel, is often
readily available from a geographic information system. However, for many
applications, such as insurance and urban planning, it is useful to have
estimates of property value at much higher spatial resolutions. We propose a
method to estimate the distribution over property value at the pixel level from
remote sensing imagery. We evaluate on a real-world dataset of a major urban
area. Our results show that the proposed approaches are capable of generating
fine-level estimates of property values, significantly improving upon a diverse
collection of baseline approaches.Comment: 4 pages, 1 figure, Accepted to IGARSS 202
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
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Predictive coding is attractive for compression onboard of spacecrafts thanks
to its low computational complexity, modest memory requirements and the ability
to accurately control quality on a pixel-by-pixel basis. Traditionally,
predictive compression focused on the lossless and near-lossless modes of
operation where the maximum error can be bounded but the rate of the compressed
image is variable. Rate control is considered a challenging problem for
predictive encoders due to the dependencies between quantization and prediction
in the feedback loop, and the lack of a signal representation that packs the
signal's energy into few coefficients. In this paper, we show that it is
possible to design a rate control scheme intended for onboard implementation.
In particular, we propose a general framework to select quantizers in each
spatial and spectral region of an image so as to achieve the desired target
rate while minimizing distortion. The rate control algorithm allows to achieve
lossy, near-lossless compression, and any in-between type of compression, e.g.,
lossy compression with a near-lossless constraint. While this framework is
independent of the specific predictor used, in order to show its performance,
in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless
compression standard, obtaining an extension that allows to perform lossless,
near-lossless and lossy compression in a single package. We show that the rate
controller has excellent performance in terms of accuracy in the output rate,
rate-distortion characteristics and is extremely competitive with respect to
state-of-the-art transform coding
Decreasing time consumption of microscopy image segmentation through parallel processing on the GPU
The computational performance of graphical processing units (GPUs) has improved significantly. Achieving speedup factors of more than 50x compared to single-threaded CPU execution are not uncommon due to parallel processing. This makes their use for high throughput microscopy image analysis very appealing. Unfortunately, GPU programming is not straightforward and requires a lot of programming skills and effort. Additionally, the attainable speedup factor is hard to predict, since it depends on the type of algorithm, input data and the way in which the algorithm is implemented. In this paper, we identify the characteristic algorithm and data-dependent properties that significantly relate to the achievable GPU speedup. We find that the overall GPU speedup depends on three major factors: (1) the coarse-grained parallelism of the algorithm, (2) the size of the data and (3) the computation/memory transfer ratio. This is illustrated on two types of well-known segmentation methods that are extensively used in microscopy image analysis: SLIC superpixels and high-level geometric active contours. In particular, we find that our used geometric active contour segmentation algorithm is very suitable for parallel processing, resulting in acceleration factors of 50x for 0.1 megapixel images and 100x for 10 megapixel images
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