19,498 research outputs found
Classification of high resolution satellite images
In this thesis the Support Vector Machine (SVM)is applied on classification of high resolution satellite images. Sveral different measures for classification, including texture mesasures, 1st order statistics, and simple contextual information were evaluated. Additionnally, the image was segmented, using an enhanced watershed method, in order to improve the classification accuracy
Recurrent Multiresolution Convolutional Networks for VHR Image Classification
Classification of very high resolution (VHR) satellite images has three major
challenges: 1) inherent low intra-class and high inter-class spectral
similarities, 2) mismatching resolution of available bands, and 3) the need to
regularize noisy classification maps. Conventional methods have addressed these
challenges by adopting separate stages of image fusion, feature extraction, and
post-classification map regularization. These processing stages, however, are
not jointly optimizing the classification task at hand. In this study, we
propose a single-stage framework embedding the processing stages in a recurrent
multiresolution convolutional network trained in an end-to-end manner. The
feedforward version of the network, called FuseNet, aims to match the
resolution of the panchromatic and multispectral bands in a VHR image using
convolutional layers with corresponding downsampling and upsampling operations.
Contextual label information is incorporated into FuseNet by means of a
recurrent version called ReuseNet. We compared FuseNet and ReuseNet against the
use of separate processing steps for both image fusion, e.g. pansharpening and
resampling through interpolation, and map regularization such as conditional
random fields. We carried out our experiments on a land cover classification
task using a Worldview-03 image of Quezon City, Philippines and the ISPRS 2D
semantic labeling benchmark dataset of Vaihingen, Germany. FuseNet and ReuseNet
surpass the baseline approaches in both quantitative and qualitative results
Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data
Informal settlements are home to the most socially and economically
vulnerable people on the planet. In order to deliver effective economic and
social aid, non-government organizations (NGOs), such as the United Nations
Children's Fund (UNICEF), require detailed maps of the locations of informal
settlements. However, data regarding informal and formal settlements is
primarily unavailable and if available is often incomplete. This is due, in
part, to the cost and complexity of gathering data on a large scale. To address
these challenges, we, in this work, provide three contributions. 1) A brand new
machine learning data-set, purposely developed for informal settlement
detection. 2) We show that it is possible to detect informal settlements using
freely available low-resolution (LR) data, in contrast to previous studies that
use very-high resolution (VHR) satellite and aerial imagery, something that is
cost-prohibitive for NGOs. 3) We demonstrate two effective classification
schemes on our curated data set, one that is cost-efficient for NGOs and
another that is cost-prohibitive for NGOs, but has additional utility. We
integrate these schemes into a semi-automated pipeline that converts either a
LR or VHR satellite image into a binary map that encodes the locations of
informal settlements.Comment: Published at the AAAI/ACM Conference on AI, ethics and society.
Extended results from our previous workshop: arXiv:1812.0081
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Multi-Entity Dependence Learning (MEDL) explores conditional correlations
among multiple entities. The availability of rich contextual information
requires a nimble learning scheme that tightly integrates with deep neural
networks and has the ability to capture correlation structures among
exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional
multivariate distribution as a generating process. As a result, the variational
lower bound of the joint likelihood can be optimized via a conditional
variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was
motivated by two real-world applications in computational sustainability: one
studies the spatial correlation among multiple bird species using the eBird
data and the other models multi-dimensional landscape composition and human
footprint in the Amazon rainforest with satellite images. We show that
MEDL_CVAE captures rich dependency structures, scales better than previous
methods, and further improves on the joint likelihood taking advantage of very
large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall
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