65 research outputs found
Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks
Automatic urban land cover classification is a fundamental problem in remote
sensing, e.g. for environmental monitoring. The problem is highly challenging,
as classes generally have high inter-class and low intra-class variance.
Techniques to improve urban land cover classification performance in remote
sensing include fusion of data from different sensors with different data
modalities. However, such techniques require all modalities to be available to
the classifier in the decision-making process, i.e. at test time, as well as in
training. If a data modality is missing at test time, current state-of-the-art
approaches have in general no procedure available for exploiting information
from these modalities. This represents a waste of potentially useful
information. We propose as a remedy a convolutional neural network (CNN)
architecture for urban land cover classification which is able to embed all
available training modalities in a so-called hallucination network. The network
will in effect replace missing data modalities in the test phase, enabling
fusion capabilities even when data modalities are missing in testing. We
demonstrate the method using two datasets consisting of optical and digital
surface model (DSM) images. We simulate missing modalities by assuming that DSM
images are missing during testing. Our method outperforms both standard CNNs
trained only on optical images as well as an ensemble of two standard CNNs. We
further evaluate the potential of our method to handle situations where only
some DSM images are missing during testing. Overall, we show that we can
clearly exploit training time information of the missing modality during
testing
Self-Constructing Graph Convolutional Networks for Semantic Labeling
Graph Neural Networks (GNNs) have received increasing attention in many
fields. However, due to the lack of prior graphs, their use for semantic
labeling has been limited. Here, we propose a novel architecture called the
Self-Constructing Graph (SCG), which makes use of learnable latent variables to
generate embeddings and to self-construct the underlying graphs directly from
the input features without relying on manually built prior knowledge graphs.
SCG can automatically obtain optimized non-local context graphs from
complex-shaped objects in aerial imagery. We optimize SCG via an adaptive
diagonal enhancement method and a variational lower bound that consists of a
customized graph reconstruction term and a Kullback-Leibler divergence
regularization term. We demonstrate the effectiveness and flexibility of the
proposed SCG on the publicly available ISPRS Vaihingen dataset and our model
SCG-Net achieves competitive results in terms of F1-score with much fewer
parameters and at a lower computational cost compared to related pure-CNN based
work. Our code will be made public soon.Comment: IGARSS-2020, code at: github.com/samleoqh/MSCG-Ne
Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks
Detailed and complete mapping of forest roads is important for the forest industry since they are used for timber transport by trucks with long trailers. This paper proposes a new automatic method for large-scale mapping forest roads from airborne laser scanning data. The method is based on a fully convolutional neural network that performs end-to-end segmentation. To train the network, a large set of image patches with corresponding road label information are applied. The final network is then applied to detect and map forest roads from lidar data covering the Etnedal municipality in Norway. The results show that we are able to map the forest roads with an overall accuracy of 97.2%. We conclude that the method has a strong potential for large-scale operational mapping of forest roads
Dense Dilated Convolutions Merging Network for Land Cover Classification
Land cover classification of remote sensing images is a challenging task due
to limited amounts of annotated data, highly imbalanced classes, frequent
incorrect pixel-level annotations, and an inherent complexity in the semantic
segmentation task. In this article, we propose a novel architecture called the
dense dilated convolutions' merging network (DDCM-Net) to address this task.
The proposed DDCM-Net consists of dense dilated image convolutions merged with
varying dilation rates. This effectively utilizes rich combinations of dilated
convolutions that enlarge the network's receptive fields with fewer parameters
and features compared with the state-of-the-art approaches in the remote
sensing domain. Importantly, DDCM-Net obtains fused local- and global-context
information, in effect incorporating surrounding discriminative capability for
multiscale and complex-shaped objects with similar color and textures in very
high-resolution aerial imagery. We demonstrate the effectiveness, robustness,
and flexibility of the proposed DDCM-Net on the publicly available ISPRS
Potsdam and Vaihingen data sets, as well as the DeepGlobe land cover data set.
Our single model, trained on three-band Potsdam and Vaihingen data sets,
achieves better accuracy in terms of both mean intersection over union (mIoU)
and F1-score compared with other published models trained with more than
three-band data. We further validate our model on the DeepGlobe data set,
achieving state-of-the-art result 56.2% mIoU with much fewer parameters and at
a lower computational cost compared with related recent work. Code available at
https://github.com/samleoqh/DDCM-Semantic-Segmentation-PyTorchComment: Semantic Segmentation, 12 pages, TGRS-2020 early access in IEEE
Transactions on Geoscience and Remote Sensing. 2020, Code available at
https://github.com/samleoqh/DDCM-Semantic-Segmentation-PyTorc
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation
We propose a novel architecture called the Multi-view Self-Constructing Graph
Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the
recently proposed Self-Constructing Graph (SCG) module, which makes use of
learnable latent variables to self-construct the underlying graphs directly
from the input features without relying on manually built prior knowledge
graphs, we leverage multiple views in order to explicitly exploit the
rotational invariance in airborne images. We further develop an adaptive class
weighting loss to address the class imbalance. We demonstrate the effectiveness
and flexibility of the proposed method on the Agriculture-Vision challenge
dataset and our model achieves very competitive results (0.547 mIoU) with much
fewer parameters and at a lower computational cost compared to related pure-CNN
based work. Code will be available at: github.com/samleoqh/MSCG-NetComment: 7-page, MSCG-Net, CVPRW-202
Experiments with remote sensing in the context of avalanche warning and detection
In Proceedings of Advances in Avalanche Forecasting, PodbanskĂŠ, Slovakia, 22 October 2012Two Norwegian projects carried out by NGI and NR have investigated and experimented with the potential of using remote sensing for avalanche warning and detection: The Norwegian Space Centre (NSC) supported project âImproved Avalanche Warning Using Satellite Dataâ (2008-2010) and the European Space Agency (ESA) funded project âAvalanche Inventory for Decision Support and Hind-cast - AvalRSâ (2008â2011)
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning
representations and simultaneously discovering cluster structure in unlabeled
data by optimizing a discriminative loss function. As opposed to supervised
deep learning, this line of research is in its infancy, and how to design and
optimize suitable loss functions to train deep neural networks for clustering
is still an open question. Our contribution to this emerging field is a new
deep clustering network that leverages the discriminative power of
information-theoretic divergence measures, which have been shown to be
effective in traditional clustering. We propose a novel loss function that
incorporates geometric regularization constraints, thus avoiding degenerate
structures of the resulting clustering partition. Experiments on synthetic
benchmarks and real datasets show that the proposed network achieves
competitive performance with respect to other state-of-the-art methods, scales
well to large datasets, and does not require pre-training steps
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