26 research outputs found
Relation Network for Multi-label Aerial Image Classification
Multi-label classification plays a momentous role in perceiving intricate
contents of an aerial image and triggers several related studies over the last
years. However, most of them deploy few efforts in exploiting label relations,
while such dependencies are crucial for making accurate predictions. Although
an LSTM layer can be introduced to modeling such label dependencies in a chain
propagation manner, the efficiency might be questioned when certain labels are
improperly inferred. To address this, we propose a novel aerial image
multi-label classification network, attention-aware label relational reasoning
network. Particularly, our network consists of three elemental modules: 1) a
label-wise feature parcel learning module, 2) an attentional region extraction
module, and 3) a label relational inference module. To be more specific, the
label-wise feature parcel learning module is designed for extracting high-level
label-specific features. The attentional region extraction module aims at
localizing discriminative regions in these features and yielding attentional
label-specific features. The label relational inference module finally predicts
label existences using label relations reasoned from outputs of the previous
module. The proposed network is characterized by its capacities of extracting
discriminative label-wise features in a proposal-free way and reasoning about
label relations naturally and interpretably. In our experiments, we evaluate
the proposed model on the UCM multi-label dataset and a newly produced dataset,
AID multi-label dataset. Quantitative and qualitative results on these two
datasets demonstrate the effectiveness of our model. To facilitate progress in
the multi-label aerial image classification, the AID multi-label dataset will
be made publicly available
Deep Learning for Aerial Scene Understanding in High Resolution Remote Sensing Imagery from the Lab to the Wild
Diese Arbeit präsentiert die Anwendung von Deep Learning beim Verständnis von Luftszenen, z. B. Luftszenenerkennung, Multi-Label-Objektklassifizierung und semantische Segmentierung. Abgesehen vom Training tiefer Netzwerke unter Laborbedingungen bietet diese Arbeit auch Lernstrategien für praktische Szenarien, z. B. werden Daten ohne Einschränkungen gesammelt oder Annotationen sind knapp
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
SEGMENTATION OF 3D PHOTOGRAMMETRIC POINT CLOUD FOR 3D BUILDING MODELING
3D city modeling has become important over the last decades as these models are being used in different studies including, energy evaluation, visibility analysis, 3D cadastre, urban planning, change detection, disaster management, etc. Segmentation and classification of photogrammetric or LiDAR data is important for 3D city models as these are the main data sources, and, these tasks are challenging due to their complexity. This study presents research in progress, which focuses on the segmentation and classification of 3D point clouds and orthoimages to generate 3D urban models. The aim is to classify photogrammetric-based point clouds (> 30 pts/sqm) in combination with aerial RGB orthoimages (~ 10 cm, RGB image) in order to name buildings, ground level objects (GLOs), trees, grass areas, and other regions. If on the one hand the classification of aerial orthoimages is foreseen to be a fast approach to get classes and then transfer them from the image to the point cloud space, on the other hand, segmenting a point cloud is expected to be much more time consuming but to provide significant segments from the analyzed scene. For this reason, the proposed method combines segmentation methods on the two geoinformation in order to achieve better results