20 research outputs found
Earth Observation Image Semantics: Latent Dirichlet Allocation Based Information Discovery
Land cover maps are among the most important products of Remote Sensing (RS) imagery. Despite remarkable advancements in land cover classification techniques, abundant detailed information in the very high-resolution RS images necessitates further improvements to harness the data and discover detailed semantic information. Moreover, scarcity of the labelled data and its quality is a major limitation in RS land cover mapping. In the present study, Latent Dirichlet Allocation is employed for semantic discovery in RS images and a novel kernel-based Bag of Visual Words model is proposed for land cover mapping
Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification
Aerial image classification is of great significance in remote sensing
community, and many researches have been conducted over the past few years.
Among these studies, most of them focus on categorizing an image into one
semantic label, while in the real world, an aerial image is often associated
with multiple labels, e.g., multiple object-level labels in our case. Besides,
a comprehensive picture of present objects in a given high resolution aerial
image can provide more in-depth understanding of the studied region. For these
reasons, aerial image multi-label classification has been attracting increasing
attention. However, one common limitation shared by existing methods in the
community is that the co-occurrence relationship of various classes, so called
class dependency, is underexplored and leads to an inconsiderate decision. In
this paper, we propose a novel end-to-end network, namely class-wise
attention-based convolutional and bidirectional LSTM network (CA-Conv-BiLSTM),
for this task. The proposed network consists of three indispensable components:
1) a feature extraction module, 2) a class attention learning layer, and 3) a
bidirectional LSTM-based sub-network. Particularly, the feature extraction
module is designed for extracting fine-grained semantic feature maps, while the
class attention learning layer aims at capturing discriminative class-specific
features. As the most important part, the bidirectional LSTM-based sub-network
models the underlying class dependency in both directions and produce
structured multiple object labels. Experimental results on UCM multi-label
dataset and DFC15 multi-label dataset validate the effectiveness of our model
quantitatively and qualitatively
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Remote Sensing Image Retrieval remains a challenging topic due to the special
nature of Remote Sensing Imagery. Such images contain various different
semantic objects, which clearly complicates the retrieval task. In this paper,
we present an image retrieval pipeline that uses attentive, local convolutional
features and aggregates them using the Vector of Locally Aggregated Descriptors
(VLAD) to produce a global descriptor. We study various system parameters such
as the multiplicative and additive attention mechanisms and descriptor
dimensionality. We propose a query expansion method that requires no external
inputs. Experiments demonstrate that even without training, the local
convolutional features and global representation outperform other systems.
After system tuning, we can achieve state-of-the-art or competitive results.
Furthermore, we observe that our query expansion method increases overall
system performance by about 3%, using only the top-three retrieved images.
Finally, we show how dimensionality reduction produces compact descriptors with
increased retrieval performance and fast retrieval computation times, e.g. 50%
faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal
contributio
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