24,813 research outputs found
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
Aerial scene recognition is a fundamental task in remote sensing and has
recently received increased interest. While the visual information from
overhead images with powerful models and efficient algorithms yields
considerable performance on scene recognition, it still suffers from the
variation of ground objects, lighting conditions etc. Inspired by the
multi-channel perception theory in cognition science, in this paper, for
improving the performance on the aerial scene recognition, we explore a novel
audiovisual aerial scene recognition task using both images and sounds as
input. Based on an observation that some specific sound events are more likely
to be heard at a given geographic location, we propose to exploit the knowledge
from the sound events to improve the performance on the aerial scene
recognition. For this purpose, we have constructed a new dataset named AuDio
Visual Aerial sceNe reCognition datasEt (ADVANCE). With the help of this
dataset, we evaluate three proposed approaches for transferring the sound event
knowledge to the aerial scene recognition task in a multimodal learning
framework, and show the benefit of exploiting the audio information for the
aerial scene recognition. The source code is publicly available for
reproducibility purposes.Comment: ECCV 202
Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
A detailed environment perception is a crucial component of automated
vehicles. However, to deal with the amount of perceived information, we also
require segmentation strategies. Based on a grid map environment
representation, well-suited for sensor fusion, free-space estimation and
machine learning, we detect and classify objects using deep convolutional
neural networks. As input for our networks we use a multi-layer grid map
efficiently encoding 3D range sensor information. The inference output consists
of a list of rotated bounding boxes with associated semantic classes. We
conduct extensive ablation studies, highlight important design considerations
when using grid maps and evaluate our models on the KITTI Bird's Eye View
benchmark. Qualitative and quantitative benchmark results show that we achieve
robust detection and state of the art accuracy solely using top-view grid maps
from range sensor data.Comment: 6 pages, 4 tables, 4 figure
LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery
State-of-the-art object detection approaches such as Fast/Faster R-CNN, SSD,
or YOLO have difficulties detecting dense, small targets with arbitrary
orientation in large aerial images. The main reason is that using interpolation
to align RoI features can result in a lack of accuracy or even loss of location
information. We present the Local-aware Region Convolutional Neural Network
(LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery.
We enhance translation invariance to detect dense vehicles and address the
boundary quantization issue amongst dense vehicles by aggregating the
high-precision RoIs' features. Moreover, we resample high-level semantic pooled
features, making them regain location information from the features of a
shallower convolutional block. This strengthens the local feature invariance
for the resampled features and enables detecting vehicles in an arbitrary
orientation. The local feature invariance enhances the learning ability of the
focal loss function, and the focal loss further helps to focus on the hard
examples. Taken together, our method better addresses the challenges of aerial
imagery. We evaluate our approach on several challenging datasets (VEDAI,
DOTA), demonstrating a significant improvement over state-of-the-art methods.
We demonstrate the good generalization ability of our approach on the DLR 3K
dataset.Comment: 8 page
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