14,850 research outputs found
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models
In recent years, large amount of high spatial-resolution remote sensing
(HRRS) images are available for land-cover mapping. However, due to the complex
information brought by the increased spatial resolution and the data
disturbances caused by different conditions of image acquisition, it is often
difficult to find an efficient method for achieving accurate land-cover
classification with high-resolution and heterogeneous remote sensing images. In
this paper, we propose a scheme to apply deep model obtained from labeled
land-cover dataset to classify unlabeled HRRS images. The main idea is to rely
on deep neural networks for presenting the contextual information contained in
different types of land-covers and propose a pseudo-labeling and sample
selection scheme for improving the transferability of deep models. More
precisely, a deep Convolutional Neural Networks is first pre-trained with a
well-annotated land-cover dataset, referred to as the source data. Then, given
a target image with no labels, the pre-trained CNN model is utilized to
classify the image in a patch-wise manner. The patches with high confidence are
assigned with pseudo-labels and employed as the queries to retrieve related
samples from the source data. The pseudo-labels confirmed with the retrieved
results are regarded as supervised information for fine-tuning the pre-trained
deep model. To obtain a pixel-wise land-cover classification with the target
image, we rely on the fine-tuned CNN and develop a hybrid classification by
combining patch-wise classification and hierarchical segmentation. In addition,
we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite
images for CNN pre-training. Experiments on multi-source HRRS images show
encouraging results and demonstrate the applicability of the proposed scheme to
land-cover classification
Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras
Sky/cloud images captured by ground-based cameras (a.k.a. whole sky imagers)
are increasingly used nowadays because of their applications in a number of
fields, including climate modeling, weather prediction, renewable energy
generation, and satellite communications. Due to the wide variety of cloud
types and lighting conditions in such images, accurate and robust segmentation
of clouds is challenging. In this paper, we present a supervised segmentation
framework for ground-based sky/cloud images based on a systematic analysis of
different color spaces and components, using partial least squares (PLS)
regression. Unlike other state-of-the-art methods, our proposed approach is
entirely learning-based and does not require any manually-defined parameters.
In addition, we release the Singapore Whole Sky IMaging SEGmentation Database
(SWIMSEG), a large database of annotated sky/cloud images, to the research
community
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box
Currently, reliable and accurate ship detection in optical remote sensing
images is still challenging. Even the state-of-the-art convolutional neural
network (CNN) based methods cannot obtain very satisfactory results. To more
accurately locate the ships in diverse orientations, some recent methods
conduct the detection via the rotated bounding box. However, it further
increases the difficulty of detection, because an additional variable of ship
orientation must be accurately predicted in the algorithm. In this paper, a
novel CNN-based ship detection method is proposed, by overcoming some common
deficiencies of current CNN-based methods in ship detection. Specifically, to
generate rotated region proposals, current methods have to predefine
multi-oriented anchors, and predict all unknown variables together in one
regression process, limiting the quality of overall prediction. By contrast, we
are able to predict the orientation and other variables independently, and yet
more effectively, with a novel dual-branch regression network, based on the
observation that the ship targets are nearly rotation-invariant in remote
sensing images. Next, a shape-adaptive pooling method is proposed, to overcome
the limitation of typical regular ROI-pooling in extracting the features of the
ships with various aspect ratios. Furthermore, we propose to incorporate
multilevel features via the spatially-variant adaptive pooling. This novel
approach, called multilevel adaptive pooling, leads to a compact feature
representation more qualified for the simultaneous ship classification and
localization. Finally, detailed ablation study performed on the proposed
approaches is provided, along with some useful insights. Experimental results
demonstrate the great superiority of the proposed method in ship detection
CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation
We analyze clouds in the earth's atmosphere using ground-based sky cameras.
An accurate segmentation of clouds in the captured sky/cloud image is
difficult, owing to the fuzzy boundaries of clouds. Several techniques have
been proposed that use color as the discriminatory feature for cloud detection.
In the existing literature, however, analysis of daytime and nighttime images
is considered separately, mainly because of differences in image
characteristics and applications. In this paper, we propose a light-weight
deep-learning architecture called CloudSegNet. It is the first that integrates
daytime and nighttime (also known as nychthemeron) image segmentation in a
single framework, and achieves state-of-the-art results on public databases.Comment: Published in IEEE Geoscience and Remote Sensing Letters, 201
A Survey on Object Detection in Optical Remote Sensing Images
Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.Comment: This manuscript is the accepted version for ISPRS Journal of
Photogrammetry and Remote Sensin
Exploring Models and Data for Remote Sensing Image Caption Generation
Inspired by recent development of artificial satellite, remote sensing images
have attracted extensive attention. Recently, noticeable progress has been made
in scene classification and target detection.However, it is still not clear how
to describe the remote sensing image content with accurate and concise
sentences. In this paper, we investigate to describe the remote sensing images
with accurate and flexible sentences. First, some annotated instructions are
presented to better describe the remote sensing images considering the special
characteristics of remote sensing images. Second, in order to exhaustively
exploit the contents of remote sensing images, a large-scale aerial image data
set is constructed for remote sensing image caption. Finally, a comprehensive
review is presented on the proposed data set to fully advance the task of
remote sensing caption. Extensive experiments on the proposed data set
demonstrate that the content of the remote sensing image can be completely
described by generating language descriptions. The data set is available at
https://github.com/201528014227051/RSICD_optimalComment: 14 pages, 8 figure
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Recently, the advancement of deep learning in discriminative feature learning
from 3D LiDAR data has led to rapid development in the field of autonomous
driving. However, automated processing uneven, unstructured, noisy, and massive
3D point clouds is a challenging and tedious task. In this paper, we provide a
systematic review of existing compelling deep learning architectures applied in
LiDAR point clouds, detailing for specific tasks in autonomous driving such as
segmentation, detection, and classification. Although several published
research papers focus on specific topics in computer vision for autonomous
vehicles, to date, no general survey on deep learning applied in LiDAR point
clouds for autonomous vehicles exists. Thus, the goal of this paper is to
narrow the gap in this topic. More than 140 key contributions in the recent
five years are summarized in this survey, including the milestone 3D deep
architectures, the remarkable deep learning applications in 3D semantic
segmentation, object detection, and classification; specific datasets,
evaluation metrics, and the state of the art performance. Finally, we conclude
the remaining challenges and future researches.Comment: 21 pages, submitted to IEEE Transactions on Neural Networks and
Learning System
Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images
We perform fine-grained land use mapping at the city scale using ground-level
images. Mapping land use is considerably more difficult than mapping land cover
and is generally not possible using overhead imagery as it requires close-up
views and seeing inside buildings. We postulate that the growing collections of
georeferenced, ground-level images suggest an alternate approach to this
geographic knowledge discovery problem. We develop a general framework that
uses Flickr images to map 45 different land-use classes for the City of San
Francisco. Individual images are classified using a novel convolutional neural
network containing two streams, one for recognizing objects and another for
recognizing scenes. This network is trained in an end-to-end manner directly on
the labeled training images. We propose several strategies to overcome the
noisiness of our user-generated data including search-based training set
augmentation and online adaptive training. We derive a ground truth map of San
Francisco in order to evaluate our method. We demonstrate the effectiveness of
our approach through geo-visualization and quantitative analysis. Our framework
achieves over 29% recall at the individual land parcel level which represents a
strong baseline for the challenging 45-way land use classification problem
especially given the noisiness of the image data
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?
Recently, deep convolutional neural network (DCNN) achieved increasingly
remarkable success and rapidly developed in the field of natural image
recognition. Compared with the natural image, the scale of remote sensing image
is larger and the scene and the object it represents are more macroscopic. This
study inquires whether remote sensing scene and natural scene recognitions
differ and raises the following questions: What are the key factors in remote
sensing scene recognition? Is the DCNN recognition mechanism centered on object
recognition still applicable to the scenarios of remote sensing scene
understanding? We performed several experiments to explore the influence of the
DCNN structure and the scale of remote sensing scene understanding from the
perspective of scene complexity. Our experiment shows that understanding a
complex scene depends on an in-depth network and multiple-scale perception.
Using a visualization method, we qualitatively and quantitatively analyze the
recognition mechanism in a complex remote sensing scene and demonstrate the
importance of multi-objective joint semantic support
- …