1,323 research outputs found
Satellite image retrieval with pattern spectra descriptors
International audienceThe increasing volume of Earth Observation data calls for appropriate solutions in satellite image retrieval. We address this problem by considering morphological descrip-tors called pattern spectra. Such descriptors are histogram-like structures that contain the information on the distribution of predefined properties (attributes) of image components. They can be computed both at the local and global scale, and are computationally attractive. We demonstrate how they can be embedded in an image retrieval framework and report their promising performances when dealing with a standard satellite image dataset
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Remote sensing image scene classification plays an important role in a wide
range of applications and hence has been receiving remarkable attention. During
the past years, significant efforts have been made to develop various datasets
or present a variety of approaches for scene classification from remote sensing
images. However, a systematic review of the literature concerning datasets and
methods for scene classification is still lacking. In addition, almost all
existing datasets have a number of limitations, including the small scale of
scene classes and the image numbers, the lack of image variations and
diversity, and the saturation of accuracy. These limitations severely limit the
development of new approaches especially deep learning-based methods. This
paper first provides a comprehensive review of the recent progress. Then, we
propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly
available benchmark for REmote Sensing Image Scene Classification (RESISC),
created by Northwestern Polytechnical University (NWPU). This dataset contains
31,500 images, covering 45 scene classes with 700 images in each class. The
proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total
image number, (ii) holds big variations in translation, spatial resolution,
viewpoint, object pose, illumination, background, and occlusion, and (iii) has
high within-class diversity and between-class similarity. The creation of this
dataset will enable the community to develop and evaluate various data-driven
algorithms. Finally, several representative methods are evaluated using the
proposed dataset and the results are reported as a useful baseline for future
research.Comment: This manuscript is the accepted version for Proceedings of the IEE
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
A Novel System for Content-Based Retrieval of Single and Multi-Label High-Dimensional Remote Sensing Images
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a novel content-based remote sensing (RS) image retrieval system that consists of the following. First, an image description method that characterizes both spatial and spectral information content of RS images. Second, a supervised retrieval method that efficiently models and exploits the sparsity of RS image descriptors. The proposed image description method characterizes the spectral content by three different novel spectral descriptors that are: raw pixel values, simple bag of spectral values and the extended bag of spectral values descriptors. To model the spatial content of RS images, we consider the well-known scale invariant feature transform-based bag of visual words approach. With the conjunction of the spatial and the spectral descriptors, RS image retrieval is achieved by a novel sparse reconstruction-based RS image retrieval method. The proposed method considers a novel measure of label likelihood in the framework of sparse reconstruction-based classifiers and generalizes the original sparse classifier to the case both single-label and multi-label RS image retrieval problems. Finally, to enhance retrieval performance, we introduce a strategy to exploit the sensitivity of the sparse reconstruction-based method to different dictionary words. Experimental results obtained on two benchmark archives show the effectiveness of the proposed system.EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEart
Impact of Feature Representation on Remote Sensing Image Retrieval
Remote sensing images are acquired using special platforms, sensors and are classified as aerial, multispectral and hyperspectral images. Multispectral and hyperspectral images are represented using large spectral vectors as compared to normal Red, Green, Blue (RGB) images. Hence, remote sensing image retrieval process from large archives is a challenging task. Remote sensing image retrieval mainly consist of feature representation as first step and finding out similar images to a query image as second step. Feature representation plays important part in the performance of remote sensing image retrieval process. Research work focuses on impact of feature representation of remote sensing images on the performance of remote sensing image retrieval. This study shows that more discriminative features of remote sensing images are needed to improve performance of remote sensing image retrieval process
Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects
Hyperspectral Imaging (HSI) has been extensively utilized in many real-life
applications because it benefits from the detailed spectral information
contained in each pixel. Notably, the complex characteristics i.e., the
nonlinear relation among the captured spectral information and the
corresponding object of HSI data make accurate classification challenging for
traditional methods. In the last few years, Deep Learning (DL) has been
substantiated as a powerful feature extractor that effectively addresses the
nonlinear problems that appeared in a number of computer vision tasks. This
prompts the deployment of DL for HSI classification (HSIC) which revealed good
performance. This survey enlists a systematic overview of DL for HSIC and
compared state-of-the-art strategies of the said topic. Primarily, we will
encapsulate the main challenges of traditional machine learning for HSIC and
then we will acquaint the superiority of DL to address these problems. This
survey breakdown the state-of-the-art DL frameworks into spectral-features,
spatial-features, and together spatial-spectral features to systematically
analyze the achievements (future research directions as well) of these
frameworks for HSIC. Moreover, we will consider the fact that DL requires a
large number of labeled training examples whereas acquiring such a number for
HSIC is challenging in terms of time and cost. Therefore, this survey discusses
some strategies to improve the generalization performance of DL strategies
which can provide some future guidelines
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
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