713 research outputs found
A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images
A partially unsupervised approach to the classification of multitemporal remote-sensing images is presented. Such an approach allows the automatic classification of a remote-sensing image for which training data are not available, drawing on the information derived from an image acquired in the same area at a previous time. In particular, the proposed technique is based on a cascade classifier approach and on a specific formulation of the expectation-maximization (EM) algorithm used for the unsupervised estimation of the statistical parameters of the image to be classified. The results of experiments carried out on a multitemporal data set confirm the validity of the proposed approach
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
Change detection is one of the central problems in earth observation and was
extensively investigated over recent decades. In this paper, we propose a novel
recurrent convolutional neural network (ReCNN) architecture, which is trained
to learn a joint spectral-spatial-temporal feature representation in a unified
framework for change detection in multispectral images. To this end, we bring
together a convolutional neural network (CNN) and a recurrent neural network
(RNN) into one end-to-end network. The former is able to generate rich
spectral-spatial feature representations, while the latter effectively analyzes
temporal dependency in bi-temporal images. In comparison with previous
approaches to change detection, the proposed network architecture possesses
three distinctive properties: 1) It is end-to-end trainable, in contrast to
most existing methods whose components are separately trained or computed; 2)
it naturally harnesses spatial information that has been proven to be
beneficial to change detection task; 3) it is capable of adaptively learning
the temporal dependency between multitemporal images, unlike most of algorithms
that use fairly simple operation like image differencing or stacking. As far as
we know, this is the first time that a recurrent convolutional network
architecture has been proposed for multitemporal remote sensing image analysis.
The proposed network is validated on real multispectral data sets. Both visual
and quantitative analysis of experimental results demonstrates competitive
performance in the proposed mode
Multitemporal Relearning with Convolutional LSTM Models for Land Use Classification
In this article, we present a novel hybrid framework, which integrates spatial–temporal semantic segmentation with postclassification relearning, for multitemporal land use and land cover (LULC) classification based on very high resolution (VHR) satellite imagery. To efficiently obtain optimal multitemporal LULC classification maps, the hybrid framework utilizes a spatial–temporal semantic segmentation model to harness temporal dependency for extracting high-level spatial–temporal features. In addition, the principle of postclassification relearning is adopted to efficiently optimize model output. Thereby, the initial outcome of a semantic segmentation model is provided to a subsequent model via an extended input space to guide the learning of discriminative feature representations in an end-to-end fashion. Last, object-based voting is coupled with postclassification relearning for coping with the high intraclass and low interclass variances. The framework was tested with two different postclassification relearning strategies (i.e., pixel-based relearning and object-based relearning) and three convolutional neural network models, i.e., UNet, a simple Convolutional LSTM, and a UNet Convolutional-LSTM. The experiments were conducted on two datasets with LULC labels that contain rich semantic information and variant building morphologic features (e.g., informal settlements). Each dataset contains four time steps from WorldView-2 and Quickbird imagery. The experimental results unambiguously underline that the proposed framework is efficient in terms of classifying complex LULC maps with multitemporal VHR images
Classification software technique assessment
A catalog of software options is presented for the use of local user communities to obtain software for analyzing remotely sensed multispectral imagery. The resources required to utilize a particular software program are described. Descriptions of how a particular program analyzes data and the performance of that program for an application and data set provided by the user are shown. An effort is made to establish a statistical performance base for various software programs with regard to different data sets and analysis applications, to determine the status of the state-of-the-art
Multistage classification of multispectral Earth observational data: The design approach
An algorithm is proposed which predicts the optimal features at every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. Results comparing probabilities of error predicted by the proposed algorithm as a function of dimensionality as compared to experimental observations are shown for aircraft and LANDSAT data. Results are obtained for both real and simulated data. Finally, two binary tree examples which use the algorithm are presented to illustrate the usefulness of the procedure
The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale
Thesis submitted to the Instituto Superior de EstatÃstica e Gestão de
Informação da Universidade Nova de Lisboa in partial fulfillment of the
requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information SystemsImaging and sensing technologies are constantly evolving so that, now, the latest
generations of satellites commonly provide with Earth’s surface snapshots at very short
sampling periods (i.e. daily images). It is unquestionable that this tendency towards
continuous time observation will broaden up the scope of remotely sensed activities.
Inevitable also, such increasing amount of information will prompt methodological
approaches that combine digital image processing techniques with time series analysis for
the characterization of land cover distribution and monitoring of its dynamics on a frequent
basis. Nonetheless, quantitative analyses that convey the proficiency of three-dimensional
satellite images data sets (i.e. spatial, spectral and temporal) for the automatic mapping of
land cover and land cover time evolution have not been thoroughly explored. In this
dissertation, we investigate the usefulness of multispectral time series sets of medium spatial
resolution satellite images for the regular land cover characterization at the national scale.
This study is carried out on the territory of Continental Portugal and exploits satellite
images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and
MEdium Resolution Imaging Spectrometer (MERIS). In detail, we first focus on the analysis
of the contribution of multitemporal information from multispectral satellite images for the
automatic land cover classes’ discrimination. The outcomes show that multispectral
information contributes more significantly than multitemporal information for the automatic
classification of land cover types. In the sequence, we review some of the most important
steps that constitute a standard protocol for the automatic land cover mapping from satellite
images. Moreover, we delineate a methodological approach for the production and
assessment of land cover maps from multitemporal satellite images that guides us in the
production of a land cover map with high thematic accuracy for the study area. Finally, we
develop a nonlinear harmonic model for fitting multispectral reflectances and vegetation
indices time series from satellite images for numerous land cover classes. The simplified
multitemporal information retrieved with the model proves adequate to describe the main
land cover classes’ characteristics and to predict the time evolution of land cover classes’individuals
Basic research planning in mathematical pattern recognition and image analysis
Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis
Multisource and Multitemporal Data Fusion in Remote Sensing
The sharp and recent increase in the availability of data captured by
different sensors combined with their considerably heterogeneous natures poses
a serious challenge for the effective and efficient processing of remotely
sensed data. Such an increase in remote sensing and ancillary datasets,
however, opens up the possibility of utilizing multimodal datasets in a joint
manner to further improve the performance of the processing approaches with
respect to the application at hand. Multisource data fusion has, therefore,
received enormous attention from researchers worldwide for a wide variety of
applications. Moreover, thanks to the revisit capability of several spaceborne
sensors, the integration of the temporal information with the spatial and/or
spectral/backscattering information of the remotely sensed data is possible and
helps to move from a representation of 2D/3D data to 4D data structures, where
the time variable adds new information as well as challenges for the
information extraction algorithms. There are a huge number of research works
dedicated to multisource and multitemporal data fusion, but the methods for the
fusion of different modalities have expanded in different paths according to
each research community. This paper brings together the advances of multisource
and multitemporal data fusion approaches with respect to different research
communities and provides a thorough and discipline-specific starting point for
researchers at different levels (i.e., students, researchers, and senior
researchers) willing to conduct novel investigations on this challenging topic
by supplying sufficient detail and references
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