105 research outputs found
A novel approach for multispectral satellite image classification based on the bat algorithm
Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems
Metacognitive Decision Making Framework for Multi-UAV Target Search Without Communication
This paper presents a new Metacognitive Decision Making (MDM) framework
inspired by human-like metacognitive principles. The MDM framework is
incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized
stochastic search without communication for detecting stationary targets
(fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with multiple
sensors (varying sensing capability) and search for targets in a largely
unknown area. The MDM framework consists of a metacognitive component and a
self-cognitive component. The metacognitive component helps to self-regulate
the search with multiple sensors addressing the issues of
"which-sensor-to-use", "when-to-switch-sensor", and "how-to-search". Each
sensor possesses inverse characteristics for the sensing attributes like
sensing range and accuracy. Based on the information gathered by multiple
sensors carried by each UAV, the self-cognitive component regulates different
levels of stochastic search and switching levels for effective searching. The
lower levels of search aim to localize the search space for the possible
presence of a target (detection) with different sensors. The highest level of a
search exploits the search space for target confirmation using the sensor with
the highest accuracy among all sensors. The performance of the MDM framework
with two sensors having low accuracy with wide range sensor for detection and
increased accuracy with low range sensor for confirmation is evaluated through
Monte-Carlo simulations and compared with six multi-UAV stochastic search
algorithms (three self-cognitive searches and three self and social-cognitive
based search). The results indicate that the MDM framework is efficient in
detecting and confirming targets in an unknown environment.Comment: 12 pages, 9 figures, 9 table
A novel approach for multispectral satellite image classification based on the bat algorithm
Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems
Automatic detection of powerlines in UAV remote sensed images
Powerline detection is one of the important applications of Uninhabited Aerial Vehicle (UAV ) based remote sensing. In this paper, powerlines are detected from UAV remote sensed images. The images are acquired from a Quad rotor UAV fitted with a GoPro® camera. In the proposed method pixel intensity-based clustering is performed followed by morphological operations. K-means clustering is applied for clustering. The number of clusters to be used in k-means clustering is automatically generated using Davies-Bouldin (DB) index. Further, the clustered data is processed to improvise the extraction using mathematical morphological operations. Performance of powerline extraction is analysed using confusion matrix method. In the observed results of powerline extraction using DB index, evaluation features derived from confusion matrix is close to one, indicating good classification
Towards deep generation of guided wave representations for composite materials
Laminated composite materials are widely used in most fields of engineering.
Wave propagation analysis plays an essential role in understanding the
short-duration transient response of composite structures. The forward
physics-based models are utilized to map from elastic properties space to wave
propagation behavior in a laminated composite material. Due to the
high-frequency, multi-modal, and dispersive nature of the guided waves, the
physics-based simulations are computationally demanding. It makes property
prediction, generation, and material design problems more challenging. In this
work, a forward physics-based simulator such as the stiffness matrix method is
utilized to collect group velocities of guided waves for a set of composite
materials. A variational autoencoder (VAE)-based deep generative model is
proposed for the generation of new and realistic polar group velocity
representations. It is observed that the deep generator is able to reconstruct
unseen representations with very low mean square reconstruction error. Global
Monte Carlo and directional equally-spaced samplers are used to sample the
continuous, complete and organized low-dimensional latent space of VAE. The
sampled point is fed into the trained decoder to generate new polar
representations. The network has shown exceptional generation capabilities. It
is also seen that the latent space forms a conceptual space where different
directions and regions show inherent patterns related to the generated
representations and their corresponding material properties
Explainable machine learning to enable high-throughput electrical conductivity optimization of doped conjugated polymers
The combination of high-throughput experimentation techniques and machine
learning (ML) has recently ushered in a new era of accelerated material
discovery, enabling the identification of materials with cutting-edge
properties. However, the measurement of certain physical quantities remains
challenging to automate. Specifically, meticulous process control,
experimentation and laborious measurements are required to achieve optimal
electrical conductivity in doped polymer materials. We propose a ML approach,
which relies on readily measured absorbance spectra, to accelerate the workflow
associated with measuring electrical conductivity. The first ML model
(classification model), accurately classifies samples with a conductivity >~25
to 100 S/cm, achieving a maximum of 100% accuracy rate. For the subset of
highly conductive samples, we employed a second ML model (regression model), to
predict their conductivities, yielding an impressive test R2 value of 0.984. To
validate the approach, we showed that the models, neither trained on the
samples with the two highest conductivities of 498 and 506 S/cm, were able to,
in an extrapolative manner, correctly classify and predict them at satisfactory
levels of errors. The proposed ML workflow results in an improvement in the
efficiency of the conductivity measurements by 89% of the maximum achievable
using our experimental techniques. Furthermore, our approach addressed the
common challenge of the lack of explainability in ML models by exploiting
bespoke mathematical properties of the descriptors and ML model, allowing us to
gain corroborated insights into the spectral influences on conductivity.
Through this study, we offer an accelerated pathway for optimizing the
properties of doped polymer materials while showcasing the valuable insights
that can be derived from purposeful utilization of ML in experimental science.Comment: 33 Pages, 17 figure
SIFT-FANN: An efficient framework for spatio-spectral fusion of satellite images
Image fusion techniques are widely used for remote sensing data. A special application is for using low resolution multi-spectral image with high resolution panchromatic image to obtain an image having both spectral and spatial information. Alignment of images to be fused is a step prior to image fusion. This is achieved by registering the images. This paper proposes the methods involving Fast Approximate Nearest Neighbor (FANN) for automatic registration of satellite image (reference image) prior to fusion of low spatial resolution multi-spectral QuickBird satellite image (sensed image) with high spatial resolution panchromatic QuickBird satellite image. In the registration steps, Scale Invariant Feature Transform (SIFT) is used to extract key points from both images. The keypoints are then matched using the automatic tuning algorithm, namely, FANN. This algorithm automatically selects the most appropriate indexing algorithm for the dataset. The indexed features are then matched using approximate nearest neighbor. Further, Random Sample Consensus (RanSAC) is used for further filtering to obtain only the inliers and co-register the images. The images are then fused using Intensity Hue Saturation (IHS) transform based technique to obtain a high spatial resolution multi-spectral image. The results show that the quality of fused images obtained using this algorithm is computationally efficient
DRBM-ClustNet: A Deep Restricted Boltzmann-Kohonen Architecture for Data Clustering
A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering
termed as DRBM-ClustNet is proposed. This core-clustering engine consists of a
Deep Restricted Boltzmann Machine (DRBM) for processing unlabeled data by
creating new features that are uncorrelated and have large variance with each
other. Next, the number of clusters are predicted using the Bayesian
Information Criterion (BIC), followed by a Kohonen Network-based clustering
layer. The processing of unlabeled data is done in three stages for efficient
clustering of the non-linearly separable datasets. In the first stage, DRBM
performs non-linear feature extraction by capturing the highly complex data
representation by projecting the feature vectors of dimensions into
dimensions. Most clustering algorithms require the number of clusters to be
decided a priori, hence here to automate the number of clusters in the second
stage we use BIC. In the third stage, the number of clusters derived from BIC
forms the input for the Kohonen network, which performs clustering of the
feature-extracted data obtained from the DRBM. This method overcomes the
general disadvantages of clustering algorithms like the prior specification of
the number of clusters, convergence to local optima and poor clustering
accuracy on non-linear datasets. In this research we use two synthetic
datasets, fifteen benchmark datasets from the UCI Machine Learning repository,
and four image datasets to analyze the DRBM-ClustNet. The proposed framework is
evaluated based on clustering accuracy and ranked against other
state-of-the-art clustering methods. The obtained results demonstrate that the
DRBM-ClustNet outperforms state-of-the-art clustering algorithms.Comment: 14 pages, 7 figure
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