We developed convolutional neural network (CNN) models using drone pictures to estimate vineyard leaf area index (LAI) and percent canopy cover. These parameters are traditionally measured using hand-held devices (e.g., AccuPAR for LAI and Ceptometer for percent canopy cover) and calculated manually, which is labor intensive and hard to apply to large-scale areas. We collected airborne images or videos by flying a low-altitude drone with a built-in digital camera over a large-scale vineyard. The airborne images convey all necessary information for developing CNN models. To date, we have collected data from the same vineyard over a couple of years. The ground truth values were manually measured using AccuPAR and Ceptometer at the same time of airborne imaging. Specifically, we trained five CNN models to estimate percent canopy cover and leaf area index (LAI). The estimated results over a large vineyard will help guide planting intercrops or cover crops to prevent soil erosion, calculating the correct amounts of expected residue in fall, and foliar sprays of pesticides and fungicides, and characterization of vegetation-atmosphere interactions.
KEYWORDS: Wavelets, Detection and tracking algorithms, Optical filters, Denoising, Signal processing, Sensors, Hyperspectral imaging, Hyperspectral target detection, Image filtering, Signal to noise ratio
In this paper, we propose an algorithm for detecting man made targets in hyperspectral imagery using correlation based
detection after wavelet domain filtering. In the proposed method, each spectral pixel in noisy hyperspectral data cube is
filtered by wavelet domain filtering. Wavelet domain filtering looks at every spectral pixel as noisy signal and filter out
noise through wavelet shrinkage based method. Then correlation between the provided target spectral signature and
spectral signal from data cube is calculated. The algorithm scans each pixel in data cube then calculates correlation with
target signature. The process yields correlation image. Applying threshold operation for correlation image provides
detection image. The detection performance of the algorithm is tested with several hyperspectral datasets. Using ROC
analysis and comparing with ground truth image, it is observed that wavelet based filtering provides better detection
performance for noisy data. The simulation results indicate that the proposed algorithm efficiently detects object of
interest in all datasets.
This paper presents an Independent Component Analysis (ICA) based linear unmixing algorithm for target detection
application. ICA is a relatively new method that attempts to separate statistically independent sources from a mixed
dataset. The developed algorithm contains two steps. In the first step, ICA based linear unmixing is used to
discriminate statistically independent sources to determine
end-members in a given dataset as well as their
corresponding abundance images. In the second step, unmixing results are analyzed to identify abundance images that
correspond to the target class. The performance of the developed algorithm has been evaluated with several real life
hyperspectral image datasets.
Now a days detection of man made or natural object using hyperspectral imagery is a great interest of both
civilian and military application. With compared to other method, hyperspectral image processing can
detect both full pixel and subpixel object by analyzing the fine details of both target and background
signatures. There are lots of algorithms to detect hyperspectral full pixel targets. There are also methods to
detect subpixel target [1-2]. In this paper we have presented an automated method to detect hyperspectral
targets using Linear Mixing Model (LMM) [4]. In our method we estimated the background endmember
signatures Vertex Component Analysis which is a fast algorithm to unmix hyperspectral data [6] after
removing target like pixels. Sensor noise is modeled as a Gaussian random vector with uncorrelated
components of equal variance. This paper provides a complete and self-contained theoretical derivation of a
subpixel target detector using the Generalized Likelihood Ratio Test (GLRT) approach and the LMM [4].
This paper proposes an algorithm for detecting object of interest in hyperspectral imagery using the principal
component analysis (PCA) as preprocessing and spectral angle mapping. PCA has found many applications in
multivariate statistics which is very useful method to extract features from higher dimensional dataset. Spectral
angle mapper is a widely used method for similarity measurement of spectral signatures. The developed algorithm
includes two main processing steps: preprocessing of hyperspectral dataset and detection of object of interest. To
improve the detection rate, the preprocessing step is implemented which processes hyperspectral data with a
median filter (MF). Then, principal component transform is applied to the output of the MF filter which completes
the preprocessing step. Spectral angle mapping is then applied to the output of preprocessing step to detect object
with the signature of interest. We have tested the developed detection algorithm with two different hyperspectral
datasets. The simulation results indicate that the proposed algorithm efficiently detects object of interest in all
datasets.
Detection of man-made or natural object using hyperspectral sensor has attracted great research interest
recently, because it can detect both the full pixel and subpixel objects by analyzing the fine details of the
object as well as the background signatures. Several algorithms have been proposed in the literature to
detect hyperspectral full pixel object and subpixel object. The objective of this paper is to develop an
automated method to detect hyperspectral objects using the linear mixing model (LMM). Here the
background is estimated from the endmember signatures using the principal component analysis (PCA) and
the vertex component analysis (VCA), which is a fast algorithm to unmix hyperspectral data. Sensor noise
is modeled as a Gaussian random vector with uncorrelated components of equal variance. A detail
theoretical analysis of the proposed subpixel object detection algorithm has been provided using the
generalized likelihood ratio test and the LMM approaches. For multipixel or resolved objects, the detection
can exploit both the spatial and spectral properties. But the detection of subpixel objects can only be
achieved by exploiting the spectral properties. Since the spectrum of a subpixel object is mixed with that of
the background, the resultant pixel contains a combined spectral signature and hence requires some kind of
(linear or nonlinear) separation of the constituent elements called the unmixing process. This paper focuses
on the algorithms for detection of low probability objects, both with full pixel and subpixel. The
endmembers have been evaluated assuming that only the data cube and the object signature are given. To
estimate the background subspace, we have applied the PCA algorithm to the data cube and finally applied
the VCA algorithm in order to estimate the background subspace signatures.
The main objective of this article is to develop techniques of robust control and their applications to communication and transportation networks. Three following problems will be studied in detail: (1) Resource Sharing: These systems belong to a class of hybrid systems where the plant to be controlled is a continuous drift free system, and the controller is implemented as a FSM. (2) Admission Control. Ramp metering control problem can be studied in either distributed or lumped parameter setting. These two settings to an isolated case have been studied by the authors and solutions for these have been obtained. (3) Traffic Routing. Traditionally both kinds of network systems have been treated using static optimization methods for congestion control. The traffic routing problem for both types of networks can be solved either in user-equilibrium setting or system-optimal. In user-equilibrium, the aim of the controller is to obtain equal travel times on alternate routes. In system-optimal, the aim is to obtain minimum total travel time on the entire network.
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