Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Green synthesis of nickel oxide nanoparticles using <i>Calotropis procera</i> leaf extract for visible-light photocatalytic degradation of RBV-5R dye.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering·2026

Same author

A decision-making framework based on complex fuzzy bipolar soft sets for investment strategy optimization.

Scientific reports·2026

Same author

Metal(loid)s Contamination in the Soil-Rice System: Health Risk Assessment Based on Source Analysis and Monte Carlo Simulation.

Journal of applied toxicology : JAT·2026

Same author

Novel Variants in PUS7 Associated With Intellectual Disability and Growth Retardation: Expanding the Clinical Spectrum in 13 Patients.

Clinical genetics·2026

Same author

miR408-5p and miR408-3p cooperatively reduce cadmium uptake and accumulation in rice.

PLoS biology·2026

Same author

Overexpression of the <i>FTSH</i> gene <i>GH_DO1G0225.1</i> enhances resistance against heat stress in cotton (<i>Gossypium hirsutum</i> L.).

Frontiers in genetics·2026

Search research articles

Related Experiment Video

Updated: Mar 18, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data.

Salman Qadri¹, Dost Muhammad Khan¹, Farooq Ahmad²

¹Department of CS & IT, The Islamia University of Bahawalpur, Punjab 63100, Pakistan.

Biomed Research International

|July 5, 2016

Summary

This study highlights the effectiveness of machine vision for land cover classification. The novel spectra-statistical framework achieved high accuracy using multispectral and texture data with artificial neural networks.

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Mar 18, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Remote Sensing
Computer Vision
Data Science

Background:

Accurate land cover classification is crucial for environmental monitoring and resource management.
Traditional methods often struggle with subjective land cover types.
Machine vision offers advanced capabilities for image analysis and feature extraction.

Purpose of the Study:

To evaluate the importance of a machine vision approach for classifying five distinct land cover types.
To introduce a novel spectra-statistical framework for accurate land cover classification.
To compare the classification performance of multispectral and texture data.

Main Methods:

Acquired multispectral data using a handheld multispectral radiometer (five spectral bands).
Extracted 229 texture features from digital camera images, reducing to 30 discriminant features using Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI).
Employed nonlinear discriminant analysis for texture data clustering and linear discriminant analysis for multispectral data, followed by classification using an artificial neural network (ANN).

Main Results:

Achieved 91.332% accuracy for texture data classification using an 80-20 cross-validation method.
Attained 96.40% accuracy for multispectral data classification using the same cross-validation method.
Demonstrated the superior performance of multispectral data over texture data for the studied land cover types.

Conclusions:

The machine vision approach, particularly using multispectral data, is highly effective for land cover classification.
The developed spectra-statistical framework provides accurate classification of diverse land cover types.
Artificial neural networks are suitable for classifying land cover data derived from both spectral and textural features.