Principle Component Analysis for Crop Discrimination using Hyperspectral Remote Sensing Data

Crop discrimination is still very challenging issue for researcher because of spectral reflectance similarity captured in non-imaging data. The objective of this research work is to focus on crop discrimination challenge. We have used ASD FieldSpec4 Spectroradiometer for collection of leaf samples of four crops Wheat, Jowar, Bajara and Maize. We used vegetation indices and some spectral reflectance band for featuring our dataset. We applied Principle Component Analysis (PCA) for discrimination and it has been observed that when we use first and second principle component, it will give poor result but if third principle component is used then we get accurate and fine results.

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Best Band Selection using Principle Component Analysis Algorithm from Remote Sensing Data

AL-Rafidain Journal of Computer Sciences and Mathematics ◽

10.33899/csmj.2010.163933 ◽

2010 ◽

Vol 7 (3) ◽

pp. 135-144

Author(s):

Maha Hasso ◽

Mona Siddiq

Keyword(s):

Remote Sensing ◽

Principle Component Analysis ◽

Remote Sensing Data ◽

Component Analysis ◽

Band Selection ◽

Analysis Algorithm ◽

Sensing Data ◽

Principle Component

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Crop Discrimination using Non-Imaging Hyperspectral Data

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e2802.0610521 ◽

2021 ◽

Vol 10 (5) ◽

pp. 269-273

Author(s):

Pooja Vinod Janse ◽

Ratnadeep R. Deshmukh

Keyword(s):

Classification Accuracy ◽

Spectral Reflectance ◽

Visual Analysis ◽

Vegetation Indices ◽

Research Work ◽

Hyperspectral Data ◽

Support Vector ◽

Spectral Bands ◽

Crop Type ◽

Crop Discrimination

Crop type discrimination is still very challenging task for researchers using non-imaging hyperspectral data. It is because of spectral reflectance similarity between crops. In this research work we have discriminated between four crops wheat, jowar, bajara and maize. We have tried to overcome the problems which have been faced my researchers. Initially by visual analysis we have selected 22 reflectance band which shows the absorption property of particular molecules and classification technique is applied, but it has given us very poor result of classification. We observed only 24% classification accuracy. So we considered nine vegetation indices along with spectral bands and achieved better classification accuracy. ASD FieldSpec 4 Spectroradiometer device is used for capturing spectral reflectance data. We calculated nine different vegetation indices and some selective reflectance bands are used for crop classification. We have used Support Vector Machine (SVM) for classification.

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ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

Recent Patents on Computer Science ◽

10.2174/2213275912666190819113541 ◽

2019 ◽

Vol 12 ◽

Cited By ~ 1

Author(s):

Basavaraj N Hiremath ◽

Malini M Patilb

Keyword(s):

Feature Extraction ◽

Computer Program ◽

Principle Component Analysis ◽

Voice Recognition ◽

Component Analysis ◽

Recognition System ◽

Principle Component ◽

Whole Process ◽

The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

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Four challenges for cognitive neuroscience and the cortico-hippocampal division of memory

Behavioral and Brain Sciences ◽

10.1017/s0140525x03370153 ◽

2003 ◽

Vol 26 (6) ◽

pp. 681-682

Author(s):

Harry Howard

Keyword(s):

Independent Component Analysis ◽

Recent Work ◽

Cognitive Neuroscience ◽

Principle Component Analysis ◽

Pattern Analysis ◽

Component Analysis ◽

Independent Component ◽

Principle Component ◽

Complementary Methods ◽

Current State

Jackendoff's criticisms of the current state of theorization in cognitive neuroscience are defused by recent work on the computational complementarity of the hippocampus and neocortex. Such considerations lead to a grounding of Jackendoff's processing model in the complementary methods of pattern analysis effected by independent component analysis (ICA) and principle component analysis (PCA).

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