scholarly journals Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data

2020 ◽  
Vol 12 (9) ◽  
pp. 1392 ◽  
Author(s):  
Chiman Kwan ◽  
David Gribben ◽  
Bulent Ayhan ◽  
Sergio Bernabe ◽  
Antonio Plaza ◽  
...  
Keyword(s):  
Land Cover ◽  
Data Storage ◽  
Near Infrared ◽  
Land Cover Classification ◽  
Research Problem ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Lidar Data ◽  
Practical Applications ◽  
Wide Range

Hyperspectral (HS) data have found a wide range of applications in recent years. Researchers observed that more spectral information helps land cover classification performance in many cases. However, in some practical applications, HS data may not be available, due to cost, data storage, or bandwidth issues. Instead, users may only have RGB and near infrared (NIR) bands available for land cover classification. Sometimes, light detection and ranging (LiDAR) data may also be available to assist land cover classification. A natural research problem is to investigate how well land cover classification can be achieved under the aforementioned data constraints. In this paper, we investigate the performance of land cover classification while only using four bands (RGB+NIR) or five bands (RGB+NIR+LiDAR). A number of algorithms have been applied to a well-known dataset (2013 IEEE Geoscience and Remote Sensing Society Data Fusion Contest). One key observation is that some algorithms can achieve better land cover classification performance by using only four bands as compared to that of using all 144 bands in the original hyperspectral data with the help of synthetic bands generated by Extended Multi-attribute Profiles (EMAP). Moreover, LiDAR data do improve the land cover classification performance even further.

scholarly journals Deep Learning for Land Cover Classification Using Only a Few Bands

2020 ◽  
Vol 12 (12) ◽  
pp. 2000 ◽  
Author(s):  
Chiman Kwan ◽  
Bulent Ayhan ◽  
Bence Budavari ◽  
Yan Lu ◽  
Daniel Perez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Data Storage ◽  
Near Infrared ◽  
Learning Algorithms ◽  
Land Cover Classification ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Practical Applications ◽  
Spectral Signatures

There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.

scholarly journals LAND COVER CLASSIFICATION ALOS AVNIR DATA USING IKONOS AS REFERENCE

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Bambang Trisakti ◽  
Dini Oktaviana Ambarwati
Keyword(s):  
Land Cover ◽  
Near Infrared ◽  
Good Accuracy ◽  
Confusion Matrix ◽  
Principal Component ◽  
Land Cover Classification ◽  
Accuracy Evaluation ◽  
Post Processing ◽  
Classification Result ◽  
Training Samples

Abstract.  Advanced Land Observation Satellite (ALOS) is a Japanese satellite equipped with 3  sensors  i.e.,  PRISM,  AVNIR,  and  PALSAR.  The  Advanced  Visible  and  Near  Infrared Radiometer (AVNIR) provides multi spectral sensors ranging from Visible to Near Infrared to observe  land  and  coastal  zones.  It  has  10  meter  spatial  resolution,  which  can  be  used  to map  land  cover  with  a  scale  of 1:25000.  The  purpose  of  this  research  was  to  determineclassification  for  land  cover  mapping  using  ALOS  AVNIR  data.  Training  samples  were collected  for  11  land  cover  classes  from  Bromo  volcano  by  visually  referring  to  very  high resolution  data  of  IKONOS  panchromatic  data.  The  training  samples  were  divided  into samples  for  classification  input  and  samples  for  accuracy  evaluation.  Principal  component analysis (PCA) was conducted for AVNIR data, and the generated PCA bands were classified using Maximum Likehood  Enhanced Neighbor method. The classification result was filtered and  re-classed  into  8  classes.  Misclassifications  were  evaluated  and  corrected  in  the  post processing  stage.  The  accuracy  of  classifications  results,  before  and  after  post  processing, were  evaluated  using  confusion  matrix  method.  The  result  showed  that  Maximum Likelihood  Enhanced  Neighbor  classifier  with  post  processing  can  produce  land  cover classification  result  of  AVNIR  data  with  good  accuracy  (total  accuracy  94%  and  kappa statistic 0.92).  ALOS AVNIR has been proven as a potential satellite data to map land cover in the study area with good accuracy.

scholarly journals Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1090 ◽  
Author(s):  
Gamal ElMasry ◽  
Nasser Mandour ◽  
Salim Al-Rejaie ◽  
Etienne Belin ◽  
David Rousseau
Keyword(s):  
Quality Assessment ◽  
Multispectral Imaging ◽  
Seed Quality ◽  
Hyperspectral Data ◽  
Imaging Systems ◽  
Quality Monitoring ◽  
Electromagnetic Spectrum ◽  
Safety Control ◽  
Practical Applications ◽  
Wide Range

As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.

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