Deep learning architectures for land cover classification using red and near-infrared satellite images

2019 ◽  
Vol 78 (13) ◽  
pp. 18379-18394 ◽  
Author(s):  
Anju Unnikrishnan ◽  
V. Sowmya ◽  
K. P. Soman
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Satellite Images ◽  
Near Infrared ◽  
Land Cover Classification ◽  
Learning Architectures

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.

Land Cover Classification from a Mapping Perspective: Pixelwise Supervision in the Deep Learning Era

2021 ◽  
Author(s):  
Thorsten Wilhelm ◽  
Dominik Kossmann
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Land Cover Classification

How up-scaling of remote-sensing images affects land-cover classification by comparison with multiscale satellite images

2018 ◽  
Vol 40 (7) ◽  
pp. 2784-2810 ◽  
Author(s):  
Kaijian Xu ◽  
Qingjiu Tian ◽  
Yanjun Yang ◽  
Jibo Yue ◽  
Shaofei Tang
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Satellite Images ◽  
Land Cover Classification ◽  
Remote Sensing Images ◽  
Up Scaling

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.

DeepSight: Land Use and Land Cover Classification Using Satellite Images

2021 ◽  
Author(s):  
Sumedh Ghavat ◽  
Parth Kodnani ◽  
Harshita Singh ◽  
Jayashree Hajgude
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Land Cover Classification

Object-Oriented Land Cover Classification Based on Two Satellite Images Obtained in One Dry Season in Cambodia

2007 ◽  
pp. 149-158 ◽  
Author(s):  
Naoyuki Furuya ◽  
Hideki Saito ◽  
Sam Preap ◽  
Bora Tith ◽  
Makara Meas
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Object Oriented ◽  
Land Cover Classification ◽  
Dry Season

scholarly journals Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

2020 ◽  
Vol 12 (6) ◽  
pp. 959 ◽  
Author(s):  
Mohammad Pashaei ◽  
Hamid Kamangir ◽  
Michael J. Starek ◽  
Philippe Tissot
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Unmanned Aircraft ◽  
High Temporal Resolution ◽  
Semantic Image Segmentation ◽  
Image Labeling ◽  
Training Samples ◽  
Learning Architectures

Deep learning has already been proved as a powerful state-of-the-art technique for many image understanding tasks in computer vision and other applications including remote sensing (RS) image analysis. Unmanned aircraft systems (UASs) offer a viable and economical alternative to a conventional sensor and platform for acquiring high spatial and high temporal resolution data with high operational flexibility. Coastal wetlands are among some of the most challenging and complex ecosystems for land cover prediction and mapping tasks because land cover targets often show high intra-class and low inter-class variances. In recent years, several deep convolutional neural network (CNN) architectures have been proposed for pixel-wise image labeling, commonly called semantic image segmentation. In this paper, some of the more recent deep CNN architectures proposed for semantic image segmentation are reviewed, and each model’s training efficiency and classification performance are evaluated by training it on a limited labeled image set. Training samples are provided using the hyper-spatial resolution UAS imagery over a wetland area and the required ground truth images are prepared by manual image labeling. Experimental results demonstrate that deep CNNs have a great potential for accurate land cover prediction task using UAS hyper-spatial resolution images. Some simple deep learning architectures perform comparable or even better than complex and very deep architectures with remarkably fewer training epochs. This performance is especially valuable when limited training samples are available, which is a common case in most RS applications.

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