This project proposes methods for classifying scenes in remote sensing images. It compares the accuracy of traditional bag-of-visual-words (BoVW) models using handcrafted features to a bag-of-convolutional features (BoCF) model using deep learning. It also applies a grey wolf optimizer (GWO) algorithm for image segmentation. Results show BoCF doubled the accuracy of BoVW, and combining BoVW with GWO improved accuracy over BoVW alone. The project concludes more work is needed to better combine remote sensing data and deep learning for classification.

1. Remote Sensing Image Scene Classification
Designed by Manik Batra, Gaurav Singh and Yashvardhan
Under supervision of Dr Mukesh Saraswat
References
1. Gong Cheng, Zhen peng Li, Xi wen Yao, Lei
Guo, and, Zhong liang Wei, “Remote Sensing
Image Scene Classification Using Bag of
Convolutional Features,” IEEE Geoscience and
Remote Sensing Letters, Oct. 2017.
2. Jiang fan Feng, Yuanyuan Liu, and Lin Wu,
“Bag of Visual Words Model with Deep
Spatial Features for Geographical Scene
Classification,” Computational Intelligence and
Neuroscience, June 2017.
3. Mirjalili S, Mir Jalili SM, Lewis A., “Grey
wolf optimizer,” Advances in Engineering
Software, 2014.
4. C. Muro, R. Escobedo, L. Spector, and R.
Coppinger, "Wolf-pack hunting strategies
emerge from simple rules in computational
simulations," Behavioural Processes, 2015
Conclusion
This project proposed a simple and effective image
feature representation method BoCF, for scene
classification. Compared with traditional
BoVW model in which the visual words are
usually obtained by using handcrafted
features. The later part of project
proposes an application of Grey Wolf
Optimizer (GWO) algorithm for satellite
image segmentation. The original GWO
has been correctly modified to work as an
instinctive clustering algorithm. Further, a
beneficial performance analysis was
carried out by comparing the proposed
method with the existing methods.
Consequently, in the future work, we need
to explore new methods and systems in
which the combination of remote sensing
data and information can be deployed to
promote the state of the art of remote
sensing image scene classification.
Aim
Our goal is to encourage the use of recent
technologies like deep learning and recent nature
inspired algorithms to detect more descriptive
features from an image through remote sensing and
to more accurate identification and classification of
the images
Classification of scenes is difficult if it contains
blurry and noisy content. The two significant areas
of scene classification problem are: learning and
scenes models for formal categories. If the images
are affected due to noise, poor quality, occlusion or
background clutter, it becomes quite a challenge to
classify an image. This difficult gets multiplied
whenever an image consists of many objects. There
has been a invariable raise in new classification
algorithms, techniques
Introduction
Remote Sensing Image Scene Classification plays
an essential role in a broad range of applications. In
this project, we have presented a mechanism for
remote sensing and image classification of large
dataset image collections. . Bag of Visual Words
(BoVW) model is used in first part of the project.
However, the traditional BoVW model only
captures the local patterns of images by utilizing
local features. Then proposed Bag of
Convolutional Features (BoCF) generates visual
words from deep convolutional features using off-
the-shelf convolutional neural networks. The
further part of project proposes an application of
Grey Wolf Optimizer (GWO) algorithm for
satellite image segmentation. The original GWO
has been suitably modified to work as an automatic
clustering algorithm.
Results
Accuracies of BoVW, BoCF
and GWO respectively
• The accuracy of traditional BoVW method with
dense SIFT is 41.72% under training ratio of
10% and 44.97% under the training ratio of
20%.
• The accuracy of BoCF method is almost
doubled by 82.65% under training ratio of 10%
and 84.32% under the training ratio of 20%.
• The accuracy of BoVW + GWO method is
78.70% under training ratio of 10% and 80.60%
under the training ratio of 20%.
Method
• Handcrafted Feature Learning:
These methods mainly focus on using
acceptable amount of engineering
handiness and domain expertise to design
various human engineering features.
• Unsupervised Feature Learning:
Unsupervised feature learning aims to
learn a set of basic functions (or filters)
used for feature encoding, in which the
input of the functions is a set of
handcrafted features
• Deep Feature Learning Based
Deep learning models that are composed
of multiple processing layers can learn
more powerful feature representations of
data with multiple levels of abstraction.
Grey Wolf Optimization
0.00%
50.00%
100.00%
BoVW +
Dense SIFT
BoCF GWO
Comparision of Remote
Sensing Classification
Algorithms
10% 20%