The document summarizes a research paper on using CNN and LSTM models for land use and land cover change detection from remote sensing images. It presents the methodology which involves preprocessing two QuickBird satellite images from Russia from 2005 and 2010. Features are extracted from the segmented images using Gabor, Zernike moments and local features. A CNN model is used for classification and identifies changes such as increased buildings and conversion from forest to land. Performance is evaluated using accuracy, sensitivity, specificity and precision metrics which are over 95% according to the results. The paper concludes the CNN model effectively identifies temporal changes in land use from the satellite images.

1. CNN & LSTM based Land use and Land cover
Change Detection in Remote Sensing Images
Presented by
Siba Jasmine S (Reg no)
II Year ME (Communication Systems)
9131 - Velammal College of Engineering & Technology
Guided by
Dr. S. Gandhimathi @ Usha
Associate Professor , ECE
9131-Velammal College of Engineering & Technology

2. Introduction
• Remote sensing is the process of detecting and monitoring an object or
phenomenon without making physical contact with the object.
• Remote sensing images are used to detect and classify the objects on
the Earth surfaces.
• Change detection is to detect significant changes from sequential
images acquired at different points in time.
• Urban change detection is used for monitoring land use/land cover
changes in urban areas over the specified interval.

3. Title Year Author Methodology Advantages Disadvantages
Buildings change
detection based on
shape matching
for
multi-resolution
remote sensing
imagery
2017 Abdessetar
and Zhong
Shape matching Algorithm It is very fast both
to build and to
match.
The bits cannot be
processed
independently
in BRIEF.
Literature Survey

4. Title Year Author Methodology Advantages Disadvantages
A theoretical
framework for
unsupervised
change detection
based on change
vector analysis in
the polar domain
2018 F. Bovolo and
L. Bruzzone,
Change Vector Analysis
(CVA) technique
A proper Polar
framework for the
representation and
the analysis of
multi-temporal
data in the context
of the CVA
technique.
The Spatial
autocorrelation
function of the
images is not
impulsive.

5. Title Year Author Methodology Advantages Disadvantages
Unsupervised
change detection
in satellite images
using PCA
and k-means
clustering
2019 T. Celik PCA and k-means clustering Unsupervised
change-detection
method is
exploited for both
the noise-free and
the noisy images
Difference image
lies in the lack of
efficient automatic
techniques for
discriminating
between changed
and unchanged
pixels in the
difference image.

6. Title Year Author Methodology Advantages Disadvantages
Change detection
in Springer
Handbook of
Geographic
Information
2020 J. Théau Image differencing, principal
component analysis, post-
classification comparison,
Change Detection technology.
change detection
algorithms have
their own merits
and no single
approach is
optimal and
applicable to all
cases.
The data selection
is a critical step in
change detection.

7. Title Year Author Methodology Advantages Disadvantages
Automatic
analysis of the
difference image
for unsupervised
change detection
2021 L. Bruzzone
and D. Prieto
Bayesian decision theory, an
iterative technique (based on
the EM
algorithm)
it allows the
automatic
selection of the
decision-threshold
value that
minimizes the
overall change-
detection error
probability.
the estimation
process cannot be
performed on the
basis of a
training set.

8. • To use Remote Sensing techniques to study Land Use changes.
• To perform Classification using CNN and LSTM.
• To compare the performance of proposed method with existing method.
Objective

9. • Shape matching algorithm.
• Change vector analysis technique.
• PCA and k-means clustering.
• EM(Expectation - Maximization) method.
Existing Methodology

10. Preprocessing
Feature Extraction
Classification
Performance Measure
Segmentation
Binary descriptors and
hamming distance
Input Image
Morphological
• Gabor
• Zernike Moment
• Local Features
CNN
• Accuracy
• Sensitivity
• Precision
• Specificity
Proposed Methodology

11. • Two QuickBird Satellite images are segmented using:
1. Binary descriptors and Hamming distance method
2. Morphological operation.
• The following features are extracted from the segmented images :
1. Gabor features
2. Zernike Moment features
3. Local Features
• After feature extraction, classification is done by CNN (Convolutional Neural Network)
Algorithm.

12. Preprocessing
• Preprocessing helps to improve the quality of the image so that the
data can inspect in a better way.
• The images are resized into an exact 256 x 256 pixels.
• These are resized according to the requirements either by Upscaling
or Downscaling method.

13. • Two QuickBird satellite images are segmented using:
1. Binary Descriptors – Converts images into binary data
2. Hamming distance method – identify the difference between the
binary data
3. Morphological operation – Extracts the different binary data
Segmentation

14. • Gabor Feature – Extracts Texture, Pattern
• Zernike moment feature – Extracts Shapes
• Local feature – Extracts Intensity
Feature Extraction

15. • The changes in the QuickBird Satellite images is classified CNN algorithm.
• CNN(Convolutional Neural Networks) is a deep learning algorithm. It is used in
image classification and recognition because of its high accuracy.
• The difference is denoted in yellow as crowded building, Forest to land.
Classification

16. Input Image 1
Place and year : Russia, 2005
Pixel size : 1130 x 1129
The above figure shows the QuickBird Satellite image of Russia, 2005 with the pixel size of 1130 X
1129 as a first input image.

17. Input Image 2
Place and year : Russia, 2010
Pixel size : 1130 x 1129
The above figure shows the QuickBird Satellite image of Russia, 2010 with the pixel size of 1130 X
1129 as a second input image.

18. Resized Images
Resized to 256 X 256 pixels

19. Segmented Image
The above figure shows segmented image in which the difference between the binary data
(identified by hamming distance method) is extracted (Morphological operation)
and marked in the colour of white and remaining data in the colour of black.

20. Zernike feature extraction

21. The above figure shows the Zernike moment features to extract the shapes by
keeping the value of threshold and pixel intensity constant and rotating the images
by horizontal oval, -45-degree oval, vertical oval, horizontal shape and vertical
shape.
The above figure shows the graph of pixel intensity vs threshold in which x-axis is
the pixel intensity and y-axis are the threshold.

22. Classification
- Change as Crowded building
The above figure shows the classification of the satellite image changes and marked
in the colour of yellow

23. Identification
The above figure shows the identification of the change such as Crowded
building, Before fire & After fire and Forest to land.

24. Performance Measures
The following 4 metrics are determined in order to find the performance measures.
The table shows the Accuracy, Sensitivity, Specificity and Precision of different
Datsets.
Parameter Dataset 1 Dataset 2
Accuracy 96 95.95
Sensitivity 95.9596 95.9132
Specificity 99.9592 99.9510
Precision 95.9596 95.9132

25. The Performance measure values for the given input dataset is shown in
above graph.

26. Conclusion
• In this method the capability of a simple technique (image difference) to display
change were tested using QuickBird Satellite data for the city of Russia to monitor
urban change in two years 2005 and 2010.
• In the result of classified image the changes in the urban area are marked in the
colour of yellow and is identified as crowded building.
• The results from sub-scenes exposed that image difference technique effectively
emphasizes temporal changes in the study area and provides an excellent and easy to
interpret first position suggestion of change in urban areas

27. [1] P. P. Singh and R. Garg, “Automatic road extraction from high resolution satellite image using adaptive
global thresholding and morphological operations,” J. Indian Soc. Remote Sens., vol. 41, no. 3, pp. 631–640,
2013.
[2] J. H. Kim et al., “Objects segmentation from high-resolution aerial images using U-Net with pyramid
pooling layers,” IEEE Geosci. Remote Sens. Lett., vol. 16, no. 1, pp. 115–119, Jan. 2019.
[3] Y. Hua, L.Mou, and X. X. Zhu, “Recurrently exploring class-wise attention in a hybrid convolutional and
bidirectional LSTM network for multi-label aerial image classification,” ISPRS J. Photogrammetry Remote
Sens., vol. 149, pp. 188–199, 2019.
References

28. [4] Y. Na, J. H. Kim, K. Lee, J. Park, J. Y. Hwang, and J. P. Choi, “Domain adaptive transfer attack-based
segmentation networks for building extraction from aerial images,” IEEE Trans. Geosci. Remote Sens., to be
published, doi: 10.1109/TGRS.2020.3010055.
[5] S. Liu, Q. Shi, and L. Zhang, “Few-shot hyperspectral image classification with unknown classes using
multitask deep learning,” IEEE Trans. Geosci. Remote Sens., to be published, doi:
10.1109/TGRS.2020.3018879.
[6] Q. Shi et al., “Domain adaption for fine-grained urban village extraction from satellite images,” IEEE
Geosci. Remote Sens. Lett., vol. 17, no. 8, pp. 1430–1434, Aug. 2020

29. Annexure
This paper was published at 10th International Conference on
Contemporary Engineering and Technology (ICCET) , May 21st – 22nd
2022 Organized by Organization of Science and Innovative Engineering
and Technology (OSIET), Chennai, India.

31. ICCET - 2022

34. Thank You…