Land Use and Cover (LUC) Change Detection using
Image Processing Techniques
Shradhda Devidas Sapkale 1
,
Prof. Dr. B. D. Phulpagar 2P.G. Student, Department of Computer Engineering, PES Modern Engineering College, Pune, Maharashtra, India1 Associate Professor, Department of Computer Engineering, PES Modern Engineering College, Pune, Maharashtra, India2
ABSTRACT:Satellite images hold a large amount of data. Applications like environmental monitoring, spatial planning, and natural resource management are among a few that have led to development of methods to extract remote sensor images. Remote sensing satellite image consists of Digital Numbers [DN] which represent image features such as color, brightness, wavelength, radiated energy frequency, or picture element in the image. Image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. An attempt is being made to improve the performance ability of municipalities/urban local bodies, so that they would be able to discharge their duties efficiently in the planning and development of urban areas. Land-use classification requires up-to-date urban maps potentially covering large areas containing thousands of things, like house, buildings, schools, slum, etc. This kind of map is usually difficult to extract particular information. Here we describe the technique and demonstrate it for an Urban land use and cover study using satellite imagery. We show that our result compares favorably with that from existing use of segmentation methods in terms of suitability for land-use Classification for urban area. System uses following methods - Histogram Hyperbolization, Canny Edge Detection, Multidate change
Detection using Spectral change vector analysis (CVA) and draw vector lines.
KEYWORDS: Urbanization, Remote Sensing, GIS, Digital Map, Image Enhancement, Change detection (CD), Object
Detection.
I. INTRODUCTION
The image processing techniques are effective in identifying the urban growth pattern from the spatial and temporal data captured by the remote sensing techniques. The spatial patterns of urban sprawl over different time periods, can be systematically mapped, monitored and accurately assessed from satellite data (remotely sensed data) along with conventional ground data. Different types of land uses or land cover are Recreational, Transport, Agricultural, Residential, and commercial.
For LUC change detection, remote sensing offers cost-effective solutions to city planners for both macro and micro level analysis of land use planning leading to urban environment management. GIS (geographical Information Systems) is best utilized for integration of various data-sets to obtain a homogeneous composite land development unit which helps in identifying the problem area ad suggest conservation measures. Remote sensing technology along with GIS is an ideal tool to identify, locate and map various types of land cover. Digital image processing refers to the methods and operations that used in order to extract useful information and various features from an image. So these operations tend to enhance or modify the properties of the image so as to achieve better results after classification.
One of the most popular techniques in image processing is image enhancement and image segmentation. Image enhancement is the improvement of digital image quality without knowledge about the source of degradation. Image enhancement techniques are used for making satellite imageries more informative and helping to achieve the goal of image interpretation. Image segmentation is used to divide the image into a number of homogeneous entities that can later be classified into different categories and the region of interest or the entity under observation can be extracted from the rest of the image.
Remote Sensing, image processing, enhancement techniques are most effective in monitoring manmade features on the surface of earth. Manmade features are dynamic and constantly changing over time and space. Here, proposing a system which takes a raw imagery as an input and output imagery provides enhanced characteristics/features available on processed with accuracy. Imagery of different time period is used to detect the change on urban land area which is unauthorized or illegal and affects the natural environment which causes natural disaster like ood and draught situations that occurred recently in last two years in India.
II. RELATED WORK
A. Our work has ties to previous work from various domains. Literature Survey explains that remote sensing has contributed to the creation of huge datasets of satellite and airborne imagery. However, these datasets had very high resolution images which required fast processors and powerful GPUs for computation. Moreover, the image processing techniques present at the time were not enhanced enough to deal with or be used for high resolution satellite and airborne imagery.
B. Literature survey explains about the past few decades have contributed a lot to the advancements in the field of image processing. These advancements and new innovative technologies have made it possible to work on huge datasets of high resolution images like satellite image dataset. Survey literature talks about some of the most popular image processing techniques and some image classification methods that are used for working on satellite images.
C. The first section, satellite image processing, talks about one of the most popular image processing techniques, image segmentation, which is the process of segmenting the image into a number of 'objects' which helps to extract the region of interest from the entire image and helps to remove the background noise.
D. Literature Survey explains about various techniques used for detecting edges. The basic underlying approach is detecting the local changes in the pixel intensity, which depicts the boundary between two regions.
E. The result of this technique gives the discontinuities in the image and helps to extract the area of interest from the background. In contrast to the region based segmentation techniques, where weak boundaries are 'submerged' and two different regions combine to form a single one, edge detection is able to extract weak boundaries as well.
III. PROPOSED ALGORITHM (METHODOLOGY) METHOD –1
A. Histogram Hyperbolization
1.1 Histograms
• Histograms plots how many times (frequency) each intensity value in image occurs. • Histograms: only statistical information.
• No indication of location of pixels 1.2 Image Contrast
• The contrast of a grayscale image indicates how easily objects in the image can be distinguished • High contrast image: many distinct intensity values
1. Original Image 2. Contrast of Original Image
METHOD -2
Canny Edge Detection
• CED is used to detect the structural information in the image. Structural- boundary of objects.
• The detected edges should be very close to real edges. There will be minimum gap between real edge and detected Edge.
• Output of CED is used to check the structural similarity between both the images.
3.Output of Edge Detection
METHOD -3
Color change detection • CED is used to check the color similarity pixel wise.
• The goal is to identify the set of pixels that are “different” either difference is less or more between the latest image of the sequence and the previous images; these pixels comprise the change mask.
• The change mask may result from a combination of factors such as, including presence or absence of objects, motion of objects related to the background in an image, or shape changes of objects.
• Identify stationary objects that undergo change in color or brightness.
4.KNN algorithm
METHOD -4
Multiple change detection using Spectral change vector analysis (CVA)
• Change Vector Analysis (CVA) is used to identify spectral changes between two identical scenes which were acquired at different times.
• CVA is limited to two bands per image. For each pixel it calculates the change vector in the two-dimensional spectral space.
• The coordinate system is defined by the order of the input bands: the first band defines the x-axis and the second band the y-axis, respectively.
• Angles are returned *in degree* beginning with 0 degrees pointing 'north', i.e. the y-axis, i.e. the second band. • Used to identify spectral changes between two identical scenes which were acquired at different times. • For each pixel it calculates the change vector in the two-dimensional spectral space.
• The type of change that has occurred can be determined by the angle or direction of the change vector.
• The type of change is then placed on a black and white base map of the region and the changed pixels are colour coded according to their directions.
5.Difference Image 6. Thresh Image
METHOD -5
Draw Vectors/polygons
• Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy.
• It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like SciPy .
IV. RESULTANDDISCUSSIONS
• If we take the images in same process vice-versa, then Fig. ( c ) Shows the same result as in Fig. (8).
• As Image (a) is contrast of latest date image shown in image (8) which is a result of histogram hyperbolization, and image (b) is an output of the canny edge detection.
a. Contrast of latest date image b. Output of CED for image (a)
c. Result after vice-versa input given to the process If we take the images in process vice-versa, then Image(c) Shows the same result as in Image(8).
V. COMPARISONOFMETHODS
Methods Process output Actual Results Remarks
Structural change detection
Detects changes only related to
boundary of objects Does not work for
pixel value change detection.
Pixel value change detection
Detects only changes in pixel
RGB values Does not work for
structural changes.
Structural + Pixel value change
detection
Using both the methods we get the multiple change detected in two different images of same locations acquired at different
time periods
Works for structural and pixel value change
detection both .
VI. CONCLUSION AND FUTURE WORK
• Image enhancement using Histogram Hyperbolization and canny edge detection for color images gives fine results of change detection in satellite images.
• Image enhancement helps to cluster much closed pixel values in different classes.
• So that very blur or not-detectable boundaries of two objects or two spaces in image is detected very finely and vector line work can be done for very short objects present in an image and detects changes automatically. • CED and color change detection together gives more accurate change detection in Images.
• High accuracy classification.
FUTURE WORK
• The multiple CD scheme can be improved by refining the hierarchical clustering technique for high dimensional deep change vector and automatically deciding the number of kinds of change.
• In future we can improve the process for working on two images of same location with different size and angle, updating the angle of image as per the previous date image of same location.
REFERENCES
1. Heather C. North, David Pairman, Stella E. Belliss, ‘Boundary Delineation of Agricultural Fields in Multitemporal Satellite Imagery’, 2018 IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 12 , Issue: 1 , 2019 ).
2. Yaseen T. Mustafa, Dilovan Ramadhan Ismail , ‘Land Use Land Cover Change in Zakho District, Kurdistan Region, Iraq: Past, Current and Future’, International Conference on Advanced Science and Engineering (ICOASE), 2019
3. Sudipan Saha, Francesca Bovolo, Lorenzo Bruzzone, ‘Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images’, IEEE Transaction Transaction On Geoscience & Remote Sensing, 2019.
4. Sicong Liu, Qian Du, Xiaohua Tong, AlimSamat, Lorenzo Bruzzone, Francesca Bovolo,’Multiscale Morphological Compressed Change Vector Analysis for Unsupervised Multiple Change Detection’, IEEE Journal of Selected Topics in Applied Earth Observation & Remote Sensing, 2017.
5. Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh, ‘Multiple Change Point Analysis: Fast Implementation And Strong Consistency’, IEEE Transaction of Signal Processing, 2016.
6. Sicong Liu, Lorenzo Bruzzone, Francesca Bovolo, Massimo Zanetti, Peijun Du, ‘Sequential Spectral Change Vector Analysis for Iteratively Discovering and Detecting Multiple Changes in Hyperspectral Images’, IEEE Transaction on Geoscience & Remote Sensing, 2015.
7. Sicong Liu, Lorenzo Bruzzone, Francesca Bovolo, Peijun Du, ‘Hierarchical Unsupervised Change Detection in Multitemporal Hyperspectral Images’, IEEE Transaction on Geoscience & Remote Sensing, 2015.
8. Jun Geng and Lifeng Lai, ‘Quickest Change-Point Detection Over Multiple Data Streams via Sequential Observations’, IEEE, 2018.
9. Sudipan Saha, Francesca Bovolo, Lorenzo Bruzzone, ‘Unsupervised Multiple- Change Detection in VHR Optical Images using Deep Features’, IEEE, 2018. 10. Sicong Liu, Qian Du, Xiaohua Tong, Alim Samat, Lorenzo Bruzzone, Francesca Bovolo ‘A Spectral - Spatial Multiscale Approach For Unsupervised
Multiple Change Detection’ IEEE, 2017.
11. J. E. Ball, D. T. Anderson, and C. S. Chan, ‘Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community,’ J. Appl.
12. H. North, D. Pairman, and S. Belliss, ‘Full geographic coverage mapping of agricultural land use with time-series satellite images,’ Landcare Res. Contract Rep. Environ. Canterbury, Christchurch, New Zealand, Tech. Rep. LC1742, Jan. 2014.
13. H. A. Othman, ‘Urban planning strategies, towards sustainable land use management in Duhok city Kurdistan Region, Iraq’ MS.c., Highr institute of planning, University of Duhok, Duhok, 2008.
