Multi-Temporal Change detection And Image Segmentation

under guidance of:Dr. Anupam Agarwal Sir

Done by :-Shubham Raina (IIT2008086)

Ashish Meena (IIT2008089)

Aim and Objective

Concept of Image Segmentation will be used inSatellite Imagery that will be to Detect Multi-Temporal Changes of particular Landscape.These changes can be of various aspects like • Forestry • Watershed Areas• Traffic• Bare soil

Area of our Interest is ForestryReason :• Remote sensing fulfills the need for information regarding forest ecosystems and provides input to ecological models to help monitor forest dynamics.• The information can be used to make some predictions for a particular period of time.

These prediction can be :• How much forest area remain after that time ?• What will be the consequences of forest area

reduction over crops, climate and water resources etc.

Tools and Techniques• Tools

ERDAS 8.6Beam 4.8

ERDAS 8.6 : ERDAS IMAGINE is a remote sensing application with raster graphics editor capabilities designed by ERDAS, Inc for geospatial applications. The latest version is 2010, version 10.1. ERDAS IMAGINE is aimed primarily at geospatial raster data processing and allows the user to prepare, display and enhance digital images for mapping use in GIS (geographical information system) or in CADD (computer-aided drafting and design )software. It is a toolbox allowing the user to perform numerous operations on an image and generate an answer to specific geographical questions

Literature Survey1. A REVIEW ON IMAGE SEGMENTATION TECHNIQUES WITHREMOTE SENSING PERSPECTIVE (2010)– V. Dey a , , Y. Zhang a, M. Zhong b, a Department of Geodesy and Geomatics ∗Engineering, University of New Brunswick (UNB), Fredericton, E3B 5A3, NB, Canada –(d1991, yunzhang)@unb.cab Department of Civil Engineering, UNB, Fredericton, E3B 5A3, NB, Canada – ming@unb.ca

• This paper deals with details of techniques developed and used for image segmentation with remote sensing perspectives. Different models discussed here are

Object-background Model Markov Random Field Model Fuzzy Model

Multi-resolution Model

Watershed Model• The selection of segmentation approach depends on

what quality of segmentation is required. Further, it also depends on what scale of information is required. Few examples, based on done literature review in this paper have been stated to illustrated the Idea of segmentation.

2. Object-oriented Change Detection Method Based on Multi-scale and Multi-Feature Fusion (2009)Wen-jie WANG , Zhong-ming ZHAO, Hai-qing ZHU Dept. of Image ProcessingInstitute of Remote Sensing Applications Chinese Academy Sciences,P.O. Box 9718, Beijing 100101, China wangwenjie@gmail.com

High resolution satellite images offer abundance information of the earth surface for remote sensing applications.

• Using change detection technology to extract the target area changes from high resolution remote sensing images and rapidly update map database has become a focus research of remote sensing information processing.

• However the traditional methods of change detection are not suitable for high resolution remote sensing images.

• To overcome the limitations of traditional pixel-level change detection methods and the difficulties of change detection of high resolution remote sensing images, based on object-oriented analysis method, this paper presents a novel way of multi-scale and multi-feature fusion for high resolution remote sensing images change detection. Experiments show that this method has a stronger advantage than the traditional pixel-level method of high resolution remote sensing image change detection.

Modules

Module 1 :- Image SegmentationModule 2 :-Change Detection and Observation

Module 1 – Image Segmentation

• segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.

• The result of image segmentation is a set of segments that collectively cover the entire image.

Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s).

MethodsK-means algorithm is an iterative technique that is used to partition an image into K clusters. The basic algorithm is: Pick K cluster centers, either randomly or based on some

Heuristic. Assign each pixel in the image to the cluster that

minimizes the distance between the pixel and the cluster center.

Re-compute the cluster centers by averaging all of the pixels in the cluster .Repeat steps 2 and 3 until convergence is attained (e.g. no pixels change clusters)• Compression-based methods –

Compression based methods postulate that the optimal segmentation is the one that minimizes, over all possible segmentations, the coding length of the data . The connection between these two concepts is that segmentation tries to find patterns in an image and any regularity in the image can be used to compress it. The method describes each segment by its texture and boundary shape. Each of these components is modeled by a probability distribution function and its coding length is computed as follows.

Watershed transformationThe watershed transformation considers the gradient magnitude of an image as a topographic surface. Pixels having the highest gradient magnitude intensities (GMIs) correspond to watershed lines, which represent the region boundaries. Water placed on any pixel enclosed by a common watershed line flows downhill to a common local intensity minimum (LIM). Pixels draining to a common minimum form a catch basin, which represents a segment.

Watershed Model will be used for Image segmentation, because if we are working on image that can be expressed as two variables function then it’s derivative at a point on image will be gradient intensity of that point . So it’s mathematics is simple to understand.

Module 2

Change DetectionObject-based post-classification change detectionThe first step in object-based change detection is to perform the image segmentation. However, instead of using multispectral imagery, we segmented the 2004 land cover classification map.The resultant objects from the segmentation were identical to those of a union overlay operation between the two classified polygon layers (i.e., the 1999 and 2004 classification maps), in which all polygons from both classification layers were split at their intersections and preserved in the resultant object level, as illustrated in Figure 2. Both classification maps for 1999 and 2004 were used as thematic layers when performing the segmentation. When using a thematic layer, the borders separating different thematic classes are restrictive for any further segmentation [28]. In other words, the generated objects were not allowed to cross any of the borders of different land cover classes. We generated the objects based exclusively on the information of thematic layers by setting the weight of the image layer to 0 [28].

Image objects for change detection (Panel C) represent the intersections betweenthe two classification maps (Panel A: 1999; Panel B: 2004). Paned D shows the changedetection results obtained from the object-based approach.

Courtesy :-Object-based Land Cover Classification and Change Analysis inthe Baltimore Metropolitan Area Using Multi-temporal HighResolution Remote Sensing DataWeiqi Zhou 1,*, Austin Troy 1 and Morgan Grove 21 Rubenstein School of Environment and Natural Resources, University of Vermont, George D. AikenCenter, 81 Carrigan Drive, Burlington, VT 05405, USA; E-mail: atroy@uvm.edu (Austin Troy)2 Northeastern Research Station, USDA Forest Service, South Burlington, VT 05403, USA;E-mail: jmgrove@gmail.com

References:-1. 2009 International Conference on Environmental Science and Information Application Technology

2. 2009 Urban Remote Sensing Joint Event

Thank You