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Review on Different Change Vector Analysis Algorithms Based Change Detection Techniques

Sartajvir Singh Assistant Professor

E.C.E. Department, Chitkara University Himachal Pradesh, India

[email protected]

Dr. Rajneesh Talwar Professor and Principal

Chandigarh Group of Colleges Landran, Punjab, India

[email protected]

Abstract— Detection of Earth surface changes is essential to monitor regional climatic changes, avalanche hazard analysis and energy balance studies that occurs due to air temperature anomalies. Geographic Information System (GIS) enables such research activities or procedures to carry out through change detection analysis. From this perspective, different change detection techniques have been developed for Land-Use Land-Covered (LULC) region. Among the various change detection techniques, Change Vector Analysis (CVA) has level headed capability of extracting maximum information in terms of overall magnitude of change and the direction of change between multi-temporal multi-spectral bands satellite datasets. Recently developed CVA techniques such as CVA in Posterior Probability Space (CVAPS), Cross Correlogram Spectral Matching (CCSM) based CVA, CVA using enhanced Principal Component Analysis (PCA) and Inverse Triangular (IT) Function, and Median CVA (MCVA), are effective LULC change detection tools. This paper presents a systematic survey on recently developed CVA algorithms along with their characteristics, features and shortcomings. This paper also summarized the necessary pre-processing steps such as geometric corrections, atmospheric corrections, radiometric corrections and topographic corrections for flat surface as well as rugged mountain terrain to correct the estimated spectral reflectance value. It is expected that this reviewed paper on different CVA techniques gives an effective guidance to algorithm designers for modifying and developing CVA based change detection techniques that effectively use the diverse and complex remotely sensed data for detection of flat as well as undulating surface changes.

Index Terms— Geographic Information System (GIS), reflectance estimation modeling, topographic correction, Change Vector Analysis (CVA).

I. INTRODUCTION It has already been proven that remote sensing is only a

practical mean for detection of changes occurring over Land-Use/ Land-Covered (LULC) thousands of square kilometer area. Change detection analysis involves the use of multi-spectral bands of multi-temporal satellite dataset to discriminate the LULC changes [1]. Research activities on LULC changes provide a rapid way to obtain up-to date information on the impacts of natural hazards and human activities on regional and ecosystem studies [2-4]. A series of different change detection techniques have been developed and explored for vegetation or snow covered area for specific application since last two or three decades [5-12]. D. Lu et al. [13] exemplify seven categories of change detection techniques such as algebraic techniques, transformation, classification, advanced techniques, Geographical Information System (GIS) techniques, visual analysis and other techniques. As compare to all other change detection techniques, algebraic techniques are easy to implement and have a capability of achieving more accuracy than other categories. A number of

techniques comes under algebraic category such as Band Differencing [14], Band Ratioing [15], Vegetation Indices [16], Regression Analysis [17] and Change Vector Analysis (CVA) [18].

Among all change detection techniques, spectral change

vector called as Change Vector Analysis (CVA) [18], provides level headed capability of describing the output in terms of overall magnitude of change and the direction of change between two different time instances from multi-spectral satellite datasets [8, 19-24]. As documented in [13], CVA is an enhanced version of Band Differencing and can detect any type of change above the selected threshold value to generate the binary image of change and no-change pixels. The difficulty of training sample selection for threshold determination has also been minimized using reference ancillary information [13, 24-25]. CVA also have capability of avoiding cumulative error in image classification of an individual date and process any numbers of spectral bands simultaneously to retrieve maximum ‘from-to’ change-type information. The selection of specific bands for specific change detection algorithm is most essential to solve a specific problem in LULC. This study reveals the comparative analysis on different CVA based change detection techniques to detect LULC flat or mountainous region.

In past two-three decades, a series of CVA techniques

have been developed and explored. Malila [18] first used CVA for forest change detection which latterly implemented on multi-spectral monitoring of coastal environment [26], high temporal dimensionality satellite data set [27], multi-spectral monitoring of land cover [20], monitoring of selective logging activities [28]. Another hybrid CVA technique based on mixture of classification and radiometric change data has been developed and experimented on land cover [20, 29]. Sohl [30] revealed that CVA is best among all other change detection techniques because of its graphically rich content and ability to detect urban as well as vegetation change with enriched information. The CVA using Tasseled Cap (TC) brightness, greenness and wetness for two different time periods has been implemented to discriminate changes [23, 31]. Allen and Kupfer [23] developed an expanded CVA technique using the information retained in the vector's spherical statistics in the change extraction process but it contains some of its inherent drawbacks. Aiming to overcome the shortcomings in threshold

Proceedings of the 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013)

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value selection [20, 32-33], a semi-automatic Double-window Flexible Pace Search (DFPS) threshold determination technique, has been proposed for LULC in Improved Change Vector Analysis (ICVA) [34]. ICVA also has a capability of determining change-type information based on direction cosines [35] of change vectors. Another formulation for threshold selection, has been proposed by means of Principal Component Analysis (PCA) and Inverse Triangular (IT) function [36].

Recently, Median Change Vector Analysis (MCVA) [37] algorithm based on an enhanced 2n-dimensional feature space comprising direction cosine, has been proposed for multi-class change detection. Furthermore, modified-Change Vector Analysis (m-CVA) [38] for delivering outputs in continuous nature, Change Vector Analysis in Posterior Probability Space (CVAPS) [39] to overcome radiometric errors, Cross Correlogram Spectral Matching (CCSM) based improved Traditional Change Vector Analysis (TCVA) [18] to extract the degree of shape similarity between Vegetation Index (VI) profiles, CVA using distance and similarity measures based on Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM) to the formulation of spectral direction of change, and Euclidean distance to calculate magnitude [40], Unsupervised CVA [41] has been proposed to detect LULC conversion. Each CVA technique has specifically their own merits and demerits, and no one technique is suitable for every application [20] so it is very essential to design and implement CVA technique on global basis that will constitute all the features.

Apart from this, pre-processing of multi-temporal satellite datasets is a crucial requirement of CVA because overall accuracy assessment of change detection techniques depends upon the geometric corrections, radiometric corrections, and atmospheric corrections [42-50]. In addition to this, topographic corrections are also very important to overcome topographic variability especially in the case of undulating terrain [51-57]. Otherwise, it will be very hard to control commission and omission errors in post-processing steps that affects the overall accuracy. After all corrections, the task of an appropriate CVA change detection technique selection will be initiated and it often depends on the required information, ground truth data availability, time and money constraints, knowledge and familiarity of the study area, complexity of landscape, and analyst’s skill and experience [13, 20]. The aim of this paper is comprehensive exploration of all the major CVA based change detection techniques along with necessary pre-processing models.

This paper is organized in five major sections. Following this introduction, a brief overview of necessary pre-processing steps for satellite image interpretation. The comparative analysis of different CVA algorithms based change detection techniques are presented in third section, followed by discussion and summarization of prior studies on the characteristics, features and limitations of the different CVA

change detection techniques in fourth section. At last, general conclusion is presented.

II. SATELLITE IMAGERY PRE-PROCESSING In remote sensing, the energy reflected by Earth surface is

represented by binary number, named as Digital Number (DN) which depends on fraction of incoming solar radiation value, surface slope and its orientation, surface anisotropy, and atmospheric constituents [58]. The estimation of spectral reflectance imagery includes different corrections such as geometric corrections, radiometric corrections, and topographic corrections [42-57]. The geometric corrections are required to assign the actual ground location coordinates to the satellite imagery [59]. The radiometric corrections are essential because of (a) differences between the imageries due to changes in illumination values, variation in Sun and Earth distance, solar azimuth angle or solar zenith angle (b) changing atmospheric conditions causing different scattering and absorption, and (c) sensor differences [60]. The topographic corrections play a very significant role in case of undulating mountainous terrain because sun-facing illuminated slopes show more reflectance values whereas shaded slopes show less reflectance values [61]. The detailed study of satellite image interpretation has been explored in various studies [42-50]. The necessary steps for satellite imagery interpretation methodology, are shown in Fig. 1.

Fig. 1. Methodology of Satellite Image Interpretation

III. CHANGE VECTOR ANALYSIS Change Vector Analysis (CVA) is a change detection tool

that characterize dynamic changes in multi-spectral space by

Raw Satellite Imagery

Geometric Corrections

Radiometric Corrections

Topographic Corrections

Topographic Corrected Reflectance Imagery

DEM Generation

Estimation of Coefficients Radiance Estimation

Illumination Image

Change Detection Analysis

Computational of Error Matrix and Accuracy Assessment for binary imagery and classification evaluation


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change vector over two different time instances in terms of magnitude and direction [18]. The basic concept of CVA is derived from Band Differencing technique [13]. CVA is capable of detecting any type of change by selecting appropriate threshold value. The threshold selection is a crucial step of CVA to discriminate changed and unchanged pixels from satellite imagery. A series of CVA techniques have been developed to make change detection more capable for identifying change area. Here, we summarized the essential improvements that have been done in CVA technique since last two-three decades.

A. Change Vector Analysis (CVA) The concept of the earlier version of Change Vector

Analysis (CVA) involves the computation of spectral change based on multi-temporal pairs of spectral measurements, and compares their magnitudes to a specified threshold criterion [18]. The computed change vectors consists of essential information both in their magnitude and direction as shown in Fig. 2. There are two main reasons behind CVA to make it level headed change detection technique than other techniques: (a) it operates directly on entirely contiguous pixels, (b) it relieves the requirement of training and ground truth data for classification. It should also be noted that this technique requires reference data only to interpret the change vectors.

Fig. 2. Basic Algorithm of CVA in Multi-dimensional Space

B. Improved Change Vector Analysis (ICVA) The traditional CVA algorithm lacks of

semiautomatic/automatic threshold selection method to discriminate change and no-change pixels. Another drawback of existing CVA is the difficulty in discriminating different types of changes especially in case when the number of spectral bands are large. The Improved Change Vector Analysis (ICVA) [34] technique has removed the difficulty of threshold determination by semiautomatic technique, called Double-window Flexible Pace Search (DFPS). The DFPS technique effectively determine the threshold value of change magnitude [12, 39]. In order to discriminate change type, ICVA combines single image classification and minimum-distance categorization based upon the direction cosines [35] of the change vector [34]. However, this technique requires reference data to identify the change type discrimination,

therefore reference imageries should be radiometric corrected otherwise it affects overall accuracy assessment. The ICVA algorithm is more complex than earlier CVA because of the addition of DFPS algorithm and minimum-distance based categorization.

C. Modified Change Vector Analysis (m-CVA) Another improvement in CVA, has been done by

modified-Change Vector Analysis (m-CVA) [38] which preserves the retained information in the change vector's magnitude and direction as continuous data and this can be applied into n-number of change indicator spectral bands. The overall result of m-CVA depends upon feature space where Cartesian coordinates in a continuous domain are used to describe each change vector. The important advantages of this method are change categorization is now fully in the continuous data domain which allow the change descriptors to be used in common change classification algorithms, the computational simplicity of the method, feature space multidimensionality and relieve the requirement of ground truth data in this technique. Balcik and Goksel [62] has also applied m-CVA technique to compare the differences in the time-trajectory of the Tasseled Cap (TC) brightness, greenness and wetness, and reported that it is difficult to decide changed categories and threshold value. The m-CVA algorithm is simple to perform as compare to ICVA [34] because simple empirical manual threshold determination method has been used instead of semiautomatic/automatic threshold selection method. Furthermore, its accuracy assessment can be improved by using semi-automatic DFPS [34] threshold determination algorithm.

D. Change Vector Analysis in Posterior Probability Space (CVAPS) All CVA based change detection techniques discussed

above require a reliable radiometric imagery because CVA is based on pixel-wise radiometric comparison whose accuracy is affected by environment factors such as different atmospheric conditions. The requirement of reliable radiometric in image processing limits the application of CVA [34]. Most recent version of Change Vector Analysis in Posterior-probability Space (CVAPS) [39] relaxes the strict requirement of radiometric consistency in remotely sensed data. The radiometric consistency required by radiometric comparison methods such as Post Classification Comparison (PCC) but it overestimate land cover changes. CVAPS incorporate merits of PCC into CVA to improve the flexibility and performance of CVA in broad applications. As documented in [63], supervised method can be used to detect LULC changes by using PCC algorithm. As compare to unsupervised methods, supervised methods also have advantages of its capability to detect LULC changes and robustness to radiometric problems.

CVAPS technique followed the semiautomatic DFPS [34]

technique for selection of threshold value. In CVAPS algorithm and direction of the change vector in a posterior probability space is determined by applying supervised or

Multi-spectral Date 1 Satellite Imagery

Multi-spectral Date 2 Satellite Imagery

Change Vector Analysis (CVA)

Change Direction Component of CVA

Change Magnitude Component of CVA


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semi-supervised classification which can be used to extract the change-type information from satellite imagery. Furthermore, CVAPS relax the requirement of training samples for threshold value selection using semi-supervised classification but it still faces the lack of accuracy. The accuracy assessment of CVAPS can be further improved by using unsupervised based threshold value selection techniques.

E. CVA using Enhanced PCA and Inverse Triangular Function

It is reported [36] that selection of threshold value can be improved by incorporating Principal Component Analysis (PCA) and Inverse Triangular (IT) function in CVA. PCA is a vector space transformation function used to minimize multi-spectral data and maximize change-type information in high variance components. Furthermore, this technique also based on fuzzy concept ‘degree of change’ which is inversely proportional to nearness to mean, thus resulting in a smooth Inverse Triangular (IT) change function. In this algorithm, Kauth-Thomas Tasseled Cap (TC) transformation has been used to extract greenness-brightness coefficients. This technique has not been compared with any other existing CVA or change detection techniques, so it is very necessary to perform comparison of this technique with existing CVA techniques to check its effectiveness.

F. Other important CVA based Change Detection Techniques

1) Hyper-Spherical Direction Cosine (HSDC) [64]: It is an enhancement of traditional two spectral band CVA technique to multi- spectral band. In this technique, class probability density function has been used to determine threshold value of change magnitude.

2) CVA using Tasselled Cap transformation [65]: In this technique, differences in the time-trajectory of the Tasseled Cap greenness and brightness, were computed and then applied to Change Vector Analysis. It also reduces the multi-dimensional bands and at the same time highlighted change categories of the land cover.

3) Change Vector Analysis using Distance and Similarity Measures [40]: In this technique spectral change direction is calculated using Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM) spectral-similarity measures. Various advantages of this technique are: information processed only in one band, the scale value represents degree of change, insensitive to illumination variation.

4) Improved Traditional CVA using Cross-Correlogram Spectral Matching (CCSM) [66]: Another approach of CVA, called improved TCVA [18] has been proposed which uses Cross-Correlogram Spectral Matching (CCSM) analysis [66], to overcome the difficulties of TCVA. The basic idea of improved TCVA is to identify and exclude areas with land-cover modification (no changes) from the total changes

detected by it. CCSM can tell the degree of shape similarity between Vegetation Index (VI) profiles to detect land-cover conversion.

5) Median Change Vector Analysis [37]: In this algorithm, enhanced 2n-dimensional feature space, incorporates the change vector and median vector in direction cosine. This execution gives the more accurate results than ICVA proposed by Jen et al. [34].

IV. SUMMARY OF CVA TECHNIQUES It has already proven that CVA analysis can be useful tool

for evaluating continuous change such as increase or decrease in vegetation, water, forest and snow. CVA technique was initially designed for interpretation of two spectral bands or dimensions [18] and later extended to the unlimited number of bands [64, 67]. It involves analysis of change vectors rather than data differences that helps CVA to complete use of satellite imagery information, elimination of errors due to misclassification, and reduced sensitivity to topographic effects. Generally, CVA uses threshold determination techniques based on empirical values [23], interactive trial-and-error procedures in which an analyst interactively adjusts the threshold value and evaluates the resulting image until satisfied [68] but the resulting differences might include external influences and the threshold selection entirely based on the analyst’s skill [13]. ICVA [34] introduced first semi-automatic DFPS algorithm for threshold value determination and change vector determination based on cosine functions in a multi-dimensional space. CVAPS eliminates the strict requirement of reliable image radiometry by incorporating merits of PCC into CVA. CVA using CCSM, called improved TCVA used to detect and exclude areas with land-cover modification (not type change) from the total changes detected by CVA.

Accuracy assessment of each change detection is an essential part in remote sensing for evaluation of decision making process. The important accuracy assessment terms involve overall accuracy, producer accuracy, user accuracy, omission errors, commission errors and Kappa coefficient which can be calculated using error matrix [69-74]. Different change detection techniques have been combined together to improve the accuracy such as CVA using Posterior-probability Space (CVAPS) [39], CVA using enhanced PCA and IT [37], HSDC [64], CVA using distance and similarity measures [40], and CVA using CCSM [66]. The CVA technique provides number of features such as less sensitive to topographic or atmospheric effects, describes the output in term of overall magnitude of the change, direction of change, simultaneously processing of multiple bands, semi/automatic threshold finding process, and makes a perfect choice of CVA as change detection technique.

V. CONCLUSION This paper summarizes all Change Vector Analysis

(CVA) based change detection techniques with their


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comparative analysis and provides recommendations for algorithms designers to experiment CVA on global basis and developing new techniques that effectively use the diverse and complex remotely sensed data for flat as well as undulating surface. CVA techniques are applied very rarely on global basis for diverse range of land use/cover application. It is also expected that future developments in CVA, provides more incorporated techniques that combines pre/post processing techniques, robust algorithms and global optimization methods in the related areas of satellite imagery vision research for land use/cover.

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