A Semi-Automated Approach For Identifying And Classifying Urban Distressed And Modern Area Based On Spectral And Spatial Patterns In Object-Oriented Remote Sensing: A Case Study Area Isfahan City

Developing of urban neighborhoods in line with the growth and expansion of the city and population growth has undergone many changes in time pass. Such change is more visible in cities environment with historical background. Thus, it can be seen well the difference between the traditional neighborhoods that no plan have been formed and after the formation, planning was done for them with new neighborhoods which were created and formed by plan.
Remote sensing is known as very effective technology for monitoring urban environments. There are several approaches for processing remote sensing satellite imagery such as pixel based and object based approaches. An Object Based Image Analysis (OBIA) is considered as one of the well-established techniques for processing satellite images when applied to monitoring cities environment. In despite of pixel based approach, the OBIA make use spectral information together with spatial characteristics of ground objects. Such specific ability allows to model ground objects effectively.
OBIA has gained prominence in the field of remote sensing over the last decade. It is credited to have the potential to overcome weaknesses associated with per-pixel analysis such as, for instance, disregarding geometric and contextual information. When it is used within the “geo-domain” or at scales which are related to earth “geo- centric” applications, it is in scientific literature often referred to as geographic object-based image analysis (GEOBIA). OBIA is a knowledge-driven approach in which a range of diagnostic features for a particular object can be integrated on the basis of expert knowledge. This approach aims to represent the content of a complex scene in a manner that best describes the imaged reality, by mimicking human perception. By incorporating spectral information (e.g., color) and spatial characteristics (e.g., size, shape), together with textural data and contextual information (e.g., association with neighboring objects), OBIA approaches the way that humans visually interpret the information on aerial photos and satellite images. OBIA techniques can be used in the review and observation of the difference and adaptive compare between the traditional and modern quarters pattern of the urban environments. In this regard, OBIA is known as effective and powerful image analysis processing method which leads to obtain high accuracy form satellite images. OBIA make use spatial and spectral information together by means of integration, segmentation and class modeling. Current research makes use of OBIA’s capabilities for modeling urban characteristics. The aim of this study is to compare textural- patterns of distressed and modern areas in Esfahan city by applying an object based approach. To achieve this goal, two types of urban neighborhoods namely Nokhajo and Mardavij were selected from distressed and modern areas respectively. The Quick Bird satellite images were acquired for year 2015. In order to perform object based approach, the object based image processing started off by applying multi resolution segmentation based on spatial and spectral patterns of each area. We used shape index, compactness for segmentation under specific scale parameters. The segmentation process was performed several times to obtain more accurate scale parameter. In order to extract the urban texture patterns, the rule based classification was performed by applying OBIA based algorithms and considering physical and spectral characteristics of urban objects. For this to happen, variety of OBIA techniques including: geometrical information, texture, compression ratio, irregular shapes and etc were employed to derive spatial patterns of each part. The outcome of these OBIA based algorithms were used to identify spatial patterns of distressed and modern zones. In doing so, after identifying the appropriate algorithms, fuzzy classification with nearest neighbor algorithm was applied for class modeling process. In terms of fuzzy rule based classification, the process was performed by employing fuzzy membership function as well as fuzzy operators. Membership functions allow you to define the relationship between feature values and the degree of membership to a class using fuzzy logic. By comparing the membership degree achieved from membership function, the “AND” operator was selected to be effective operator for object based fuzzy classification. Accordingly fuzzy rule based classification was performed by employing “AND” operator and applying textural, shape, geometric, statistical, spatial and spectral indices. In order to assess the accuracy of results, the accuracy assessment process was performed based on data which were gathered in field operation. The error matrix and kappa coefficient were derived by comparing the ground truth dataset and results of classifications. Results of this research indicated that OBIA is indeed effective method for modeling urban structure and classifying their based on characteristic of each item. According the results integration of spectral and spatial patterns leads to model urban structure effectively. Our research results also confirmed that textural algorithms leads to detect urban component effectively. Well organized road network system together with distribution of green space and normal density in building were identified as most important indicators in modern part of Esfahan. While, very high density in building, less of green space area with narrow road network systems were observed in distressed section of the study area. According to this statement OBIA represent very effective and powerful methodology for modeling urban structure by means of integration spectral and spatial characteristics. Results of this research are great of important for identifying and classifying urban textures patterns. The archived results can be used in rapid identification of texture patterns in urban environments and is a useful to a variety of urban planning studies. The proposed approach in this research will guide researchers/students to employ effective algorithms in OBIA which leads to obtain more accurate results. Results are also great of important for regional governmental departments such a Municipality of Esfahan for updating land use/cover maps which are base of any decision and planning.