نوع مقاله : مقاله پژوهشی

نویسندگان

• محمد کریمی فیروزجایی ¹
• امیر صدیقی ¹
• نجمه نیسانی سامانی ²

¹ دانشجوی دکتری گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران

² استادیار گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران

چکیده

بکارگیری ویژگی‌‌های بهینه در الگوریتم‌های مختلف طبقه‌بندی، بر دقت نتایج حاصل از طبقه‌بندی تأثیرگذار می‌باشد. هدف از پژوهش حاضر بررسی قابلیت‌های تصاویر هایپریون و لندست و مقایسه کارایی الگوریتم‌های بهینه‌سازی ازدحام ذرات و جستجوی گرانشی جهت تعیین ویژ‌گی‌های بهینه برای تفکیک اراضی فضای سبز و شالیزار می‌باشد. در این مطالعه از تصاویر ماهواره‌ای لندست، هایپریون و مجموعه داده‌های واقعی مربوط به منطقه‌ای در شمال ایران استفاده شده است. در این مطالعه کارایی الگوریتم‌های بهینه‌سازی ازدحام ذرات و جستجوی گرانشی جهت تعیین ویژ‌گی‌های بهینه و قابلیت تصاویر لندست و هایپریون برای تفکیک اراضی فضای سبز و شالیزار با استفاده از مجموعه‌ی داده‌های واقعی مقایسه گردید. برای ارزیابی نتایج از پارامترهای دقت کاربر، دقت تولید کننده، دقت کلی و ضریب کاپا استفاده شده است. نتایج پژوهش بیانگر این است که دقت کلی تفکیک اراضی فضای سبز و شالیزار با تصویر هایپریون 15 درصد بالاتر از تصویر لندست می‌باشد. بکارگیری شاخص‌های طیفی در فرایند طبقه‌بندی، سبب بهبود دقت تفکیک اراضی فضای سبز و شالیزار در هر دو داده لندست و هایپریون می گردد. همچنین استفاده از الگوریتم بهینه‌سازی برای تعیین ویژگی‌های بهینه و استفاده از ویژگی‌های بهینه در فرایند طبقه‌بندی سبب افزایش دقت تفکیک اراضی فضای سبز و شالیزار می‌گردد. با توجه به مقادیر دقت کلی، کارایی الگوریتم بهینه‌سازی جستجوی گرانشی برای تفکیک اراضی فضای سبز و شالیزار 2 درصد بهتر از الگوریتم ازدحام ذرات می‌باشد.

کلیدواژه‌ها

• طبقه بندی
• ویژگی های بهینه
• ازدحام ذرات
• جستجوی گرانشی
• لندست
• هایپریون

20.1001.1.25883860.1399.29.113.8.0

عنوان مقاله [English]

Assessment and comparison of the efficiency of PSO and GSA algorithms for the separation of similar land uses: A case study of green spaces & rice fields in for thern Iran

نویسندگان [English]

• Mohammad Karimi Firozjaei ¹
• Amir Sedighi ¹
• Najmeh Neisany Samany ²

¹ Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran

² Assistant Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran

چکیده [English]

Introduction
Remote sensing data provide valuable information for the agricultural section and natural resources managers. Nowadays, performance management and estimation via using various methods such as classification and mapping have gained great significance. An example of such data is the mapping of crops cultivation and orchards at national and regional levels, which is one of the key tools in sustainable agricultural planning and management. These studies appear necessary especially in the field of strategic commodities such as rice and citrus which are among the most important food items for the Iranian people. The spatial information on agricultural lands in the field of agricultural planning and management can help the prevention of the spread of pests, management of the environmental stresses, crop performance estimation and vulnerability assessment in crop production. Field surveys and observations for crops mapping in the growing season in different years are very time-consuming, costly, and only suitable for small-scale studies. In contrast, over the past decades, remote sensing has been recognized as a suitable method for crops mapping for large areas in the shortest time and at low cost. Due to the climatic conditions of the areas in North of Iran, green spaces including vegetation and orchards, and rice fields are located near each other. At the time of the maximum growth of rice products, the spectral characteristics of these land covers are very similar. Therefore, the separation of these two land covers using satellite image classification process faces serious challenges. The aim of this study is to investigate the efficiency of the satellite images and the optimization algorithms for separating green spaces and rice fields from each other at the time of maximum growth. The present study differs from others in this field from two aspects; first, the study compares the capabilities of multispectral and hyperspectral satellite images with each other; additionally, it aims at comparing and evaluating the efficiency of the Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) so as to determine the optimal features for increasing the separation accuracy of green spaces and rice fields.
 
Materials and methodology
This research was carried out based on the two objectives of studying the capabilities of the Hyperion and Landsat images and comparing the efficiency of the PSO and GSA to determine optimal features for the separation of green spaces and rice fields. For this purpose, the two Landsat and Hyperion satellite images as well as ground data sets of the case study in North of Iran were employed. In the first step, preprocessing of the Hyperion and Landsat images was performed. In the second step, various features were extracted from the Hyperion and Landsat images using different spectral indices and transformations. In the third step, the Support Vector Machine (SVM) classifier was applied with two strategies, i.e. the usage of spectral bands and the usage of spectral bands as well as indices as the features in the classification process to extract green spaces and rice fields. In the fourth step, PSO and GSA were employed to extract optimal features from the Hyperion image to distinguish between green spaces and rice fields; then, classification was done with the extracted optimal features; and finally, the efficiency of PSO and GSA were compared to determine the optimal features for the separation of green spaces and rice fields using ground data sets.
 
Results and discussion
The results indicate that the use of Landsat image is not effective for the separation of rice fields and green spaces. In other words, due to the high spectral similarity of these land covers, a large percentage of pixels related to the two classes are mistakenly classified in another class. However, the accuracy of the producer and user relating to each class has increased by an average of 10 percent with the addition of spectral indices to the classification process.
Using Hyperion image is more effective than Landsat image for the separation of rice fields and green spaces. Moreover, the accuracy for the separation of rice fields and green spaces has increased with the simultaneous consideration of the bands and spectral indices in the classification process. It should be noted that one of the key factors in the efficiency evaluation process of the classification methods is the processing time. The results of using optimization algorithms for determining the optimal features indicate that out of the 150 spectral features (including 140 Hyperion image bands and 10 spectral indices and transformations), using PSO and GSA, only 25 and 31 optimal features were selected for the separation of green spaces and rice fields, respectively.The use of optimal features in the classification increases the accuracy for the separation of green spaces and rice fields more, compared to the use of all features in the classification. Additionally, GSA is superior to PSO when used for extracting optimal features for the separation of green spaces and rice fields. 
 
Conclusion
The results of this research indicate that the separation accuracy of green spaces and rice fields using Landsat image,is less than that of Hyperion image. With the addition of spectral indices to the classification process, the separation accuracy in both Landsat and Hyperion data increases. Moreover, using an optimization algorithm to determine the optimal features in the classification process will increase the separation accuracy of green spaces and rice fields. Given the overall accuracy values, the efficiency of GSA for separating green spaces and rice fields is higher than PSO.

کلیدواژه‌ها [English]

• Classification
• Optimal features
• PSO
• GSA
• Landsat
• Hyperion