آنالیز مقایسه‏ای بازیابی شاخص سطح برگ با استفاده از روش‏های یادگیری ماشین مبتنی‌بر داده‌های ابرطیفی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد سنجش از دور و GIS، دانشگاه تربیت مدرس

2 استادیار گروه سنجش از دور و GIS، دانشگاه تربیت مدرس

چکیده

متغیرهای بیوفیزیکی و بیوشیمیایی پوشش گیاهی، به‌منزلة متغیرهای ورودی، برای مدل‌های متفاوت چرخة کربن، آب، انرژی و مدل‌های اقلیمی و کشاورزی دقیق نقش مهمی ایفا‌ می‌کنند. یکی از مهم‌ترین متغیرهای مربوط به تاج‌پوشش گیاه، که کاربردهای فراوانی در مدل‌سازی‏های گوناگون خاک و گیاه و اتمسفر دارد، شاخص سطح برگ (LAI) است. روش‏های گوناگونی برای بازیابی LAI از تصاویر ابرطیفی به‌کار رفته‌اند که، از میان آنها، روش‏های ناپارامتریک غیرخطی یادگیری ماشین بسیار مورد توجه قرار گرفته‌اند زیرا، در مواجهه با داده‌های دارای ابعاد زیاد، انعطاف‌پذیرند. بااین‌حال، در مطالعات پیشین، به بررسی عملکرد روش‏های یادگیری ماشین در بازیابی مقادیر LAI در مقادیر حاشیه‏ای (مقادیر خارج از دامنة نمونه‏گیری زمینی) و قابلیت این روش‌ها در تهیة نقشة متغیر توجه چندانی نشده است. در این تحقیق، عملکرد چهار روش پرکاربرد یادگیری ماشین شامل رگرسیون بردار پشتیبان، فرایند گاوسی، شبکة عصبی مصنوعی و جنگل تصادفی در بازیابی LAI از تصویر ابرطیفی ماهوارة کریس‌ـ پروبا بررسی شده است. نتایج نشان داد که، به‌رغم کارآیی هر چهار روش در بازیابی مقادیر LAI برای دامنة مقادیر اندازه‏گیری‌‌شدة زمینی با RMSE بهتر از 0.5 و خطای نسبی کمتر از 10%، روش‏های فرایند گاوسی و رگرسیون بردار پشتیبان صحت بالاتری در مقایسه با سایر روش‏ها دارند. باوجوداین، عملکرد روش شبکة عصبی مصنوعی، در تخمین LAIهای دارای مقادیر حاشیه‏ای، بهتر از دیگر روش‏هاست و نقشة تهیه‌شده با این روش و تابع یادگیری GDA  تطابق بیشتری با نقشة NDVI و تصویر ابرطیفی منطقه دارد.

کلیدواژه‌ها

عنوان مقاله [English]

Comparative analysis of LAI retrieval from hyperspectral imagery using machine learning approaches

نویسندگان [English]

  • Behzad Mohammadi Sheikh Razi 1
  • Mohammad Sharif Molla 1
  • Ali Jafar Mousivand 2
  • Ali Shamsoddini 2
1 M.sc. in Remote Sensing and GIS, Tarbiat Modarres University
2 . Assistant prof. of Remote Sensing, Remote Sensing and GIS Dep., Tarbiat Modarres University
چکیده [English]

< p >Vegetation biophysical and biochemical variables are key inputs to a wide range of modelling approaches for carbon, water, energy cycle, climate and agricultural applications. Leaf Area Index (LAI) is among the most important canopy variables, used by many different physiological and functional plant models. Several approaches have been developed for vegetation properties retrieval from remotely sensed hyperspectral data. Among them, nonparametric machine learning methods have increasingly gained attention in vegetation variable retrieval due to their flexibility and efficiency while working with data of high dimensionality over the last decades. Although these methods provide reasonable accuracy at relatively high speed, they are mainly restricted to estimate values within their training domain and often perform poorly on the marginal values (i.e. outside of the training domain). The performance of these methods has not been adequately studied in retrieving LAI on the marginal values. This study employs four well-known machine learning methods including SVR, GPR, ANN, and RF to retrieve LAI from a hyperspectral CHRIS-Proba image over Barrax, Spain, in order to inspect their capability in retrieving marginal values. The results showed that although all the methods perform similarly well on retrieving LAI over the training domain values with RMSE values of less than 0.5 and relative error of less than 10%, GPR and SVR performed slightly better. However, ANN outperformed the other methods in estimating LAI on the marginal values, resulted in the generated LAI map more consistent with the NDVI map, as well as, the hyperspectral image of the region.

کلیدواژه‌ها [English]

  • Parameter retrieval
  • Leaf Area Index
  • nonparameteric machine leraning
  • CHRIS-Proba

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