تلفیق اطلاعات طیفی و مکانی به‌منظور تفکیک محصولات کشاورزی با استفاده از تصاویر چندزمانة سنتینل‌ـ2 (مطالعة موردی: شهرستان قروه)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه ژئودزی و مهندسی نقشه‌برداری، دانشگاه تفرش

2 استادیار، گروه ژئودزی و مهندسی نقشه‌برداری، دانشگاه تفرش

چکیده

امروزه کاربردهای تصاویر ماهواره‌ای، در پایش و مدیریت زمین‌های کشاورزی، رو به گسترش است. با توجه به قدرت تفکیک مکانی، طیفی و زمانی بالای تصاویر سنتینل‌ـ 2، در این مطالعه، در کشاورزی دقیق در شهرستان قروه از این تصاویر استفاده شده است. ابتدا با توجه به تقویم زراعی محصولات متفاوت آن منطقه، تصاویر سری زمانی جمع‌آوری شد. در روش پیشنهادی، نخست، فضای ویژگی طیفی براساس بازتاب طیفی باندها و همچنین شاخص‌های گیاهی، ایجاد شد. ابعاد فضای ویژگی طیفی، با استفاده از روش آنالیز مؤلفه‌های اصلی، کاهش یافت. سپس چهار طبقه‌بندی‌کنندة قدرتمند ماشین‌های بردار پشتیبان، شبکة عصبی پرسپترون چندلایه، نزدیک‌ترین k همسایه و جنگل‌های تصادفی نقشة طبقه‌بندی از اطلاعات طیفی تولید کردند. در ادامه، مکانی با هدف تعیین مرز مزارع، اطلاعات استخراج شد. برای این منظور، از شناسایی لبه‌ها در سری زمانی تصاویر سنتینل‌ـ 2 استفاده شد. در نهایت، نقشة طبقه‌بندی نهایی، با تلفیق اطلاعات مکانی و ادغام نتایج طبقه‌بندی‌کننده‌ها ایجاد شد. نتایج به‌دست‌آمده نشان داد که دقت طبقه‌بندی‌کننده‌های نزدیک‌ترین k همسایه، ماشین‌های بردار پشتیبان، شبکة عصبی پرسپترون چندلایه و جنگل‌های تصادفی روی فضای ویژگی طیفی اولیه، به‌ترتیب 78/77%، 16/79%، 41/76% و 89/76% است. با استفاده از روش پیشنهادی، دقت طبقه‌بندی به 72/94% افزایش پیدا کرد که حاکی از توانایی آن در منطقة مورد مطالعه است.

کلیدواژه‌ها


عنوان مقاله [English]

Fusion of spectral and spatial information for agricultural crop classification in multi-temporal Sentinel images (Case Study: Qorveh County)

نویسندگان [English]

  • Saeid Ahmadi 1
  • Hadiseh Hasani 2
1 M.Sc. Student, Geodesy and Surveying Engineering, Tafresh University, Tafresh
2 Assistant Prof., Dep. of Geodesy and Surveying Engineering, Tafresh University, Tafresh
چکیده [English]

Nowaday, there are wide applications for satellite images in agriculture monitoring and management. According to high spatial, spectral and temporal resolution of Sentinel-2 images, we used them for precise agriculture in Qorveh country. Proposed methd consist of five step: firstly, multi-temporal images are collected based on agriculture calender of crops. Then feature space is generated based on spectral reflectance and vegetation indices which consists of 70 features. According to high dimensionality of feature space, principle component analysis is applied to reduce its dimension. Four power classifiers include support vector machine, k-nearest neighbour, multi-layer perceptron and random forests classify the reduced spectral feature space. On the other hand, spatial information are extracted from multi-temporal multispectral images. For this pupose, strandard deviation (STD) maps are generated for red, NIR and SWIR bands of each epoch. Then, by averaging the STD maps, final STD map is obtained. Edge detection is performed on STD map and it improves by removing small lines, smoothing, thining, etc. Finally, crop mapping is done by fusion of four classification maps and agriculture farm boundaries. The obtained results show that classification accuracy of k-nearest neighbour, support vector machine, multi-layer perceptron and random forest classifiers are 77.78%, 79,16%, 76.41% and 76.89%, respectively. The overall accuracy of the proposed method improve up to 94.72% which proves high potential of the proposed method.

کلیدواژه‌ها [English]

  • Multi-temporal Sentinel-2 images
  • Agriculture farm boundary
  • Classifier fusion
  • Spatial information
  • Spectral information
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