@article { author = {Saadikhani, M. and Maharlooei, M. and Rostami, M. A. and Edalat, M.}, title = {Fusion of Multispectral and Radar Images to Enhance Classification Accuracy and Estimate the Area under Various Crops Cultivation}, journal = {Journal of Agricultural Machinery}, volume = {13}, number = {4}, pages = {493-508}, year = {2023}, publisher = {Ferdowsi University of Mashhad}, issn = {2228-6829}, eissn = {2423-3943}, doi = {10.22067/jam.2022.78446.1123}, abstract = {IntroductionRemote sensing is defined as data acquisition about an object or a phenomenon related to a geographic location without physical. The use of remote sensing data is expanding rapidly. Researchers have always been interested in accurately classifying land coverage phenomena using multispectral images. One of the factors that reduces the accuracy of the classification map is the existence of uneven surfaces and high-altitude areas. The presence of high-altitude points makes it difficult for the sensors to obtain accurate reflection information from the surface of the phenomena. Radar imagery used with the digital elevation model (DEM) is effective for identifying and determining altitude phenomena. Image fusion is a technique that uses two sensors with completely different specifications and takes advantage of both of the sensors' capabilities. In this study, the feasibility of employing the fusion technique to improve the overall accuracy of classifying land coverage phenomena using time series NDVI images of Sentinel 2 satellite imagery and PALSAR radar imagery of ALOS satellite was investigated. Additionally, the results of predicted and measured areas of fields under cultivation of wheat, barley, and canola were studied.Materials and MethodsThirteen Sentinel-2 multispectral satellite images with 10-meter spatial resolution from the Bajgah region in Fars province, Iran from Nov 2018 to June 2019 were downloaded at the Level-1C processing level to classify the cultivated lands and other phenomena. Ground truth data were collected through several field visits using handheld GPS to pinpoint different phenomena in the region of study. The seven classes of distinguished land coverage and phenomena include (1) Wheat, (2) Barley, (3) Canola, (4) Tree, (5) Residential regions, (6) Soil, and (7) others. After the preprocessing operations such as radiometric and atmospheric corrections using predefined built-in algorithms recommended by other researchers in ENVI 5.3, and cropping the region of interest (ROI) from the original image, the Normalized Difference Vegetation Index (NDVI) was calculated for each image. The DEM was obtained from the PALSAR sensor radar image with the 12.5-meter spatial resolution of the ALOS satellite. After preprocessing and cropping the ROI, a binary mask of radar images was created using threshold values of altitudes between 1764 and 1799 meters above the sea level in ENVI 5.3. The NDVI time series was then composed of all 13 images and integrated with radar images using the pixel-level integration method. The purpose of this process was to remove the high-altitude points in the study area that would reduce the accuracy of the classification map. The image fusion process was also performed using ENVI 5.3. The support Vector Machine (SVM) classification method was employed to train the classifier for both fused and unfused images as suggested by other researchers.To evaluate the effectiveness of image fusion, Commission and Omission errors, and the Overall accuracy were calculated using a Confusion matrix. To study the accuracy of the estimated area under cultivation of main crops in the region versus the actual measured values of the area, regression equation and percentage of difference were calculated.Results and DiscussionVisual inspection of classified output maps shows the difference between the fused and unfused images in classifying similar classes such as buildings and structures versus regions covered with bare soil and lands under cultivation versus natural vegetation in high altitude points. Statistical metrics verified these visual evaluations.  The SVM algorithm in fusion mode resulted in 98.06% accuracy and 0.97 kappa coefficient, 7.5% higher accuracy than the unfused images.As stated earlier, the similarities between the soil class (stones and rocks in the mountains) and manmade buildings and infrastructures increase omission error and misclassification in unfused image classification. The same misclassification occurred for the visually similar croplands and shallow vegetation at high altitude points. These results were consistence with previous literature that reported the same misclassification in analogous classes. The predicted area under cultivation of wheat and barley were overestimated by 3 and 1.5 percent, respectively. However, for canola, the area was underestimated by 3.5 percent.ConclusionThe main focus of this study was employing the image fusion technique and improving the classification accuracy of satellite imagery. Integration of PALSAR sensor data from ALOS radar satellite with multi-spectral imagery of Sentinel 2 satellite enhanced the classification accuracy of output maps by eliminating the high-altitude points and biases due to rocks and natural vegetation at hills and mountains. Statistical metrics such as the overall accuracy, Kappa coefficient, and commission and omission errors confirmed the visual findings of the fused vs. unfused classification maps.}, keywords = {Confusion Matrix,Normalized Difference Vegetation Index (NDVI),Radar Image,Sentinel 2 satellite,Support vector machine}, title_fa = {ادغام تصاویر چندطیفی و راداری به‌منظور افزایش دقت طبقه‌بندی و برآورد سطح زیر کشت محصولات زراعی}, abstract_fa = {سنجش از دور فنّ به‌دست‌آوردن اطلاعات درباره یک شئ، عارضه و یا پدیده‌های مربوط به یک منطقه جغرافیایی خاص بدون تماس فیزیکی با آن‌ها است. دستیابی به دقت بالا در طبقه‌بندی عوارض سطح زمین به کمک تصاویر چندطیفی همواره مد نظر پژوهشگران بوده است. یکی از عوامل کاهش دقت نقشه طبقه‌بندی، ناهموار بودن سطح زمین است. وجود نقاط مرتفع موجب می‌شود که سنجنده در دریافت دقیق اطلاعات بازتابی از سطح پدیده‌ها با مشکل روبه‌رو شود. تصاویر رادار با ارائه مدل رقومی ارتفاع (DEM) در شناسایی و تعیین ارتفاع پدیده‌های سطح زمین موثر است. استفاده از خصوصیات تصاویر دو سنجنده کاملاً متفاوت به‌منظور بهره‌گیری از قابلیت‌های مثبت آن‌ها با کمک روش ادغام تصاویر ممکن می‌شود. در این پژوهش به‌منظور برآورد سطح زیر کشت و طبقه‌بندی محصولات زراعی و سایر پدیده‌های موجود در منطقه مورد مطالعه، از تصاویر چندطیفی ماهواره سنتینل2 مربوط به منطقه باجگاه واقع در استان فارس استفاده شد. بدین منظور سری زمانی NDVI متشکل از 13 تصویر ایجاد و با تصویر راداری سنجنده PALSAR در سطح پیکسل، با هدف حذف نقاط مرتفع، تلفیق شد. نتایج این پژوهش نشان داد طبقه‌بندی تصاویر برای شناسایی مزارع زیر کشت محصولات مختلف با دقت بالایی انجام شده است و سطح زیر کشت با دقت 97درصد در گندم، 99.5درصد در جو و 96.5 درصد در کلزا نسبت به مقادیر اندازه‌گیری‌شده در مزرعه تخمین زده شده است. تصاویر ادغام‌شده دارای دقت کلی 98.1 درصد و ضریب کاپا 0.97 بود که دقت کلی را نسبت به تصاویر مجزا 7.5 درصد بهبود بخشید.}, keywords_fa = {سنتینل2,شاخص نرمال‌شده اختلاف پوشش گیاهی (NDVI),ضریب کاپا,ماتریس آشفتگی,ماشین بردار پشتیبان}, url = {https://jame.um.ac.ir/article_43118.html}, eprint = {https://jame.um.ac.ir/article_43118_a57b7aaba03d768dcf49e1713c9ff891.pdf} }