Fusion of Multispectral and Radar Images to Enhance Classification Accuracy and Estimate the Area under Various Crops Cultivation

ماشین های کشاورزی

Article 6, Volume 13, Issue 4 - Serial Number 30, December 2023, Pages 493-508   

Document Type: Research Article

DOI: 10.22067/jam.2022.78446.1123

Authors

M. Saadikhani¹; M. Maharlooei^{* 2}; M. A. Rostami³; M. Edalat⁴

¹MSc Student in Biosystems Mechanical Engineering, Department of Biosystems Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

²Department of Biosystems Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

³Agricultural Engineering Research Department, Fars Agricultural and Resource Research and Education Center, AREEO, Shiraz, Iran

⁴Department of Agronomy and Plant Breeding, Shiraz University, Shiraz, Iran

Abstract

Introduction
Remote 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 Methods
Thirteen 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 Discussion
Visual 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.
Conclusion
The 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

References

Abdikan, S. (2018). Exploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area. Geocarto International, 33, 21-37. https://doi.org/10.1080/10106049.2016.1222635

Ahmad, A. (2005). Change detection in high density urban area and rural area using high resolution satellite image. Atılım Üniversitesi.

Arekhi, S., & Adibnejad, M. (2011). Efficiency assessment of the of Support Vector Machines for land use classification using Landsat ETM+ data (Case study: Ilam Dam Catchment). Iranian Journal of Range and Desert Research, 18, 420-440. (in Persian). https://doi.org/10.22092/ijrdr.2011.102175

Bounoua, L., Collatz, G., Los, S., Sellers, P., Dazlich, D., Tucker, C., & Randall, D. (2000). Sensitivity of climate to changes in NDVI. Journal of Climate, 13, 2277-2292. https://doi.org/10.1175/1520-0442(2000)013<2277:SOCTCI>2.0.CO;2

Efimov, A. I., Kolchaev, D. A., Nikiforov, M. B., & Novikov, A. I. (2018). Algorithm of geometrical transformation and merging of radar and video images for technical vision systems. Pages 1-4. 2018 7^th Mediterranean Conference on Embedded Computing (MECO): IEEE. https://doi.org/10.1109/meco.2018.8406061

Fang, L., He, N., Li, S., Ghamisi, P., & Benediktsson, J. A. (2017). Extinction profiles fusion for hyperspectral images classification. IEEE Transactions on Geoscience and Remote Sensing, 56, 1803-1815 https://doi.org/10.1109/tgrs.2017.2768479

Gomez, C., White, J. C., & Wulder, M. A. (2016). Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 55-72. https://doi.org/10.1016/j.isprsjprs.2016.03.008

Hunger, S., Karrasch, P., & Wessollek, C. (2016). Evaluating the potential of image fusion of multispectral and radar remote sensing data for the assessment of water body structure. Pages 374-384. Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII: SPIE. https://doi.org/10.1117/12.2241264

Johnson, S. J. (2009). An evaluation of land change modeler for ARCGIS for the ecological analysis of landscape composition. Southern Illinois University at Carbondale.

Knorn, J., Rabe, A., Radeloff, V. C., Kuemmerle, T., Kozak, J., & Hostert, P. (2009). Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote Sensing of Environment, 113, 957-964. https://doi.org/10.1016/j.rse.2009.01.010

Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15, 91-100. https://doi.org/10.1016/0273-1177(95)00079-t

Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.

Malingreau, J., Tucker, C., & Laporte, N. (1989). AVHRR for monitoring global tropical deforestation. International Journal of Remote Sensing, 10, 855-867. https://doi.org/10.1080/01431168908903926

Monsalve-Tellez, J. M., Torres-León, J. L., & Garcés-Gómez, Y. A. (2022). Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm. Agriculture, 12, 955. https://doi.org/10.3390/agriculture12070955

Myneni, R. B., Asrar, G., Tanre, D., & Choudhury, B. J. (1992). Remote sensing of solar radiation absorbed and reflected by vegetated land surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30, 302-314. https://doi.org/10.1109/36.134080

Nouri, H., Anderson, S., Sutton, P., Beecham, S., Nagler, P., Jarchow, C. J., & Roberts, D. A. (2017). NDVI, scale invariance and the modifiable areal unit problem: An assessment of vegetation in the Adelaide Parklands. Science of the Total Environment, 584, 11-18. https://doi.org/10.1016/j.scitotenv.2017.01.130

Palubinskas, G., Makarau, A., & Tao, J. (2011). Fusion of optical and radar data for the extraction of higher quality information.

Rahnama, S., Maharlooei, M., Rostami, M., & Maghsoudi, H. (2018). Date palm identification using Sentinel and Landsat satellites imagery. Pages 1. 2018 ASABE Annual International Meeting: American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/aim.201801777

Rahnama, S., Maharlooei, M., Rostami, M. A., & Maghsoudi, H. (2019). Determining the Best Classification Algorithm in order to Estimate the Area under Date Palm Cultivation using LANDSAT 8 Satellite Imagery. Journal of Agricultural Machinery, 9(2), 321-335. (in Persian). https://doi.org/10.22067/jam.v9i2.67310

Rajah, P., Odindi, J., & Mutanga, O. (2018). Feature level image fusion of optical imagery and Synthetic Aperture Radar (SAR) for invasive alien plant species detection and mapping. Remote Sensing Applications: Society and Environment, 10, 198-208. https://doi.org/10.1016/j.rsase.2018.04.007

Senay, G., & Elliott, R. (2000). Combining AVHRR-NDVI and landuse data to describe temporal and spatial dynamics of vegetation. Forest Ecology and management, 128, 83-91. https://doi.org/10.1016/s0378-1127(99)00275-3

Tuia, D., Merenyi, E., Jia, X., & Grana-Romay, M. (2014). Foreword to the special issue on machine learning for remote sensing data processing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 1007-1011. https://doi.org/10.1109/jstars.2014.2311915

Wang, H., Li, Q., Gao, Z., Sun, B., & Du, X. (2014). Assessment of land degradation using time series trends analysis of vegetation indictors in Beijing-Tianjin dust and sandstorm source region. Pages 753-756. 2014 IEEE Geoscience and Remote Sensing Symposium: IEEE. https://doi.org/10.1109/igarss.2014.6946533

Zhang, M., Ghamisi, P., & Li, W. (2017). Classification of hyperspectral and LIDAR data using extinction profiles with feature fusion. Remote Sensing Letters, 8, 957-966. https://doi.org/10.1080/2150704x.2017.1335902