A Study on the Rice Paddy Identification Using Multi-Temporal Remotely Sensed Optical and Radar Data

• Main Authors: Huo-Hsing HSIAO, 蕭國鑫
• Other Authors: Tian-Yuan SHIH
• Format: Others
• Language: zh-TW
• Published: 1998
• Online Access: http://ndltd.ncl.edu.tw/handle/17013011192376324961

碩士 === 國立交通大學 === 土木工程系 === 87 === Multi-temporal optical and radar data were used in this study to evaluate various classification approaches and search for an optimal combination of images taken in various stages of crop growth. Both conventional pixel-based and parcel-based classification approaches were adopted. For the parcel-based classification, attribute information for cadastral parcels and optical spectral characteristics and radar backscattering coefficient of various growing stages are all taken into account for achieving a better understanding of the rice paddy fields. For the pixel-by-pixel classification, NDVI (Normalized Difference Vegetation Index) and GI (Greenness Index) are derived for the identification of paddy fields in addition to the three SPOT spectral bands. In the multi-temporal processes, image combinations of SPOT band 2 and band 3 are used to analyze rice paddy by unsupervised classification method. Besides, these are also adopted in a modified classification method by first using a pixel-based classification and then introducing the parcel boundaries for grouping the classified results. For the parcel-based classification, indices such as NDVI, GI, BI (Brightness Index), and difference of NDVI and GI derived from SPOT images and backscattering coefficient, and difference of backscattering coefficient of RADARSAT are applied to identify the rice fields. Results are further characterized by error matrix, kappa , and τ. For evaluating the improvement of classification accuracy, the parcels of non-paddy are mask-out before setting a threshold to classify the rice fields for the difference images with a classification accuracy better than 90%, kappa , and τmore than 0.85. Images from two test areas are analyzed in a similar approach for verifying the possibilities of adopting common criteria. The results of the processing of optical images include: (1) An overall accuracy of 93.06% with kappa and Tau of 0.89 can be achieved by using an optimal combination of multi-temporal images; whereas, by using single time of images, a maximum overall accuracy of 90.41% with kappa and Tau of 0.85. This result shows that multi-temporal images give better classification accuracy than single time images. (2) It is shown that the best results were obtained by combining transplant and lush rice stages images. Little improvement of accuracy and indications of and Tau was gained by using additional images from other rice stages that the Z-test is not significant (Z0.05<1.75). (3) An improvement of classification accuracy of 1.0~1.5% and kappa and Tau of 0.02 was obtained by applying parcel-based classification as compared to pixel-based classification (Z0.05>1.99). The results of the processing of radar data include: (1) A classification accuracy of 69.0% was obtained by single time Radarsat images; an accuracy of 71.13% with kappa and Tau of 0.54 and 0.39 by difference images taken in two times. (2) In case where multi-temporal images are not available, optical images of single time would give a better classification result than Radarsat data.