Extraction of farmland classification based on multi-temporal remote sensing data

Abstract: Both artificial visual interpretation and computer automatic classification were the mainly remote sensing methods used to extract farmland information. At present, it is still difficult to entirely replace the artificial visual interpretation for the computer automatic classification to extract farmlands' type information of the remote sensing image, because the automatic method needs more efforts to improve the precision of the classification results, so the problem became the key link of the automatic classification extraction. How to extract farmlands' type information in the western part of Jilin is one of the major problems which the paper attempts to solve to distinguish paddy field from dry land. A new solution to extract the farmland information has been designed for the remote sensing automatic classification, based on the spatial variation theory. The classification scheme was carried out by operating in an R language platform and the remote sensing software ERDAS platform. The farmland type of Zhenlai in the western of Jilin was extracted and monitored by making use of four indexes, the range of NDVI series, the local variance of image texture, the modified soil adjusted vegetation index, and the normalized difference water index, which have significant meaning for the farmland cover type in the transition zone between the cropping area and the nomadic area. These variances with clear physical meaning information (including the vegetation, water, soil drought conditions) and phonological information were used to build a multi-dimensional feature space classification data set. The results indicated that: 1) the dry field of which an area of 1065.337 km2 was cultivated in the study area was the largest farmlands' type, and also was one of the most important ecological landscape types. It's the spatial distribution characteristic of the study area that is a relatively dispersed dry field, and a relatively concentrated paddy field; 2) based on the multidimensional space data set, the algorithm of a support vector machine (SVM) was chosen to automatically extract the farmland types' data of the paddy field and the dry land. The overall classification accuracy of the algorithm was 94%, the Kappa coefficient of the classification was 0.87, and the extracted accuracy of the paddy field was 98.3%, while the extracted accuracy of the dry land was 98%. The existing automatic extraction approach was implemented to obtain a comparatively ideal classification result; 3) through the farmland's regional analysis, a depression that has more lowland and easy seeper is suitable for the reclamation of paddy field. It's noted that the extracted classification has an obvious regional farmland type, and the regional features were consistent with the farming cultivation characteristics in the northeast plains; 4) both rice and corn were typical unimodal type growth crops, and the similar growth peak, but the result of the range (rice, 3.8±0.4; upland crops, 3.4±0.3;) noted that: the phenology information of different vegetation types has its own characteristics; this characteristic of the NDVI seasonal variation curve is real.