The classification maps are required for management and for the estimation of agricultural disaster compensation; however, those techniques have yet to be established. Some supervised learning models may allow accurate classification. In this study, the Random Forest (RF) classifier and the classification and regression tree (CART) were applied to evaluate the potential of multi-temporal TerraSAR-X dual-polarimetric data, on the StripMap mode, for classification of crop type. Furthermore, comparisons of the two algorithms and polarizations were carried out. In the study area, beans, beet, grasslands, maize, potato and winter wheat were cultivated, and these crop types were classified using the data set acquired in 2009. The classification results of RF were superior to those of CART and the overall accuracies were 0.91 to 0.93.

Although classification maps are required for management and for the estimation of agricultural disaster compensation, those techniques have yet to be established. This paper describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X (including TanDEM-X) dual-polarimetric data. In the study area, beans, beets, grasslands, maize, potatoes and winter wheat were cultivated. In this study, classification using TerraSAR-X-derived information was performed. Coherence values, polarimetric parameters and 2 gamma nought values were also obtained and evaluated regarding their usefulness in crop classification. Accurate classification may be possible with currently existing supervised learning models. A comparison between the classification and regression tree (CART), support vector machine (SVM) and random forests (RF) algorithms was performed. Even though J-M distances were lower than 1.0 on all TerraSAR-X acquisition days, good results were achieved (e.g., separability between winter wheat and grass) due to the characteristics of the machine learning algorithm. It was found that SVM performed best, achieving an overall accuracy of 95.0% based on the polarimetric parameters and gamma nought values for HH and VV polarizations. The misclassified fields were less than 100 a in area and 79.5-96.3% were less than 200 a with the exception of grassland. When some feature such as a road or windbreak forest is present in the TerraSAR-X data, the ratio of its extent to that of the field is relatively higher for the smaller fields, which leads to misclassifications.

This article describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X data. In the study area, beans, beets, grasslands, maize, potatoes, and winter wheat were cultivated. Although classification maps are required for both management and estimation of agricultural disaster compensation, those techniques have yet to be established. Some supervised learning models may allow accurate classification. Therefore, comparisons among the classification and regression tree (CART), the support vector machine (SVM), and random forests (RF) were performed. SVM was the optimum algorithm in this study, achieving an overall accuracy of 89.1% for the same-year classification, which is the classification using the training data in 2009 to classify the test data in 2009, and 78.0% for the cross-year classification, which is the classification using the training data in 2009 to classify the data in 2012

This paper describes a method for monitoring winter wheat growth using multi-temporal TerraSAR-X dual-polarimetric data. Six TerraSAR-X HH/VV images were collected in Hokkaido, and the temporal responses to the winter wheat fields were analyzed. The height, moisture content and dry matter of the crops were measured at nearly the same time as TerraSAR-X data was acquired, and the relationships between these parameters and SAR data, including sigma naught and coherence, were studied. Quadratic relationships between the crop height and sigma naught were observed for HH polarization. The determination coefficient was 0.73 and the model had an RMS error of 0.17 dB for the validation data. Coherence is expressed as a regression equation with two explanatory variables: crop height and elongation. Next, the determination coefficient of 0.69 was observed for HH, while the RMS error of coherence was 0.01 for the validation data. The possibility of using the co-polarization ratio of TerraSAR-X to estimate the vegetation's water content was also analyzed and a determination coefficient of 0.70 was obtained. The results confirm that X-band SAR data possess great potential for the development of an operational system for monitoring wheat growth.

ABSTRACT:The notable improvements on performance and low cost of digital cameras and GPS/IMU devices have caused MMSs (Mobile Mapping Systems) to be gradually becoming one of the most important devices for mapping highway and railway networks, generating and updating road navigation data and constructing urban 3D models over the last 20 years. Moreover, the demands for large scale visual street-level image database construction by the internet giants such as Google and Microsoft have made the further rapid development of this technology. As one of the most important sensors, the omni-directional cameras are being commonly utilized on many MMSs to collect panoramic images for 3D close range photogrammetry and fusion with 3D laser point clouds since these cameras could record much visual information of the real environment in one image at field view angle of 360° in longitude direction and 180° in latitude direction. This paper addresses the problem of panoramic epipolar image generation for 3D modelling and mapping by stereoscopic viewing. These panoramic images are captured with Point Grey's Ladybug3 mounted on the top of Mitsubishi MMS-X 220 at 2m intervals along the streets in urban environment. Onboard GPS/IMU, speedometer and post sequence image analysis technology such as bundle adjustment provided high accuracy position and attitude data for these panoramic images and laser data, this makes it possible to construct the epipolar geometric relationship between any two adjacent panoramic images and then the panoramic epipolar images could be generated. Three kinds of projection planes: sphere, cylinder and flat plane are selected as the epipolar images' planes. In final we select the flat plane and use its effective parts (middle parts of base line's two sides) for epipolar image generation. The corresponding geometric relations and results will be presented in this paper.