Pansharpening aims to fuse a low-resolution multispectral image with a high-resolution panchromatic image to create a multispectral image with high spatial and spectral resolution. The intensity-hue-saturation (IHS) fusion method transforms an image from RGB space to IHS space. This paper reports a method to improve the spectral resolution of a final multispectral image. The proposed method implies two modifications on the basic IHS method to improve the sharpness of the final image. First, the paper proposes a method based on a genetic algorithm to find the weight of each band of multispectral image in the fusion process. Later on, a texture-based technique is proposed to save the spectral information of the final image with respect to the texture boundaries. Spectral quality metrics in terms of SAM, SID, Q-average, RASE, RMSE, CC, ERGAS and UIQI are used in our experiments. Experimental results on IKONOS and QuickBird data show that the proposed method is more efficient than the original IHS-based fusion approach and some of its extensions, such as IKONOS IHS, edge-adaptive IHS and explicit band coefficient IHS, in preserving spectral information of multispectral images.
This paper reports research in compensating for position inaccuracy and flexibility problems in a loosely coupled robot arm by means of machine learning methods. Error sources in the system are studied and problems are described. A number of methods for eliminating problems due to inaccuracy in 2D-space have been previously reported. These methods have been extended to address the problem in 3 dimensions. Utilizing a real time monitoring system, the end-effector position is sensed. The collected data is converted into appropriate error maps. Using a novel machine learning method, the error maps are used to predict system errors and compensate for them. The machine learning engine generalizes the data for the points between the sampled points. The experimental results are presented.
This paper reports a new approach to error compensation for inaccuracies in position control for the end-effector of a Robot Arm. The goal is to overcome the problem of inaccuracy, due to the low precision in manufacturing of Robot Arms and the flexibility of their structure, by means of machine intelligence. Utilizing a mesh sensory system, a Real Time Monitoring System is designed. The position of the end-effector is monitored in real time and the positioning data for the end-effector is collected. A direction independent filtering system is designed to eliminate the noise from the collected data. After extracting the error map from the collected data, a novel Proportional Keen Approximation Method is implemented to generalize the error map. One of the main features of this method is the elimination of the training stage as in the Artificial Neural Networks. Using the knowledge obtained from the maps, the system compensates for the errors.
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