tailieunhanh - Humanitarian Demining Part 5

Tham khảo tài liệu 'humanitarian demining part 5', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Multi-sensor Data Fusion Based on Belief Functions and Possibility Theory 111 Close Range Antipersonnel Mine Detection and Remote Sensing Mined Area Reduction classifier or detector by comparing the diagonal elements in all matrices for each class. In the illustrated example the best detections according to the confusion matrix of each classifier or detector are detailed in Subsection . They provide the inputs of the combination step and a simple maximum operator performs well for this step. This approach is very fast. It uses only a part of the information which could also be a drawback if this part is not chosen appropriately. Some weights have to be tuned which may need some user interaction in some cases. Although it may sound somewhat ad hoc it is interesting to show what we can get by using the best parts of all classifiers. . Knowledge Introduction and Spatial Regularization Knowledge inclusion is one of the main powers of our algorithms with respect to the commercial ones. This aspect has led to a lot of work in SMART at different levels. Note that knowledge on the classifiers their behaviors etc. is already included in the previous steps. At this step we use only the pieces of knowledge that directly provide information on the landcover classification. Other pieces of knowledge such as mine reports etc. are not directly related to classes of interest but rather to the dangerous areas and are thus included in the danger map construction which follows the fusion. Several pieces of knowledge proved to be very useful at this step. They concern on the one hand some sure detection. Some detectors are available for roads and rivers which provide areas or lines that surely belong to these classes. There is almost no confusion but some parts can be missing. Then these detections can be imposed on the classification results. This is simply achieved by replacing the label of each pixel in the decision image by the label of the detected class if this pixel is .

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