In this paper, we introduce a new unsupervised classifier for Hyperspectral images (HSI) using image segmentation and spectral unmixing. In the proposed method, first the number of classes is considered equal to the number of endmembers. Second, the endmember matrix is defined. Third, the abundance fraction maps are extracted. Fourth, an initial groundtruth is constructed by choosing the location of maximum absolute value of abundance fractions corresponding to each pixel. Fifth, each pixel which has the same eight neighboring (vertical, horizontal and diagonal) class is a good candidate for training data and after that some of good candidate pixels are randomly selected as final training data and remaining pixels are considered as testing data. Finally, support vector machine is applied to the HSI and initial groundtruth is iteratively repeated. In order to validate the efficiency of the proposed algorithm, two real HSI datasets are used. The obtained classification results are compared with some of state-of-the-art initial algorithms and the classification accuracy of the proposed method is close to the supervised algorithms.
In this paper, two new techniques are proposed for manipulation of the microsatellite imaging structures and sensors in order to reduce the micro-satellite’s weight and improve the image quality. Theses satellites generally include three mirrors. First, replacing primary mirror by deformable mirror with appropriate actuators is suggested. In this design, the primary mirror is replaced with deformable mirror (DM) and the secondary mirror can be aligned in the cassegrain design and tertiary mirror could be ignored. Second, by changing the position of sensor, the image quality of different pixels could be changed. Normally, when the sensor is fixed, parts of the image might be blurred, noisy and distorted. Therefore, if the sensor is capable of changing its position, the quality of the distorted pixels will be improved but other parts will become blurred. In this case blurred pixels should be omitted and improved pixels should be saved and final image would be taken form processed pixels. In this paper a new concept of “local focusing” is introduced. This concept aims to process images at a variable distance of a sensor, which can cause the final image quality to become better than the fixed sensor.
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