A study of winter rape extraction at sub-pixel fusing multi-source data based on Artificial Neural Networks:A case study of Jianghan and Dongting Lake Plain
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Abstract:With pixel un-mixing, the omission of pixel caused by mixed pixel can be resolved so as to improve the classification accuracy. But the trouble is only the proportion of each end member object in one pixel which can be got through pixel un-mixing, while the spatial distribution of is uncertain. The objective of this study is to introduce the sub-pixel mapping based on spatial attraction model and explore some issues of it, such as neighboring pixels selection and spatial attraction normalization. As the experiments proven, the sub-pixel mapping could get better result by the eight neighboring pixels selecting mode and normalizing by sub-pixel mode in most cases.
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The interdigitation concept demonstration utilized lc[60 K] ~ 15 &mgr;m HgCdTe pixels in a 96 × 96 array format. Each pixel consisted of four interdigitated sub-pixels. Electronic circuitry on the ROIC deselects defective sub-pixels. High detector response is maintained across the pixel, even if one or two interdigitated sub-pixels are deselected, because interdigitation provides that the predominance of minority carriers photogenerated in the pixel is collected by the selected sub-pixels. An interdigitated sub-pixel is deselected where there is a short in at least one of the detectors of the sub-pixel. The configuration of the interdigitated sub-pixels for a pixel is selected such that photogenerated charge carriers generated anywhere within the pixel would be collected by any adjacent, interdigitated sub-pixels within the same pixel that are not deselected because the diffusion length for the charge carriers is long enough. Deselected interdigitated sub-pixels are disconnected so that no charge will be collected on deselected sub-pixels. Therefore, only detectors of adjacent selected interdigitated sub-pixels collect substantially all of the photogenerated charges corresponding to the impinging radiation. Photoresponse modeling of the interdigitated subpixel approach was performed. An example is that for a 20 &mgr;m diffusion length, the calculated QE changed from 85 % with 0 sub-pixels deselected, to 78 % with 1 sub-pixel, 67 % with two sub-pixels and 48 % with three sub-pixels deselected. A good comparison has been obtained between modeled and measured performance as a function of sub-pixel deselect.
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K-pass pixel value ordering (PVO) is an effective reversible data hiding (RDH) technique. In k-pass PVO, the complexity measurement may lead to a weak estimation result because the unaltered pixels in a block are excluded to estimate block complexity. In addition, the prediction-error is computed without considering the location relationship of the second largest and largest pixels or the second smallest and smallest pixels. To this end, an improved RDH technique is proposed in this paper to enhance the embedding performance. The improvement mainly lies in the following two aspects. First, some pixels in a block, which are excluded from data hiding in some existing RDH methods, are exploited together with the neighborhood surrounding this block to increase the estimation accuracy of local complexity. Second, the remaining pixels in a block, i.e., three largest and three smallest pixels are involved in data embedding. Taking three largest pixels for example, when the difference between the largest and third largest pixels is relatively large (e.g., > 1), we improve k-pass PVO by considering the location relationship of the second largest and largest pixels. The advantage of doing this is that the difference valued 3 between the maximum and the second largest pixel which is shifted in k-pass PVO, is able to carry 1 bit data in our method. In other words, a larger amount of pixels are able to carry data bits in our scheme compared with k-pass PVO. Abundant experimental results reveal that the proposed method achieves preferable embedding performance compared with the previous work, especially when a larger payload is required.
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Pixel mixture is a technique to reduce the read-out time of an image by mixing multiple pixel values on an imager. Ordinary pixel mixtures mix the values of equicolor pixels. However, the equicolor pixels are not contiguous on the Bayer pattern. Mixing non-contiguous pixels degrades the resolution of the mixed image. This paper proposes a novel pixel mixture method which we call a "neighbor pixel mixture". The resolution of the neighbor pixel mixture image is superior to that of existing pixel mixtures. The proposed method mixes the values of the neighbor pixels with different colors. In the proposed method, a weighted average is used for the mixing operation, whereas ordinary pixel mixtures apply a simple average. We also discuss a guideline to design weights for averaging
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Bad pixel correction on pixelated solid-state detectors typically uses the average of the direct neighboring pixels (AVG) to derive the value of a bad pixel. However, the AVG approach was suboptimal for high resolution imaging. Therefore, we developed a least gradient approach (LGA) in this work. In the LGA approach, the gradients of the image in a 5 $\times$
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Sub-pixel mapping of remotely sensed imagery is often performed by assuming that land cover is spatially dependent both within and between image pixels. Intra- and inter-pixel dependencies are two widely used approaches to represent different land-cover spatial dependencies at present. However, merely using intra- or inter-pixel dependence alone often fails to fully describe land-cover spatial dependence, making current sub-pixel mapping models defective. A more reasonable object for sub-pixel mapping is maximizing both intra- and inter-pixel dependencies simultaneously instead of using only one of them. In this article, the differences between intra- and inter-pixel dependencies are discussed theoretically, and a novel sub-pixel mapping model aiming to maximize hybrid intra- and inter-pixel dependence is proposed. In the proposed model, spatial dependence is formulated as a weighted sum of intra-pixel dependence and inter-pixel dependence to satisfy both intra- and inter-pixel dependencies. By application to artificial and synthetic images, the proposed model was evaluated both visually and quantitatively by comparing with three representative sub-pixel mapping algorithms: the pixel swapping algorithm, the sub-pixel/pixel attraction algorithm, and the pixel swapping initialized with sub-pixel/pixel attraction algorithm. The results showed increased accuracy of the proposed algorithm when compared with these traditional sub-pixel mapping algorithms.
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In this paper, a new sub-pixel mapping algorithm is proposed based on sub-pixel/sub-pixel spatial attraction model (SSSAM). Different from the original sub-pixel/pixel spatial attraction model (SPSAM), the SSSAM considers the spatial distribution of each sub-pixel within neighbor pixels, when calculating the spatial attractions for sub-pixels within the centre pixel. Then the attractions are used to determine the class values of these sub-pixels. Two experiments on three artificial images and one real remote sensing image are processed. Both of the results show that compared with traditional SPSAM, the proposed method can produce sub-pixel mapping results with higher accuracy.
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For high‐quality display, a novel pixel structure design‐MIS (main in sub) pixel is proposed. Normal 8‐domain pixel of vertical alignment display is divided into two parts: main‐pixel and sub‐pixel, and these two parts are separated from upper and lower. However, in MIS pixel, main‐pixel is surrounded by sub‐pixel. Through experiments and simulation, we find that the panel with MIS pixel has wider viewing angle, higher liquid crystal efficiency than that with normal pixel.
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Although the number of pixels in image sensors is increasing exponentially, production techniques have only been able to linearly reduce the probability that a pixel will be defective. The result is a rapidly increasing probability that a sensor will contain one or more defective pixels. The defect pixel detection and defect pixel correction are operated separately but the former must employ before the latter is in use. Traditional detection scheme, which finds the defect pixels during manufacturing, is not able to discover the spread defect pixels years late. Consequently, the lifetime and robust defect pixel detection technique, which identifies the fault pixels when camera is in use, is more practical and developed. The paper presents a two stages dead pixel detection technique without complicated mathematic computations so that the embedded devices can easily implement it. Using six dead pixel types are tested and the experimental result indicates that it can be accelerated more than four times the detection time.
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