Sized satellite images (pyramids) are not widely used in classification due to their dissimilarity with respect to the original image. But varied size images can reduce the cost, the time and mass storage of the classification system and can give us an initial impression of the result. This research work is an investigation to study the dependency upon varied size images in classification instead of using the original satellite image. The reference map is prepared to study the performance of the proposed system. Then, the varied size image is constructed for each band of the satellite image. Then, the classification is carried out using competitive learning neural networks (CLNN) method for all digital image pyramids, either the original satellite image or its sized images. The last step is the evaluation of the studied elements such as accuracy, classification time and storage volume. Some resized images are useful in such application, so the study suggests which size suits such applications.

中文翻译：

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研究在多光谱卫星图像分类中使用数字高斯金字塔的潜力

定尺寸的卫星图像（金字塔）由于与原始图像不同，因此并未广泛用于分类。但是不同尺寸的图像可以降低分类系统的成本、时间和海量存储，并且可以给我们一个结果的初步印象。这项研究工作是研究分类中对不同尺寸图像的依赖性而不是使用原始卫星图像的调查。准备参考图以研究所提出系统的性能。然后，为卫星图像的每个波段构建不同尺寸的图像。然后，使用竞争学习神经网络 (CLNN) 方法对所有数字图像金字塔进行分类，无论是原始卫星图像还是其大小的图像。最后一步是评估所研究的元素，例如准确性，分类时间和存储量。一些调整大小的图像在此类应用程序中很有用，因此该研究建议哪种尺寸适合此类应用程序。