Classification of remotely sensed imagery with the integration of spatial context should be a constructive way of improved accuracy in image classification. An efficient spatial–spectral classification approach with colorimetric edge preservation of spatial–spectral modeling is processed. This research paper consists of three phases first, and the multispectral imagery [Captured by unmanned aerial vehicles (UAV)] is classified using a per-pixel classification method, i.e., support vector machine (SVM). Then, the classified image is constituted as various probability thematic maps and an edge preservation using chromaticity mapping is conducted on each probability map, with the principal component analysis of the multispectral imagery (Captured by UAVs) using as the color or gray-guidance imagery. Finally, from the edge preservation probability images, the classes of all the pixels are specified with the maximum probabilities. Significantly, the accuracy assessment of image classification improved in a short computational time.

中文翻译：

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边缘保留方法的空间光谱图像分类

结合空间背景的遥感影像分类应该是提高影像分类精度的一种建设性方式。处理具有空间光谱建模的比色边缘保留的有效空间光谱分类方法。本研究论文首先分为三个阶段，使用逐像素分类方法，即支持向量机（SVM）对多光谱图像[由无人驾驶飞行器（UAV）捕获]进行分类。然后，将分类图像构成为各种概率专题图，并对每个概率图使用色度映射进行边缘保留，以多光谱图像（无人机捕获）的主成分分析作为彩色或灰度引导图像。最后，从边缘保留概率图像中，用最大概率指定所有像素的类别。重要的是，图像分类的准确性评估在很短的计算时间内得到了提高。