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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Innovative Multi Pcnn Based Network for Green Area Monitoring - Identification and Description of Nearly Indistinguishable Areas - In Hyperspectral Satellite Images
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Innovative Multi Pcnn Based Network for Green Area Monitoring - Identification and Description of Nearly Indistinguishable Areas - In Hyperspectral Satellite Images

机译：基于Multi Pcnn的创新型绿地监控网络-高光谱卫星图像中几乎无法区分的区域的识别和描述

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The paper presents an original neural network approach for region of interest detection and classification in multi-spectral satellite images. The proposed method uses a sequence of Pulse Coupled Neural Networks that identifies plausible regions of interest. These regions are passed to a dimension reduction algorithm, Principle Component Analysis, in order to generate the input data for a Support Vector Machine classifier, that validates the data. The algorithm's parameters are optimized using a Genetic Algorithm. The algorithm is designed to distinguish regions that are extremely similar, such as parks in a city that has entire districts made up of houses with yards. The algorithm has been tested on images provided by the Sentinel-2 satellite, and it proved that it can recall 76.85% of the pixels marked as park in the ground truth data, which was obtained from Open Street Map.
机译：本文提出了一种用于多光谱卫星图像中感兴趣区域检测和分类的原始神经网络方法。所提出的方法使用了一个脉冲耦合神经网络序列，该序列识别了可能的感兴趣区域。将这些区域传递到降维算法（主成分分析），以生成支持向量机分类器的输入数据，以验证数据。使用遗传算法优化算法的参数。该算法旨在区分极为相似的区域，例如城市中的公园，整个区域都由带院子的房屋组成。该算法已在Sentinel-2卫星提供的图像上进行了测试，并证明它可以调用从“开放街道地图”获得的地面真实数据中标记为“停放”的像素的76.85％。

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