欧亚国际

来自法国农业科学院的专家利用Airphen多光谱成像系统，发表了题为DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images的文章，文章发表在知名期刊Remote Sensing上。

Hiphen是专注于在植物育种、有效农业领域提供高通量表型系统和服务的公司，公司与 ARVALIS植物研究所、INRA法国农业科学院进行联合科研，开发出了相关系统和方法。Hiphen公司也向植物育种领域的育种公司、作物生产团体以及其它客户提供技术服务。自2014年成立以来，Hiphen以其先进的团队以及研究技术屡获业界大奖和殊荣，其先进的产品和服务已经获得先进客户认可，Hiphen致力于成为第四次农业革命领域的领军企业，其合作伙伴广泛分布于各地。

目前利用Hiphen设备和系统发表的文章已经超过几十篇。北京欧亚国际科技有限公司是其中国区合作伙伴，负责其系列产品在中国市场的推广、销售和售后服务。

DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images

Abstract and Figures

The form of a remote sensing index is generally empirically defined, whether by choosing specific reflectance bands, equation forms or its coefficients. These spectral indices are used as preprocessing stage before object detection/classification. But no study seems to search for the best form through function approximation in order to optimize the classification and/or segmentation. The objective of this study is to develop a method to find the optimal index, using a statistical approach by gradient descent on different forms of generic equations. From six wavebands images, five equations have been tested, namely: linear, linear ratio, polynomial, universal function approximator and dense morphological. Few techniques in signal processing and image analysis are also deployed within a deep-learning framework. Performances of standard indices and DeepIndices were evalsuated using two metrics, the dice (similar to f1-score) and the mean intersection over union (mIoU) scores. The study focuses on a specific multispectral camera used in near-field acquisition of soil and vegetation surfaces. These DeepIndices are built and compared to 89 common vegetation indices using the same vegetation dataset and metrics. As an illustration the most used index for vegetation, NDVI (Normalized Difference Vegetation Indices) offers a mIoU score of 63.98% whereas our best models gives an analytic solution to reconstruct an index with a mIoU of 82.19%. This difference is significant enough to improve the segmentation and robustness of the index from various external factors, as well as the shape of detected elements.