ABSTRACT

Crop classification is a significant requirement to estimate crop area, structure, and spatial distribution, as well as provide important input parameters for crop yield models. Different techniques were considered in this system and providing betterment in automation. But none of them gave promising results. So here, a Convolutional Deep Neural Network (CDNN) is proposed to identify the crop areas with the help of Synthetic-Aperture Radar (SAR) satellite images as well as the cultivation status of the crop. First, in training phase, the segmented image of the crop is preprocessed using HLS, then feature is extracted using BRIEF, then, they are classified using CDNN. Then after in testing phase, the input SAR image from the database is further processed using MRG algorithm and classified centered on the training results. After classification, the cultivation status of each classified crop can be identified by taking the Euclidean distance (ED) betwixt the standard parameters and resultant parameters of a specific crop. After computing ED, the ED is contrasted with the threshold value and the cultivation status of a particular crop can be identified. The results are analyzed to ascertain the performance shown by the proposed technique with other existent techniques.

摘要

作物分类是估算作物面积，结构和空间分布的重要要求，并为作物产量模型提供重要的输入参数。在该系统中考虑了不同的技术，这些技术提供了自动化方面的改进。但是他们都没有给出令人满意的结果。因此，在这里，提出了一个卷积深度神经网络（CDNN）来利用合成孔径雷达（SAR）卫星图像以及作物的种植状况来识别作物区域。首先，在训练阶段，使用HLS对作物的分割图像进行预处理，然后使用BRIEF提取特征，然后使用CDNN对它们进行分类。然后在测试阶段之后，使用MRG算法对来自数据库的输入SAR图像进行进一步处理，并以训练结果为中心进行分类。分类后 可以通过将特定作物的标准参数和结果参数之间的欧氏距离（ED）进行求和，来确定每种分类农作物的栽培状态。在计算ED之后，将ED与阈值进行对比，并且可以识别出特定作物的栽培状态。分析结果以确定所提出的技术与其他现有技术所显示的性能。