Abstract

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

摘要

农作物的鉴定是农业领域的一个重要课题。高光谱遥感数据对于作物特征提取和分类非常有用。遥感数据是一种非结构化数据，卷积神经网络 (CNN) 可以有效地处理非结构化数据。本文介绍了使用从 AVIRIS 传感器获得的 Indian Pines 标准数据集和从 EO-1hyperion 传感器获得的研究区域数据集对 CNN 进行作物分类的评估。通过在不同参数上训练模型来调整优化的 CNN。它已与两种分类算法进行了比较：深度神经网络 (DNN) 和卷积自动编码器。根据测试结果，与其他两种方法相比，所提出的优化 CNN 模型提供了更好的结果。CNN 给出了 97 ± 0。