Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation. However, most of the research has currently focused on the classification of pixel-based image data for analysis. The study was carried out to develop a multi-category crop hyperspectral image classification system to identify the major crops in the Chiayi Golden Corridor. The hyperspectral image data from CASI (Compact Airborne Spectrographic Imager) were used as the experimental data in this study. A two-stage classification was designed to display the performance of the image classification. More specifically, the study used a multi-class classification by support vector machine (SVM) + convolutional neural network (CNN) for image classification analysis. SVM is a supervised learning model that analyzes data used for classification. CNN is a class of deep neural networks that is applied to analyzing visual imagery. The image classification comparison was made among four crops (paddy rice, potatoes, cabbages, and peanuts), roads, and structures for classification. In the first stage, the support vector machine handled the hyperspectral image classification through pixel-based analysis. Then, the convolution neural network improved the classification of image details through various blocks (cells) of segmentation in the second stage. A series of discussion and analyses of the results are presented. The repair module was also designed to link the usage of CNN and SVM to remove the classification errors.

中文翻译：

---

集成CNN和SVM的创新智能系统：通过高光谱图像数据考虑各种作物

主题图的生成对于科学家和农业工程师分析给定田地中的不同作物非常重要。遥感数据已被广泛接受用于作物研究的广泛领域中的图像分类。但是，目前大多数研究都集中在基于像素的图像数据的分类上以进行分析。进行该研究以开发多类别农作物高光谱图像分类系统，以识别嘉义黄金走廊的主要农作物。来自CASI（紧凑型机载光谱成像仪）的高光谱图像数据用作本研究的实验数据。设计了两阶段分类以显示图像分类的性能。进一步来说，该研究使用支持向量机（SVM）+卷积神经网络（CNN）的多类分类进行图像分类分析。SVM是一种监督学习模型，可以分析用于分类的数据。CNN是一类用于分析视觉图像的深度神经网络。在四种作物（水稻，马铃薯，卷心菜和花生），道路和建筑物之间进行图像分类比较。在第一阶段，支持向量机通过基于像素的分析处理高光谱图像分类。然后，在第二阶段中，卷积神经网络通过分割的各个块（单元）改善了图像细节的分类。提出了一系列的讨论和结果分析。