Classification of Hyperspectral Images with CNN in Agricultural Lands

Abstract

Hyperspectral images (HSI) offer detailed spectral reflectance information about sensed objects through provision of information on hundreds of narrow spectral bands. HSI have a leading role in a broad range of applications, such as in forestry, agriculture, geology, and environmental sciences. The monitoring and management of agricultural lands is of great importance for meeting the nutritional and other needs of a rapidly and continuously increasing world population. In relation to this, classification of HSI is an effective way for creating land use and land cover maps quickly and accurately. In recent years, classification of HSI using convolutional neural networks (CNN), which is a sub-field of deep learning, has become a very popular research topic and several CNN architectures have been developed by researchers. The aim of this study was to investigate the classification performance of CNN model on agricultural HSI scenes. For this purpose, a 3D-2D CNN framework and a well-known support vector machine (SVM) model were compared using the Indian Pines and Salinas Scene datasets that contain crop and mixed vegetation classes. As a result of this study, it was confirmed that use of 3D-2D CNN offers superior performance for classifying agricultural HSI datasets.

Deep Learning in Medical Hyperspectral Images: A Review

“…Hyperspectral imaging (HSI) originally originated from remote sensing and was used by NASA for various applications with richer spectral information as well as spatial information than conventional optical images. It has been broadly applied in diverse fields of remote sensing data [1,2], agriculture [3,4], image enhancement [5], horticultural protection [6,7], disaster monitoring [8], food safety and assessment [9,10], and medicine [11][12][13], showing its great potential.…”