In hyperspectral images, each pixel contains information from hundreds of frequency bands, which is helpful for improving the classification accuracy; however, this also reduces the processing rate. In conventional pixelbased
image classification, pixel data rarely represent spatial correlations between objects. Thus, an object-based deep
neural network (DNN) was developed to classify hyperspectral images.
The research data included images of Indian Pines (Indiana, USA) and Salinas (California, USA) recorded by an airborne
visible/infrared imaging spectrometer, along with the associated ground truths for image classification. First, the minimum noise fraction technique was applied to separate the noise from the images in order to provide useful information and reduce the need of calculations. Then, object-based image analysis was implemented to explore spatial correlation. Simple linear iterative clustering was then used to categorize the objects based on size and compactness of each object. Finally, applying DNN classification can solve the salt-and-pepper effect, and requires less computing time than pixel-based classification, especially for a large area. The classification accuracy and kappa of the objectbased
classification were higher than those of the pixel-based classification method. The proposed method was verified
using an image of Salinas with a classification accuracy of 94.62 % and less computation time.
Key Words: hyperspectral image, minimum noise fraction, simple linear iterative clustering, deep neural network