Evaluation of CNN model by comparing with convolutional autoencoder and deep neural network for crop classification on hyperspectral imagery

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.

Keywords:

• Remote sensing data
• Convolutional Neural Network (CNN)
• Principal Component Analysis (PCA)
• Hyperspectral data
• Deep learning
• Land Use Land Cover (LULC)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Figure 1. (a) Study Area (b) EO-I Hyperion Data of study area.

Figure 2. Flow diagram of important processes in the proposed work.

Figure 3. Architecture of Deep Learning Convolutional Neural Network.