Fusing Conventional and Deep Learning Features for Hyperspectral Image Change Detection

Abstract

In the field of remote sensing technology Change Detection (CD) is one of the major areas of research. Changes that have occurred on the earth’s surface over time can be detected with this tool. Hyperspectral Image (HSI) data with high spectral resolution can help in identifying subtle changes than the typical multispectral image (MSI), and CD technology has benefitted immensely with the applications of HSI. Traditional CD techniques that used MSI as their input data are challenging to implement on HSI due to the high dimensionality of hyperspectral data. Furthermore, HSI data is affected by a lot of distortion and redundancy, contaminating the spectral-only information for CD purposes. CD accuracy can be improved by extracting the useful features of HSI. In Change Detection algorithms, the initial step is to extract features. Traditionally it is done using arithmetic operation, image transformation, and statistical methods. While some advanced strategies for extracting features are utilizing convolutional neural networks (CNNs) using the deep learning method. In this work, we aimed to integrate the conventional features with CNN extracted features to boost the overall ac-curacy of popular DL-based CD techniques. Spectral matching algorithms are used for extracting conventional features. In addition, appropriate changes are made to the recent deep learning architectures called Three-Directions Spectral–Spatial Convolution neural network (TDSSC) and General End-To-End Neural Network (GETNET), to fuse the conventional features. Farmland, River and USA data sets are used for experimentation. The proposed approach proves to be useful in improving the performance of DL-based CD techniques.