# GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection

## TL;DR

This paper introduces GETNET, a novel 2D CNN framework for hyperspectral image change detection that effectively handles high-dimensional data and utilizes subpixel abundance information, outperforming existing methods.

## Contribution

The paper proposes a new end-to-end 2D CNN framework with a mixed-affinity matrix for hyperspectral change detection, enhancing feature learning and data fusion capabilities.

## Key findings

- Outperforms most state-of-the-art methods on real datasets.
- Effectively utilizes subpixel abundance information.
- Introduces a new hyperspectral change detection dataset.

## Abstract

Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with the highspectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD). The main contributions of this work are threefold: 1) Mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multi-source information; 2) 2-D CNN is designed to learn the discriminative features effectively from multi-source data at a higher level and enhance the generalization ability of the proposed CD algorithm; 3) A new HSI-CD data set is designed for the objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate the proposed method outperforms most of the state-of-the-arts.