Change Detection on Multi-Spectral Images Based on Feature-level U-Net

Abstract

This paper proposes a change detection algorithm on multi-spectral images based on feature-level U-Net. A low-complexity pan-sharpening method is proposed to employ not only panchromatic images, but also multi-spectral images for enhancing the performance of the deep neural network. The high-resolution multi-spectral (HRMS) images are then fed into the proposed feature-level U-Net. The proposed feature-level U-Net consists of two-stages: a feature-level subtracting network and U-Net. The feature-level subtracting network is used to extract dynamic difference images (DI) for the use of low-level and high-level features. By employing this network, the performance of change detection algorithms can be improved with a smaller number of layers for U-Net with a low computational complexity. Furthermore, the proposed algorithm detects small changes by taking benefits of both geometrical and spectral resolution enhancement and adopting an intensity-hue-saturation (IHS) pan-sharpening method. A modified of IHS pan-sharpening algorithm is introduced to solve spectral distortion problem by applying mean filtering in high frequency. We found that the proposed change detection on HRMS images gives a better performance compared to existing change detection algorithms by achieving an average F-1 score of 0.62, a percentage correct classification (PCC) of 98.78%, and a kappa of 61.60 for test datasets. INDEX TERMS Convolutional neural network, deep learning, remote sensing, satellite images, change detection.

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

“…Wiratama et al [140] used a feature-level U-Net to create a robust land cover change segmentation approach in HR multispectral images. The suggested pan-sharpening was introduced by applying a low-pass filter to remove spectral distortion in an IHS highfrequency image.…”

Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

“…With the progress and development of machine learning theory, various machine learning techniques such as support vector machine (SVM) [37][38][39][40][41] and random forest (RF) [42][43][44][45][46] have been applied for change detection. In recent years, with the rapid development of big data and artificial intelligence technology, deep learning methods such as deep belief networks, convolutional neural network and twin network [47][48][49][50][51][52] has been applied to change detection, which improves the accuracy of change detection greatly. There are two types of change detection by supervision.…”

Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features

“…Compared with above two types of change detection methods, deep learning technique shows a potential advantage for feature extraction and semantic segmentation in change detection tasks [7][8][9][10][11]. Earlier architectures, such as the Deep Belief Network [12] and Multilayer Perceptron [13], only consist of fully connected layers, requiring an update of large numbers of parameters when using images as input, which consumes additional computing resources.…”