Hyperspectral change detection based on modification of UNet neural networks

Abstract

The Earth's surface changes continuously due to several natural and humanmade factors. Efficient change detection (CD) is useful in monitoring and managing different situations. The recent rise in launched hyperspectral platforms provides a diversity of spectrum in addition to the spatial resolution required to meet recent civil applications requirements. Traditional multispectral CD algorithms hardly cope with the complex nature of hyperspectral images and their high dimensionality. To overcome these limitations, a CD deep convolutional neural network (CNN) semantic segmentation-based workflow was proposed. The proposed workflow is composed of four main stages, namely preprocessing, training, testing, and evaluation. Initially, preprocessing is performed to overcome hyperspectral image noise and the high dimensionality problem. Random oversampling (ROS), deep learning, and bagging ensemble were incorporated to handle imbalanced dataset. Also, we evaluated the generality and performance of the original UNet model and four variants of UNet, namely residual UNet, residual recurrent UNet, attention UNet, and attention residual recurrent UNet. Three hyperspectral CD datasets were employed in performance assessment for binary and multiclass change cases; all datasets suffer from class imbalance and small region of interest size. Recurrent residual UNet presented the best performance in both accuracy and inference time. Overall, the obtained results imply that deep CNN segmentation models can be utilized to implement efficient CD for hyperspectral imageries. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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

“…The recurrent, residual U-Net demonstrating the most outstanding performance, in terms of accuracy, is 0.99 [149]. Some of the previous methods also had good performances, as can be seen in references [146][147][148][149][150]155,156]. However, ref.…”

“…Using different hyperspectral image datasets, Moustafa et al [149] proposed CD architecture known as attention residual recurrent U-Net (Att R2U-Net). This model used four different variants of U-Net, recurrent U-Net, attention U-Net, and attention residual recurrent U-Net.…”

“…There is limited work on multiple CDs, in which the change class is further subdivided into several types of change. Few studies, such as [149,150,152,153,163], have focused on multiple change maps. Furthermore, hyperdimensional images are used for binary CDs; to the best of our knowledge, only one study has used hyperdimensional images, i.e., [152], for multiclass CD.…”

A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition

“…It is a very common preprocessing method in the field of deep learning. Different from the linear interpolation in [30] and [31], in this work, the bilinear interpolation method is used to adjust the size of the image, which can keep the details of the image features to ensure the integrity of the face image and to avoid the identification accuracy reduction.…”

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“…Simultaneously, it has an end-to-end simple form, which can automatically learn the non-linear characteristics between image pairs, making it the mainstream image analysis research method for researchers [18]. Scholars innovated based on the CNN framework and proposed many networks that exhibit different advantages, such as VGGNet [19], CaffeNet [20], SegNet [21], and U-Net [22,23]. Based on the framework of these models, deep learning methods can be categorized into single-branch network methods [24] and multi-branch network methods [25].…”