Change Detection of Remote Sensing Images Based on Attention Mechanism

Chen, Long; Zhang, Dezheng; Li, Peng; Lv, Peng

doi:10.1155/2020/6430627

Cited by 19 publications

(7 citation statements)

References 32 publications

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“…en, use tanh to process the state of nerve cells, and get the output of information through the output gate [31,32]. To minimize the training error, a gradient descent method can be used such as applying a temporal inverse transfer algorithm, which can be used to modify the weights each time based on errors:…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

Natural Language Description Generation Method of Intelligent Image Internet of Things Based on Attention Mechanism

Ouyang

2022

Security and Communication Networks

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With the rapid development of Internet of Things technology, the image data on the Internet are growing at an amazing speed. How to describe the semantic content of massive image data is facing great challenges. Attentional mechanisms originate from the study of human vision. In cognitive science, due to bottlenecks in information processing, humans selectively attend to a portion of all information while ignoring the rest of the visible information. This study mainly discusses the natural language description generation method of Internet of Things intelligent image based on attention mechanism. In this study, a CMOS sensor based on Internet of Things technology is used for image data acquisition and display. FPGA samples cis16bit parallel port data, writes FIFO, stores image data, and then transmits it to host computer for display through network interface. In order to minimize the value of cross-entropy loss function, maximum-likelihood estimation is used to maximize the joint probability of word sequences in the language model when sentence descriptions are generated using the encoder-decoder framework. At each moment, in addition to image features, additional text features are input. Image feature vector and text feature vector are weighted and summed by attention mechanism at each time. In decoding, the attention mechanism gives each image region feature weight, and the long-term and short-term memory network decodes in turn, but the long-term and short-term memory network has limited decoding ability. We use bidirectional long-term and short-term memory network instead of long-term memory network, and dynamically focus on context information through forward LSTM and reverse LSTM. The specificity of the proposed network is 5% higher than that of the 3D convolution residual link network. The results show that the performance of image description model is improved by inputting image context and text context into long-term memory network decoder.

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Section: Long Short-term Memory Networkmentioning

confidence: 99%

Natural Language Description Generation Method of Intelligent Image Internet of Things Based on Attention Mechanism

Ouyang

2022

Security and Communication Networks

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“…This is one of the challenging tasks and with the increasing amount of multi-temporal RS images has become more popular. At-DL was used in 7 papers to detect changes in general [110,111], in buildings [51], or any other objects [81,112]. (vi) Other tasks, such as image dehazing [113], digital elevation model (DEM) void filling [114], and SAR image despeckling [115] were addressed with At-DL in 9 papers.…”

Section: Overview Of the Reviewed Papersmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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“…Chen et al [27] presented a spatial-temporal attention-based change-detection method (STA), which simulates the spatial-temporal relationship by the self-attention module. Chen et al [28] proposed a novel network that paid more attention to the regions with significant changes and improved the model's anti-noise capability. Ma et al [29] presented a dual-branch interactive spatial-channel collaborative attention enhancement network (SCCA-net) for multi-resolution classification.…”

Section: Introductionmentioning

confidence: 99%

A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

Zhang

Liu

et al. 2022

Remote Sensing

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Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researchers’ attention. Most deep-learning-based methods only explore the spatial–spectral features simultaneously. However, we assume the key spatial-change areas should be more important, and attention should be paid to the specific bands which can best reflect the changes. To achieve this goal, we propose the spatial–spectral joint attention network (SJAN). Compared with traditional methods, SJAN introduces the spatial–spectral attention mechanism to better explore the key changed areas and the key separable bands. To be more specific, a novel spatial-attention module is designed to extract the spatially key regions first. Secondly, the spectral-attention module is developed to adaptively focus on the separable bands of land-cover materials. Finally, a novel objective function is proposed to help the model to measure the similarity of learned spatial–spectral features from both spectrum amplitude and angle perspectives. The proposed SJAN is validated on three benchmark datasets. Comprehensive experiments have been conducted to demonstrate the effectiveness of the proposed SJAN.

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Change Detection of Remote Sensing Images Based on Attention Mechanism

Cited by 19 publications

References 32 publications

Natural Language Description Generation Method of Intelligent Image Internet of Things Based on Attention Mechanism

Natural Language Description Generation Method of Intelligent Image Internet of Things Based on Attention Mechanism

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

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