Weakly Supervised Silhouette-based Semantic Scene Change Detection

Satoh, Ken; Shibuya, Mikiya; Wang, Weimin

doi:10.1109/icra40945.2020.9196985

Cited by 48 publications

(64 citation statements)

References 36 publications

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“…The previously developed silhouette-based semantic change detection network (SSCDNet) [11] is largely similar to our proposed method. The SSCDNet is a method for simultaneously detecting changed regions and estimating the class of the changed object.…”

Section: Sscdnetmentioning

confidence: 99%

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Semantic Segmentation And Change Detection By Multi-Task U-Net

Tsutsui

Hirakawa

Yamashita

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Change detection involves extracting the changed regions from images taken of the same place at different times. Potential applications are automatically updating of HD maps or identifying damages caused by natural disasters. However, conventional change detection methods merely detect changed regions without classifying them. In this paper, we propose a change detection method that can estimate the object class of a changed region. Our method extends a U-Net as a multi-task learning framework and estimates changed regions and semantic segmentation simultaneously. We propose using the pixel-wise classification probabilities of semantic segmentation for detecting changed regions rather than the conventional L2 norm-based difference of feature maps. In our experiments, we show that our method can improve change detection performance and estimate the classes of corresponding changed objects.

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Section: Sscdnetmentioning

confidence: 99%

“…CSCDNet [11]: This method consists of CSCDNet for detecting change regions and SSCDNet for estimating the object class of the changed regions. We compare the change detection accuracy of CSCDNet.…”

Section: Experimental Settingsmentioning

confidence: 99%

Semantic Segmentation And Change Detection By Multi-Task U-Net

Tsutsui

Hirakawa

Yamashita

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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“…In comparison, the data size of change detection is more than 100 times smaller than that of the remote sensing image-classification dataset. Second, the image pairs or image sequences used for change detection are often obtained from different viewpoints [26][27][28][29]. In other words, it is difficult to capture a scene from similar viewpoints every time in remote sensing change detection.…”

Section: Introductionmentioning

confidence: 99%

Change Capsule Network for Optical Remote Sensing Image Change Detection

Chen

Zhou

et al. 2021

Remote Sensing

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Change detection based on deep learning has made great progress recently, but there are still some challenges, such as the small data size in open-labeled datasets, the different viewpoints in image pairs, and the poor similarity measures in feature pairs. To alleviate these problems, this paper presents a novel change capsule network by taking advantage of a capsule network that can better deal with the different viewpoints and can achieve satisfactory performance with small training data for optical remote sensing image change detection. First, two identical non-shared weight capsule networks are designed to extract the vector-based features of image pairs. Second, the unchanged region reconstruction module is adopted to keep the feature space of the unchanged region more consistent. Third, vector cosine and vector difference are utilized to compare the vector-based features in a capsule network efficiently, which can enlarge the separability between the changed pixels and the unchanged pixels. Finally, a binary change map can be produced by analyzing both the vector cosine and vector difference. From the unchanged region reconstruction module and the vector cosine and vector difference module, the extracted feature pairs in a change capsule network are more comparable and separable. Moreover, to test the effectiveness of the proposed change capsule network in dealing with the different viewpoints in multi-temporal images, we collect a new change detection dataset from a taken-over Al Udeid Air Basee (AUAB) using Google Earth. The results of the experiments carried out on the AUAB dataset show that a change capsule network can better deal with the different viewpoints and can improve the comparability and separability of feature pairs. Furthermore, a comparison of the experimental results carried out on the AUAB dataset and SZTAKI AirChange Benchmark Set demonstrates the effectiveness and superiority of the proposed method.

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“…Scene Change Detection (SCD) aims to compare images captured at different times to identify changes that occur in the image. Till now, scene change detection has found various application scenarios, such as land cover monitoring [1], medical diagnosis [2], urban landscape analysis and autonomous driving [3][4] [5]. With the development of learning systems for image classification and semantic segmentation, Convolutional Neural Networks (CNNs) have been widely used in computer vision tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Similar to dense image semantic segmentation, scene change detection also addresses pixelwise detection. Because of this, current networks designed for scene change detection are mostly based on CNNs and encoder-decoder-architectures [1][3] [5][9] [10].…”

Section: Introductionmentioning

confidence: 99%

DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection

Chen¹,

Yang²,

Stiefelhagen³

2021

Preprint

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Street scene change detection continues to capture researchers' interests in the computer vision community. It aims to identify the changed regions of the paired street-view images captured at different times. The state-of-the-art network based on the encoder-decoder architecture leverages the feature maps at the corresponding level between two channels to gain sufficient information of changes. Still, the efficiency of feature extraction, feature correlation calculation, even the whole network requires further improvement. This paper proposes the temporal attention and explores the impact of the dependencyscope size of temporal attention on the performance of change detection. In addition, based on the Temporal Attention Module (TAM), we introduce a more efficient and light-weight version -Dynamic Receptive Temporal Attention Module (DRTAM)and propose the Concurrent Horizontal and Vertical Attention (CHVA) to improve the accuracy of the network on specific challenging entities. On street scene datasets 'GSV', 'TSUNAMI' and 'VL-CMU-CD', our approach gains excellent performance, establishing new state-of-the-art scores without bells and whistles, while maintaining high efficiency applicable in autonomous vehicles.

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Weakly Supervised Silhouette-based Semantic Scene Change Detection

Cited by 48 publications

References 36 publications

Semantic Segmentation And Change Detection By Multi-Task U-Net

Semantic Segmentation And Change Detection By Multi-Task U-Net

Change Capsule Network for Optical Remote Sensing Image Change Detection

DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection

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