Remote Sensing Image Change Detection With Transformers

Chen, Hao; Qi, Zhongyang; Shi, Zhenwei

doi:10.1109/tgrs.2021.3095166

Cited by 584 publications

(495 citation statements)

References 45 publications

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“…Transformers can learn explicit long-range dependencies, which are particularly suitable for pixel-wise labeling tasks in positionunconstrained remote sensing images. Some promising works on applying transformers in the remote sensing field have recently emerged, including image classification [19], change detection [6], image caption generation [64], hyperspectral image classification [20,65], segmentation [66], and so forth. SETR [27] expands transformers on the natural image segmentation task, which conducts a standard transformer encoder and a convolutional decoder.…”

Section: Transformer-based Methodsmentioning

confidence: 99%

“…Bazi et al [19] applied vision transformers to remote sensing scene classification and achieve a good performance. Chen et al [6] employ an efficient transformer method for remote sensing image change detection and achieve a state-of-the-art performance, compared with other methods.…”

Section: Transformer-based Methodsmentioning

confidence: 99%

“…Additionally, in keeping with our goal, we have considered the distribution and amount of buildings per image while determining the number of sparse tokens. In the transformer encoder and decoder, we set the number of multi-head to k s /8 for spatial transformers and k c /4 for channel transformers to learn richer feature representation in different subspaces [6,18,27].…”

Section: Parameter Settingmentioning

confidence: 99%

“…Building extraction plays an important role in many applications, such as urban planning [1], population estimation [2,3], economic activities distribution [4], disaster reporting [5], illegal building construction, and so forth. It can also be used as an essential prerequisite for downstream tasks like change detection from remote sensing images [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Building Extraction from Remote Sensing Images with Sparse Token Transformers

2021

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Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from high computational complexity and memory usage. In this paper, we explored the potential of using transformers for efficient building extraction. We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark building extraction datasets. Since single buildings in remote sensing images usually only occupy a very small part of the image pixels, we represent buildings as a set of “sparse” feature vectors in their feature space by introducing a new module called “sparse token sampler”. With such a design, the computational complexity in transformers can be greatly reduced over an order of magnitude. We refer to our method as Sparse Token Transformers (STT). Experiments conducted on the Wuhan University Aerial Building Dataset (WHU) and the Inria Aerial Image Labeling Dataset (INRIA) suggest the effectiveness and efficiency of our method. Compared with some widely used segmentation methods and some state-of-the-art building extraction methods, STT has achieved the best performance with low time cost.

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Section: Transformer-based Methodsmentioning

confidence: 99%

Section: Transformer-based Methodsmentioning

confidence: 99%

Section: Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Building Extraction from Remote Sensing Images with Sparse Token Transformers

2021

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“…Transformer has also achieved good results in various remote sensing tasks. For example, Chen et al [ 19 ] applied the transformer encoder to the modern change detection in RS. While SpectralFormer [ 20 ] rethinks hyperspectral image classification from a sequential perspective with transformers.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

Chen

Liu

et al. 2022

Sensors

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Aircraft detection in remote sensing images (RSIs) has drawn widespread attention in recent years, which has been widely used in the military and civilian fields. While the complex background, variations of aircraft pose and size bring great difficulties to the effective detection. In this paper, we propose a novel aircraft target detection scheme based on small training samples. The scheme is coarse-to-fine, which consists of two main stages: region proposal and target identification. First, in the region proposal stage, a circular intensity filter, which is designed based on the characteristics of the aircraft target, can quickly locate the centers of multi-scale suspicious aircraft targets in the RSIs pyramid. Then the target regions can be extracted by adding bounding boxes. This step can get high-quality but few candidate regions. Second, in the stage of target identification, we proposed a novel rotation-invariant feature, which combines rotation-invariant histogram of oriented gradient and vector of locally aggregated descriptors (VLAD). The feature can characterize the aircraft target well by avoiding the impact of its rotation and can be effectively used to remove false alarms. Experiments are conducted on Remote Sensing Object Detection (RSOD) dataset to compare the proposed method with other advanced methods. The results show that the proposed method can quickly and accurately detect aircraft targets in RSIs and achieve a better performance.

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Feature Difference Enhancement Fusion for Remote Sensing Image Change Detection

Pei

Peng

et al. 2022

Pattern Recognition and Computer Vision

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Remote Sensing Image Change Detection With Transformers

Cited by 584 publications

References 45 publications

Building Extraction from Remote Sensing Images with Sparse Token Transformers

Building Extraction from Remote Sensing Images with Sparse Token Transformers

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

Feature Difference Enhancement Fusion for Remote Sensing Image Change Detection

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