MiCro: Modeling Cross-Image Semantic Relationship Dependencies for Class-Incremental Semantic Segmentation in Remote Sensing Images

Rong, Xuee; Wang, Peijin; Diao, Wenhui; Yang, Yiran; Yin, Wenxin; Zeng, Xuan; Wang, Hongqi; Sun, Xian

doi:10.1109/tgrs.2023.3297203

Cited by 4 publications

(3 citation statements)

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“…3, the first category, known as data-replay methods, involves storing a portion of past training data as exemplar memory such as [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36]. The second category, termed datafree methods, includes methods like [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49]. These methods utilize transfer learning techniques, such as knowledge distillation, to inherit the capabilities of the old model.…”

Section: Semantic Driftmentioning

confidence: 99%

“…Qiu et al [48] use self-attention to capture both within-class and between-class knowledge. Current methods continually explore the indepth distillation manners from class weighted [42], [49], dense intermediate-features alignment [40], [44], class-wise enhancement [147], [148], cross-image relationship modeling [45], prototype rehearsal [27], [53], [101] and cross-scene modeling [149], etc. With respect to the network architecture, some research proves that a stronger backbone is able to improve the distillation performance such as Transformers [49], [150].…”

Section: Regularization-based Mannermentioning

confidence: 99%

“…For instance, Shan et al [151] perform multi-level feature distillation including both soft distillation and hard distillation on feature representation from different layers of networks. MiCro [45] starts from establishing relationship dependencies between images and distills the pairwise pixel relationship dependencies across mini-batch images in the intermediate feature space. The second category is the pre-training manner.…”

Section: Regularization-based Mannermentioning

confidence: 99%

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Ecosystem Health of the Beiyun River Basin (Beijing, China) as Evaluated by the Analytic Hierarchy Process Method

Yuan

Zhao

et al. 2021

Preprint

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Ecosystem services provided by river ecosystems rely on healthy ecosystem structure and ecological processes. The Beijing-Tianjin-Hebei urban is a typical water-deficient area. As an important part of the urban-rural integration construction, evaluating the health status of the Beiyun River Basin and discovering the weak links in the water environment is the basis for improving the health of the basin. In this study, Analytic Hierarchy Process was used to establish an evaluation index system for the Beiyun River Basin from 5 aspects including water quality, biology, habitat, hydrology, and social functions, and to assign weights to the index layer. The evaluation results showed that the health evaluation results of the Beiyun River Basin in 2019 are “sub-healthy”, and the overall health status is gradually worsening from northwest to southeast. In the middle reaches of the region, the evaluation result is "healthy", followed by the upstream, and the downstream is the worst. The results showed that areas with less human interference are in better health. The factors that affect the overall health evaluation status in the basin are the level of nutrition, biodiversity, and vegetation coverage. For the comprehensive management of the Beiyun River, the improvement of water quality and habitat ecological restoration is the key to the health of the upstream ecosystem health status. The improvement of the health status of the downstream should focus on equal emphasis on water quality and quantity, restoration of biodiversity, and improvement of the quality of the riparian ecological environment.

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