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Open AccessArticle

Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge

by Hugo Costa, Pedro Benevides, Francisco D. Moreira, Daniel Moraes and Mário Caetano

Remote Sens. 2022, 14(8), 1865; https://doi.org/10.3390/rs14081865 - 13 Apr 2022

Cited by 40 | Viewed by 6654

Abstract

Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology developed to produce a prototype relative to 2018 as the first land cover map of the future annual map series (COSsim). A total of thirteen land cover classes are represented, including the most important tree species in Portugal. The mapping approach developed includes two levels of spatial stratification based on landscape dynamics. Strata are analysed independently at the higher level, while nested sublevels can share data and procedures. Multiple stages of analysis are implemented in which subsequent stages improve the outputs of precedent stages. The goal is to adjust mapping to the local landscape and tackle specific problems or divide complex mapping tasks in several parts. Supervised classification of Sentinel-2 time series and post-classification analysis with expert knowledge were performed throughout four stages. The overall accuracy of the map is estimated at 81.3% (±2.1) at the 95% confidence level. Higher thematic accuracy was achieved in southern Portugal, and expert knowledge significantly improved the quality of the map. Full article

(This article belongs to the Special Issue Advances in Satellite-Based Land Cover Mapping and Monitoring)

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15 pages, 32733 KB

Open AccessArticle

Extending Contrastive Learning to Unsupervised Redundancy Identification

by Jeongwoo Ju, Heechul Jung and Junmo Kim

Appl. Sci. 2022, 12(4), 2201; https://doi.org/10.3390/app12042201 - 20 Feb 2022

Viewed by 3147

Abstract

Modern deep neural network (DNN)-based approaches have delivered great performance for computer vision tasks; however, they require a massive annotation cost due to their data-hungry nature. Hence, given a fixed budget and unlabeled examples, improving the quality of examples to be annotated is a clever step to obtain good generalization of DNN. One of key issues that could hurt the quality of examples is the presence of redundancy, in which the most examples exhibit similar visual context (e.g., same background). Redundant examples barely contribute to the performance but rather require additional annotation cost. Hence, prior to the annotation process, identifying redundancy is a key step to avoid unnecessary cost. In this work, we proved that the coreset score based on cosine similarity (cossim) is effective for identifying redundant examples. This is because the collective magnitude of the gradient over redundant examples exhibits a large value compared to the others. As a result, contrastive learning first attempts to reduce the loss of redundancy. Consequently, cossim for the redundancy set exhibited a high value (low coreset score). We first viewed the redundancy identification as the gradient magnitude. In this way, we effectively removed redundant examples from two datasets (KITTI, BDD10K), resulting in a better performance in terms of detection and semantic segmentation. Full article

(This article belongs to the Topic Machine and Deep Learning)

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14 pages, 2666 KB

Open AccessArticle

An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes

by Yucheng Lin, Zhigang Chen, Jia Wu and Leilei Wang

Algorithms 2018, 11(8), 119; https://doi.org/10.3390/a11080119 - 6 Aug 2018

Cited by 9 | Viewed by 4659

Abstract

The mobility of nodes leads to dynamic changes in topology structure, which makes the traditional routing algorithms of a wireless network difficult to apply to the opportunistic network. In view of the problems existing in the process of information forwarding, this paper proposed a routing algorithm based on the cosine similarity of data packets between nodes (cosSim). The cosine distance, an algorithm for calculating the similarity between text data, is used to calculate the cosine similarity of data packets between nodes. The data packet set of nodes are expressed in the form of vectors, thereby facilitating the calculation of the similarity between the nodes. Through the definition of the upper and lower thresholds, the similarity between the nodes is filtered according to certain rules, and finally obtains a plurality of relatively reliable transmission paths. Simulation experiments show that compared with the traditional opportunistic network routing algorithm, such as the Spray and Wait (S&W) algorithm and Epidemic algorithm, the cosSim algorithm has a better transmission effect, which can not only improve the delivery ratio, but also reduce the network transmission delay and decline the routing overhead. Full article

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