1,763 research outputs found
Mapping Chestnut Stands Using Bi-Temporal VHR Data
This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife
Changer: Feature Interaction is What You Need for Change Detection
Change detection is an important tool for long-term earth observation
missions. It takes bi-temporal images as input and predicts "where" the change
has occurred. Different from other dense prediction tasks, a meaningful
consideration for change detection is the interaction between bi-temporal
features. With this motivation, in this paper we propose a novel general change
detection architecture, MetaChanger, which includes a series of alternative
interaction layers in the feature extractor. To verify the effectiveness of
MetaChanger, we propose two derived models, ChangerAD and ChangerEx with simple
interaction strategies: Aggregation-Distribution (AD) and "exchange". AD is
abstracted from some complex interaction methods, and "exchange" is a
completely parameter\&computation-free operation by exchanging bi-temporal
features. In addition, for better alignment of bi-temporal features, we propose
a flow dual-alignment fusion (FDAF) module which allows interactive alignment
and feature fusion. Crucially, we observe Changer series models achieve
competitive performance on different scale change detection datasets. Further,
our proposed ChangerAD and ChangerEx could serve as a starting baseline for
future MetaChanger design.Comment: 11 pages, 5 figure
Multi-scale diff-changed feature fusion network for hyperspectral image change detection.
For hyperspectral images (HSI) change detection (CD), multi-scale features are usually used to construct the detection models. However, the existing studies only consider the multi-scale features containing changed and unchanged components, which is difficult to represent the subtle changes between bi-temporal HSIs in each scale. To address this problem, we propose a multi-scale diff-changed feature fusion network (MSDFFN) for HSI CD, which improves the ability of feature representation by learning the refined change components between bi-temporal HSIs under different scales. In this network, a temporal feature encoder-decoder sub-network, which combines a reduced inception module and a cross-layer attention module to highlight the significant features, is designed to extract the temporal features of HSIs. A bidirectional diff-changed feature representation module is proposed to learn the fine changed features of bi-temporal HSIs at various scales to enhance the discriminative performance of the subtle change. A multi-scale attention fusion module is developed to adaptively fuse the changed features of various scales. The proposed method can not only discover the subtle change of bi-temporal HSIs but also improve the discriminating power for HSI CD. Experimental results on three HSI datasets show that MSDFFN outperforms a few state-of-the-art methods
Implementing bi-temporal properties into various NoSQL database categories
© Research Institute for Intelligent Computer Systems, 2019. NoSQL database systems have emerged and developed at an accelerating rate in the last years. Attractive properties such as scalability and performance, which are needed by many applications today, contributed to their increasing popularity. Time is very important aspect in many applications. Many NoSQL database systems do not offer built in management for temporal properties. In this paper, we discuss how we can embed temporal properties in NoSQL databases. We review and differentiate between the most popular NoSQL stores. Moreover, we propose various solutions to modify data models for embedding bitemporal properties in two of the most popular categories of NoSQL databases (Key-value stores and Column stores). In addition, we give examples of how to represent bitemporal properties using Redis Key-value store and Cassandra column oriented store. This work can be used as basis for designing and implementing temporal operators and temporal data management in NoSQL databases
End-to-end Remote Sensing Change Detection of Unregistered Bi-temporal Images for Natural Disasters
Change detection based on remote sensing images has been a prominent area of
interest in the field of remote sensing. Deep networks have demonstrated
significant success in detecting changes in bi-temporal remote sensing images
and have found applications in various fields. Given the degradation of natural
environments and the frequent occurrence of natural disasters, accurately and
swiftly identifying damaged buildings in disaster-stricken areas through remote
sensing images holds immense significance. This paper aims to investigate
change detection specifically for natural disasters. Considering that existing
public datasets used in change detection research are registered, which does
not align with the practical scenario where bi-temporal images are not matched,
this paper introduces an unregistered end-to-end change detection synthetic
dataset called xBD-E2ECD. Furthermore, we propose an end-to-end change
detection network named E2ECDNet, which takes an unregistered bi-temporal image
pair as input and simultaneously generates the flow field prediction result and
the change detection prediction result. It is worth noting that our E2ECDNet
also supports change detection for registered image pairs, as registration can
be seen as a special case of non-registration. Additionally, this paper
redefines the criteria for correctly predicting a positive case and introduces
neighborhood-based change detection evaluation metrics. The experimental
results have demonstrated significant improvements
Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data
Due to climate and land-use change, natural disasters such as flooding have
been increasing in recent years. Timely and reliable flood detection and
mapping can help emergency response and disaster management. In this work, we
propose a flood detection network using bi-temporal SAR acquisitions. The
proposed segmentation network has an encoder-decoder architecture with two
Siamese encoders for pre and post-flood images. The network's feature maps are
fused and enhanced using attention blocks to achieve more accurate detection of
the flooded areas. Our proposed network is evaluated on publicly available
Sen1Flood11 benchmark dataset. The network outperformed the existing
state-of-the-art (uni-temporal) flood detection method by 6\% IOU. The
experiments highlight that the combination of bi-temporal SAR data with an
effective network architecture achieves more accurate flood detection than
uni-temporal methods.Comment: Accepted in IGARSS202
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