Questions of Concern in Drawing Up a Remote Sensing Change Detection Plan

Nasrabadi, Sayyed Bagher Fatemi; Bagher, Sayyed

doi:10.1007/s12524-019-00997-5

Cited by 3 publications

(6 citation statements)

References 104 publications

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“…In contrast, MCENet achieved optimal quantitative results on the SECOND dataset, with the overall performance evaluation reaching 88.42% OA, 52.21% F1, 22.06% Sek, and 37.41% Score. In the partial visualization results of Figure 9, all the benchmark networks exhibit incompleteness and fragmentation issues in change region detection, and they also have confusion in identifying low vegetation and trees (e.g., (1) in Figure 9). FCCDN, PCFN, and SCDNet encounter segmentation ambiguity when detecting high-density and non-obvious buildings, and the ability to detect partial classes is unsatisfactory, especially playgrounds (e.g., ( 2) and ( 4) in Figure 9).…”

Section: Results On the Second Datasetmentioning

confidence: 99%

“…While BIT overcomes the shortcoming in feature extraction, the lack of connection among branches leads to semantic false classification and miss detection in complex regions (e.g., ( 2) and (3) in Figure 9). The results of BiSRNet reveal that its ability to differentiate between different surfaces with similar spectral features is not highly satisfactory, including semantic confusion between non-vegetated surfaces and buildings (e.g., (1) in Figure 9). Furthermore, BiSRNet has also exhibited similar issues of target omission as the baseline network (e.g., (2) and (3) in Figure 9).…”

Section: Results On the Second Datasetmentioning

confidence: 99%

“…Through the corresponding visualization results of MCENet, it can be observed that the issues encountered by other networks are effectively eliminated or alleviated by MCENet. Most importantly, MCENet demonstrates superior performance in integrity segmenting of change regions and significantly alleviating the pseudo-change phenomena in the surface with intraclass differences (e.g., (1) in Figure 9). Notably, MCENet demonstrates a remarkable ability to completely identify and segment targets, such as water, which other networks failed to achieve accurately.…”

Section: Results On the Second Datasetmentioning

confidence: 99%

“…where L i wss |i ∈ [1,2] denotes the loss function with adaptive weights of the SS branches of T1 and T2, respectively, L bcd denotes the loss function with adaptive weight of the BCD branch; and L a denotes the auxiliary loss function.…”

Section: Loss Functionmentioning

confidence: 99%

“…Remote sensing change detection is the process of recognizing change information about land use/land cover (LULC) by employing a variety of temporal remote sensing images from the same geographic location [1,2]. Currently, change detection techniques have important applications in urban planning [3][4][5], disaster monitoring [6][7][8], land resource management [9][10][11], as well as ecological protection [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Multi-Task Consistency Enhancement Network for Semantic Change Detection in HR Remote Sensing Images and Application of Non-Agriculturalization

Lin,

Wang,

et al. 2023

Remote Sensing

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It is challenging to investigate semantic change detection (SCD) in bi-temporal high-resolution (HR) remote sensing images. For the non-changing surfaces in the same location of bi-temporal images, existing SCD methods often obtain the results with frequent errors or incomplete change detection due to insufficient performance on overcoming the phenomenon of intraclass differences. To address the above-mentioned issues, we propose a novel multi-task consistency enhancement network (MCENet) for SCD. Specifically, a multi-task learning-based network is constructed by combining CNN and Transformer as the backbone. Moreover, a multi-task consistency enhancement module (MCEM) is introduced, and cross-task mapping connections are selected as auxiliary designs in the network to enhance the learning of semantic consistency in non-changing regions and the integrity of change features. Furthermore, we establish a novel joint loss function to alleviate the negative effect of class imbalances in quantity during network training optimization. We performed experiments on publicly available SCD datasets, including the SECOND and HRSCD datasets. MCENet achieved promising results, with a 22.06% Sek and a 37.41% Score on the SECOND dataset and a 14.87% Sek and a 30.61% Score on the HRSCD dataset. Moreover, we evaluated the applicability of MCENet on the NAFZ dataset that was employed for cropland change detection and non-agricultural identification, with a 21.67% Sek and a 37.28% Score. The relevant comparative and ablation experiments suggested that MCENet possesses superior performance and effectiveness in network design.

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Section: Results On the Second Datasetmentioning

confidence: 99%

Section: Results On the Second Datasetmentioning

confidence: 99%

Section: Results On the Second Datasetmentioning

confidence: 99%

Section: Loss Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Multi-Task Consistency Enhancement Network for Semantic Change Detection in HR Remote Sensing Images and Application of Non-Agriculturalization

Lin,

Wang,

et al. 2023

Remote Sensing

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Change and Persistence in an Olive Landscape of Sicily. Geospatial Insights Into Biocultural Heritage

Ferrara,

Sala,

La Mantia

2024

Hum Ecol

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Intercropping landscapes characterised by the presence of certain plant features are usually considered traditional landscapes, important for their biocultural heritage. In recent decades, olive agroforestry systems previously widespread throughout Sicily have transitioned to monocultures alongside the disappearance of other tree species. To analyse the dynamics of land use, we combine mathematical representations and oral narratives of spatial change, focussing our case study on a rural area of inner Sicily, Cozzo del Lampo, characterised by a high presence of century-old olive trees. By using local geonarratives in combination with the results of change detection analysis using historical aerial images spanning 50 years (1955 – 2005), we gain insights into the relationality of people and places over time, highlighting how biocultural heritage is correlated to both local culture and ecology, and demonstrating the value of ecological perspectives to understand past and current human actions. The active engagement of the local population in the interpretation of their own (past-present) practices is key to access new ecological knowledge.

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Remote sensing change detection: a comparative study of spectral distances

Heydari

Nasrabadi

2023

Geocarto International

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Questions of Concern in Drawing Up a Remote Sensing Change Detection Plan

Cited by 3 publications

References 104 publications

A Multi-Task Consistency Enhancement Network for Semantic Change Detection in HR Remote Sensing Images and Application of Non-Agriculturalization

A Multi-Task Consistency Enhancement Network for Semantic Change Detection in HR Remote Sensing Images and Application of Non-Agriculturalization

Change and Persistence in an Olive Landscape of Sicily. Geospatial Insights Into Biocultural Heritage

Remote sensing change detection: a comparative study of spectral distances

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