Detection and mapping of agriculture seasonal variations with deep learning–based change detection using Sentinel-2 data

Singh, Gurwinder; Singh, Sartajvir; Sethi, Ganesh Kumar; Sood, Vishakha

doi:10.1007/s12517-022-10105-6

Cited by 14 publications

(4 citation statements)

References 60 publications

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“…Over the past decade, extensive work has been done using multispectral datasets through numerous classifier or change detection models 28 , 70 . Despite various advantages, the multispectral dataset is not able to produce the desired results because of a minimal number of spectral bands.…”

Section: Resultsmentioning

confidence: 99%

“…Over the past decade, extensive work has been done using multispectral datasets through numerous classifier or change detection models. 28,70 Despite various advantages, the multispectral dataset is not able to produce the desired results because of a minimal number of spectral bands. Many studies extended the applicability of remote sensing in the extraction of earth surface parameters using hyperspectral datasets over water bodies and wetlands, plant diseases, and forest monitoring.…”

Section: Discussionmentioning

confidence: 99%

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Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Dahiya

Gupta

Singh

2023

J. Appl. Rem. Sens.

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Remote sensing is an effective way to analyze land surface changes on regular basis globally. In the previous literature, numerous change detection models were developed to detect the multitemporal or seasonal variations using different optical datasets, such as multispectral and hyperspectral. But there are many challenges involved in the processing of numerous bands, especially in the case of hyperspectral datasets, such as computational constraints and radiometric/atmospheric distortion. A simple framework-based artificial neural network (ANN) and postclassification comparison (PCC), named ANN-based PCC (ANPC), has been proposed to detect the multitemporal changes over agricultural land using hyperspectral, i.e., Earth Observation (EO-1) Hyperion and multispectral, i.e., Landsat-8. For cross-referencing, a comparative analysis is performed with respect to well-known PCC-based change detection methods, i.e., random forest-based post-comparison (RFPC) and support vector machine-based post-comparison (SVMPC). Experimental outcomes confirmed the effectiveness of ANPC (with an accuracy of more than 90%) in the extraction of multitemporal changes as compared to RFPC and SVMPC (with an accuracy of <90%). Our study enhances the utilization of the hyperspectral dataset (due to narrow spectral bands) in the extraction of critical information about the earth's surface parameters.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Dahiya

Gupta

Singh

2023

J. Appl. Rem. Sens.

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“…With the gradual improvement in the resolution of existing images, the classification accuracy of tiny features in a small area has further improved. However, the accurate identification of features, especially erosion gullies, is challenging due to issues such as ambiguity in boundary delineation [14], the complexity of features at high resolution, and significant seasonal variations [15]. The object-oriented method views a target object as a unified entity and utilizes its spectral, geometric, and textural features.…”

Section: Introductionmentioning

confidence: 99%

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Xie,

Wang,

Wang

et al. 2024

Sustainability

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Drainage difficulties in the waterlogged areas of sloping cropland not only impede crop development but also facilitate the formation of erosion gullies, resulting in significant soil and water loss. Investigating the distribution of these waterlogged areas is crucial for comprehending the erosion patterns of sloping cropland and preserving black soil resource. In this study, we built varied models based on two stages (one using only deep learning methods and the other combining object-based image analysis (OBIA) with deep learning methods) to identify waterlogged areas using high-resolution remote sensing data. The results showed that the five deep learning models using original remote sensing imagery achieved precision rates varying from 54.6% to 60.9%. Among these models, the DeepLabV3+-Xception model achieved the highest accuracy, as indicated by an F1-score of 53.4%. The identified imagery demonstrated a significant distinction in the two categories of waterlogged areas: sloping cropland erosion zones and erosion risk areas. The former had obvious borders and fewer misclassifications, exceeding the latter in terms of identification accuracy. Furthermore, the accuracy of the deep learning models was significantly improved when combined with object-oriented image analysis. The DeepLabV3+-MobileNetV2 model achieved the maximum accuracy, with an F1-score of 59%, which was 6% higher than that of the model using only original imagery. Moreover, this advancement mitigated issues related to boundary blurriness and image noise in the identification process. These results will provide scientific assistance in managing and reducing the impact in these places.

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“…Change detection in remote sensing is a significant and challenging task that involves identifying differences in land cover or land surface using multi-temporal images of the same geospatial area [1]. It is widely used across various applications, including agricultural land use activities, urban planning, and disaster assessment [2][3][4]. Over the past decade, deep learning has revolutionized remote sensing applications, encompassing tasks like image fusion, land cover classification, and object detection [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

TTNet: A Temporal-Transform Network for Semantic Change Detection Based on Bi-Temporal Remote Sensing Images

Jiang,

Li,

Huang

et al. 2023

Remote Sensing

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Semantic change detection (SCD) holds a critical place in remote sensing image interpretation, as it aims to locate changing regions and identify their associated land cover classes. Presently, post-classification techniques stand as the predominant strategy for SCD due to their simplicity and efficacy. However, these methods often overlook the intricate relationships between alterations in land cover. In this paper, we argue that comprehending the interplay of changes within land cover maps holds the key to enhancing SCD’s performance. With this insight, a Temporal-Transform Module (TTM) is designed to capture change relationships across temporal dimensions. TTM selectively aggregates features across all temporal images, enhancing the unique features of each temporal image at distinct pixels. Moreover, we build a Temporal-Transform Network (TTNet) for SCD, comprising two semantic segmentation branches and a binary change detection branch. TTM is embedded into the decoder of each semantic segmentation branch, thus enabling TTNet to obtain better land cover classification results. Experimental results on the SECOND dataset show that TTNet achieves enhanced performance when compared to other benchmark methods in the SCD task. In particular, TTNet elevates mIoU accuracy by a minimum of 1.5% in the SCD task and 3.1% in the semantic segmentation task.

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Detection and mapping of agriculture seasonal variations with deep learning–based change detection using Sentinel-2 data

Cited by 14 publications

References 60 publications

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

TTNet: A Temporal-Transform Network for Semantic Change Detection Based on Bi-Temporal Remote Sensing Images

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