Land Cover Prediction from Satellite Imagery Using Machine Learning Techniques

Panda, Abhisek; Singh, Akhilesh Kumar; Kumar, K. Vignesh; Kumar, Akash; Uddeshya,; Swetapadma, Aleena

doi:10.1109/icicct.2018.8473241

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Similarly, the application of SMOTE improved the classification results. Other examples of the successful application of SMOTE in remote sensing can be found in [25,26].…”

Section: Informed Resamplingmentioning

confidence: 99%

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

et al. 2019

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The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource management. The ability to build robust automatic classifiers and produce accurate maps can have a significant impact on the way we manage and optimize natural resources. The difficulty in achieving these results comes from many different factors, such as data quality and uncertainty. In this paper, we address the imbalanced learning problem, a common and difficult conundrum in remote sensing that affects the quality of classification results, by proposing Geometric-SMOTE, a novel oversampling method, as a tool for addressing the imbalanced learning problem in remote sensing. Geometric-SMOTE is a sophisticated oversampling algorithm which increases the quality of the instances generated in previous methods, such as the synthetic minority oversampling technique. The performance of Geometric- SMOTE, in the LUCAS (Land Use/Cover Area Frame Survey) dataset, is compared to other oversamplers using a variety of classifiers. The results show that Geometric-SMOTE significantly outperforms all the other oversamplers and improves the robustness of the classifiers. These results indicate that, when using imbalanced datasets, remote sensing researchers should consider the use of these new generation oversamplers to increase the quality of the classification results.

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“…Similarly, the application of SMOTE improved the classification results. Other examples of the successful application of SMOTE in remote sensing can be found in [25,26].…”

Section: Informed Resamplingmentioning

confidence: 99%

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Remote sensing imaging classification, anomaly detection, and prediction issues can be resolved using machine learning modeling. Maximum likelihood classifiers, Markov chain models, support vector machines, Markov chain models and other machine learning algorithms have been historically used to classify images [23], [7], [8], [11], [24]. But, in recent years, with the advancement in the performance of computing units, several approaches in the context of deep learning have been used.…”

Section: B Lulc Classification Methodsmentioning

confidence: 99%

“…With this information, it is possible to better understand the effects of natural phenomena and human use of the landscape [40]. Maps can be also used to assess urban growth, model water quality issues, predict flood and storm surge impacts, track wetland loss and potential sea level rise impacts, prioritize conservation efforts, and compare land-cover changes with environmental effects or linkages in socioeconomic changes like population growth [23].…”

Section: A Lulc Classificationmentioning

confidence: 99%

Hybrid Deep Learning Architecture for Land Use: Land Cover Images Classification with a Comparative and Experimental Study

Wiam¹,

Khouloud²,

Bouchra³

et al. 2022

IJACSA

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Deep Learning algorithms have become more popular in computer vision, especially in the image classification field. This last has many applications such as moving object detection, cancer detection, and the classification of satellite images, also called images of land use-land cover (LULC), which are the scope of this paper. It represents the most commonly used method for decision making in the sustainable management of natural resources at various geographical levels. However, methods of satellite images analysis are expensive in the computational time and did not show good performance. Therefore, this paper, on the one hand, proposes a new CNN architecture called Modified MobileNet V1 (MMN) based on the fusion of MobileNet V1 and ResNet50. On the other hand, it presents a comparative study of the proposed model and the most used models based on transfer learning, i.e. MobileNet V1, VGG16, DenseNet201, and ResNet50. The experiments were conducted on the dataset Eurosat, and they show that ResNet50 results emulate the other models.

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“…k-NN is the most accurate method. [1] Shahrin, F., et al [2020] Most of Bangladesh's people work in agriculture, which accounts for a large portion of the country's total employment. However, agricultural yields are unreliable and farming infrastructure is inefficient, which has a negative impact on food security.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery

Sai

Rao

Vani

et al. 2022

IJRITCC

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The growing world is more expensive to estimate land use, road length, and forest cover using a plant-scaled ground monitoring system. Satellite imaging contains a significant amount of detailed uncertain information. Combining this with machine learning aids in the organization of these data and the estimation of each variable separately. The resources necessary to deploy Machine learning technologies for Remote sensing images, on the other hand, restrict their reach ability and application. Based on satellite observations which are notably underutilised in impoverished nations, while practical competence to implement SIML might be restricted. Encoded forms of images are shared across tasks, and they will be calculated and sent to an infinite number of researchers who can achieve top-tier SIML performance by training a regression analysis onto the actual data. By separating the duties, the proposed SIML solution, MOSAIKS, shapes SIML approachable and global. A Featurization stage turns remote sensing data into concise vector representations, and a regression step makes it possible to learn the correlations which are specific to its particular task which link the obtained characteristics to the set of uncertain data.

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Land Cover Prediction from Satellite Imagery Using Machine Learning Techniques

Cited by 6 publications

References 7 publications

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

Hybrid Deep Learning Architecture for Land Use: Land Cover Images Classification with a Comparative and Experimental Study

Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery

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