A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery

Alhassan, Victor; Henry, Christopher J.; Ramanna, Sheela; Storie, Christopher D.

doi:10.1007/s00521-019-04349-9

Cited by 64 publications

(43 citation statements)

References 27 publications

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“…Although satellites offer practically continuous monitoring, classification of land cover commonly uses multispectral data at a single observation date (Jia et al, 2014;Mahdianpari et al, 2018;Alhassan et al, 2019). However, this approach can induce confusion in the classification of the different existing land-cover patterns in dry seasonal forests, due to the similarity of the vegetation's spectral response in specific phenological stages (Karnieli, 2002).…”

Section: Introductionmentioning

confidence: 99%

Evaluating single and multi-date Landsat classifications of land-cover in a seasonally dry tropical forest

Andrade

Cunha

Silva

et al. 2021

Remote Sensing Applications: Society and Environment

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Accurate information on the land cover is crucial for efficient monitoring and development of environmental studies in the Brazilian Caatinga forest. It is the largest tropical seasonal forest in South America, presenting high biodiversity and is under intense anthropogenic disturbance. Caatinga's land cover is heterogeneous, and rainfall is its primary phenological regulator, presenting mainly deciduous species. Different land-cover patterns show distinct spatial responses to climate and soils changes and modify their physical properties over time. Rainfall is highly variable over time and space, but seasonally concentrated between 2 to 4 months. Therefore, distinguishing the different patterns of land cover through medium spatial-resolution remote sensing, such as the Landsat image series, is challenging, due to the particularities of the climate-vegetation interaction. Two remote sensing approaches have a high potential for efficient land-cover mapping in Caatinga: single and multi-date imagery. The heterogeneity of the land cover of this environment can contribute to a better performance of multispectral approaches, although it is normally applied for singledate images. In a land-cover mapping effort in Caatinga, the temporal factor gains relevance, and the use of time series can bring advantages, but, in general, this approach uses vegetation index, losing multispectral information. This manuscript aims to assess the accuracies and advantages of single-date multispectral and multi-date Normalized Difference Vegetation Index (NDVI) approaches in land-cover classification. Both approaches use the Random Forest method, and the results are evaluated based on samples collected during field surveys. Results indicate that land-cover classification obtained from multi-date NDVI performs better than single-date multispectral data. The lower performance observed for single-date multispectral classification is due to similarities in spectral responses: targets of deciduous vegetation lose their foliage and can be misread as non-vegetated areas. Meanwhile, an accurate classification by time series of plant clusters in seasonal forests allows incorporating seasonal variability of land-cover classes during the rainy and dry seasons, as well as transitions between seasons.

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Section: Introductionmentioning

confidence: 99%

Evaluating single and multi-date Landsat classifications of land-cover in a seasonally dry tropical forest

Andrade

Cunha

Silva

et al. 2021

Remote Sensing Applications: Society and Environment

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“…Examples of these methodologies are adaptive reflectance fusion models, maximum likelihood classifiers, decision trees, convolutional neural networks (CNNs), deep neural networks (DNNs), etc. [21][22][23][24][25]. In some cases, DNNs are also used for pre-trained the data, in other cases the image classification for LULC is accomplished via more complex machine learning (ML) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images

et al. 2020

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Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land-use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard elements, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote-sensing images, normalization and histogram equalization methods are used to improve the quality of the images. Then, a hybrid optimization is accomplished by using the local Gabor binary pattern histogram sequence (LGBPHS), the histogram of oriented gradient (HOG) and Haralick texture features, for the feature extraction from the selected images. The benefits of this hybrid optimization are a high discriminative power and invariance to color and grayscale images. Next, a human group-based particle swarm optimization (PSO) algorithm is applied to select the optimal features, whose benefits are a fast convergence rate and ease of implementation. After selecting the optimal feature values, a long short-term memory (LSTM) network is utilized to classify the LULC classes. Experimental results showed that the human group-based PSO algorithm with a LSTM classifier effectively well differentiates the LULC classes in terms of classification accuracy, recall and precision. A maximum improvement of 6.03% on Sat 4 and 7.17% on Sat 6 in LULC classification is reached when the proposed human group-based PSO with LSTM is compared to individual LSTM, PSO with LSTM, and Human Group Optimization (HGO) with LSTM. Moreover, an improvement of 2.56% in accuracy is achieved, compared to the existing models, GoogleNet, Visual Geometric Group (VGG), AlexNet, ConvNet, when the proposed method is applied.

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“…Pre-activation architecture implementation is carried out utilizing the activation feature moving BN and Revised Linear Units (ReLU). The pre-activation residual block is expressed as(7)…”

mentioning

confidence: 99%

Hybrid Enriched Stacked Auto Encoder and FFO-MPARCNN Algorithm for the Multispectral Image LULC Classification

Bhuvaneswari¹,

T²,

Arshad³

et al. 2021

Preprint

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The satellite imagery classification task is fundamental to spatial knowledge discovery. Land Cover and Land usage (LULC) maps are created using a variety of image classification techniques, making it easier to conduct research on spatial and ecological processes as well as human activities. One of the most well-known applications of geographical monitoring is LULC classification. Owing to its improved feature learning and feature expression capacity, the convolutional neural network (CNN) has made several breakthroughs in feature extraction as well as classification of multispectral images in recent times as compared with conventional machine learning approaches. But on the other hand, standard CNN models have certain disadvantages, for instance, a large number of layers, which contribute to difficult computing costs. The Hybrid Enriched Stacked Auto Encoder and Pre-Activated Residual Convolutional Neural Network combined with a Fruit Fly Optimization Algorithm (HESAE-FFO-MPARCNN) has formulated where FFO used to optimise parameters and thus enhance the accuracy of classification in this work to tackle this issue. The designed FFO-MPARCNN model with its modified hyperparameters produces higher classical models as PB-RNN, ResNet and FHS-DBN for computational efficiency and accuracy of classification.

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A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery

Cited by 64 publications

References 27 publications

Evaluating single and multi-date Landsat classifications of land-cover in a seasonally dry tropical forest

Evaluating single and multi-date Landsat classifications of land-cover in a seasonally dry tropical forest

Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images

Hybrid Enriched Stacked Auto Encoder and FFO-MPARCNN Algorithm for the Multispectral Image LULC Classification

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