Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

Mora, André; Santos, Tiago M. A.; Łukasik, Szymon; Silva, João M. N.; Falcão, António J.; Fonseca, José; Ribeiro, Rita A.

doi:10.3390/info8040147

Cited by 28 publications

(16 citation statements)

References 33 publications

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“…The non-parametric machine learning methods create models that may be hard to understand in a physical sense. Decreases in robustness may also occur with complex models, for example, artificial neural networks through overfitting [50]. In the second phase, the land cover classifications were compiled from the continuous predictions of the probabilities of the land cover classes using a hierarchical rule based category classification ( Figure 5).…”

Section: Classification Of the Medium Resolution Satellite Datamentioning

confidence: 99%

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

et al. 2018

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The applicability of optical and synthetic aperture radar (SAR) data for land cover classification to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) MRV (measuring, reporting and verification) services was tested on a tropical to sub-tropical test site. The 100 km by 100 km test site was situated in the State of Chiapas in Mexico. Land cover classifications were computed using RapidEye and Landsat TM optical satellite images and ALOS PALSAR L-band and Envisat ASAR C-band images. Identical sample plot data from Kompsat-2 imagery of one-metre spatial resolution were used for the accuracy assessment. The overall accuracy for forest and non-forest classification varied between 95% for the RapidEye classification and 74% for the Envisat ASAR classification. For more detailed land cover classification, the accuracies varied between 89% and 70%, respectively. A combination of Landsat TM and ALOS PALSAR data sets provided only 1% improvement in the overall accuracy. The biases were small in most classifications, varying from practically zero for the Landsat TM based classification to a 7% overestimation of forest area in the Envisat ASAR classification. Considering the pros and cons of the data types, we recommend optical data of 10 m spatial resolution as the primary data source for REDD MRV purposes. The results with L-band SAR data were nearly as accurate as the optical data but considering the present maturity of the imaging systems and image analysis methods, the L-band SAR is recommended as a secondary data source. The C-band SAR clearly has poorer potential than the L-band but it is applicable in stratification for a statistical sampling when other image types are unavailable.

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Section: Classification Of the Medium Resolution Satellite Datamentioning

confidence: 99%

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

et al. 2018

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“…Remote sensing can monitor many crops and vegetation parameters through images at various wavelengths. In the past, remote sensing was often based on satellite images [4,5] or images acquired by using manned aircraft in order to monitor vegetation status at specific growth stages. However, satellite imagery is often not the best option because of the low spatial resolution of images acquired and the restrictions of the temporal resolutions as satellites are not always available to capture the necessary images.…”

Section: Introductionmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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“…Only then is possible to work with all RGB cameras regardless of their resolution; • Static Texture: To extract the terrain's static textures, Gray-Level Co-Occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM) were used to calculate features capable of providing information to classify terrain types; • Dynamic Texture: To identify the movement of each terrain type, an extraction of dynamic textures was performed using the Optical flow concept; • Classification: The outputs generated by the static and dynamic extraction phases are turned into inputs for a Neural Network (NN) [32] tasked with classifying the terrain the UAV is flying over. Machine learning techniques have already been proven to be efficient for terrain classification [33][34][35]. In this work, an NN was used, namely a Multilayer Perceptron (MLP) architecture.…”

Section: Proposed System Modelmentioning

confidence: 99%

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

et al. 2019

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The ability to precisely classify different types of terrain is extremely important for Unmanned Aerial Vehicles (UAVs). There are multiple situations in which terrain classification is fundamental for achieving a UAV's mission success, such as emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation. Previous research works describe different terrain classification approaches mainly using static features from RGB images taken onboard UAVs. In these works, the terrain is classified from each image taken as a whole, not divided into blocks; this approach has an obvious drawback when applied to images with multiple terrain types. This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV's downwash effect: Static textures-Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic textures-Optical Flow method. This system has been fully implemented using the OpenCV library, and the GLCM algorithm has also been partially specified in a Hardware Description Language (VHDL) and implemented in a Field Programmable Gate Array (FPGA)-based platform. In addition to these feature extraction algorithms, a neural network was designed with the aim of classifying the terrain into one of four classes. Lastly, in order to store and access all the classified terrain information, a dynamic map, with this information was generated. The system was validated using videos acquired onboard a UAV with an RGB camera.Several methods for terrain classification have been proposed in different works. Texture algorithms, such as those proposed in [2][3][4][5], have been widely recommended to emphasize the high and low frequencies of the images, supporting image classification. Other algorithms use color information to classify terrains, such as presented in [6], which is able to distinguish four different terrain types within an image. During this process, each channel's pixel is divided by the square root of its own three channels intensity. The final result will emphasize the color that most represents the terrain type (eg, blue for water). Additionally, frequency domain [7,8], segmentation [6,9,10], bayesian network [11], and Hyperspectal Images [12] can also be used in terrain classification.Other types of sensors such as LiDAR [13][14][15][16] can complement the classification decision. Algorithms that use laser scanners proved to be qualified to accurately distinguish between water and non-water terrains [13][14][15][16]. However, shallow water terrains increase the decision error due to laser reflection, which leads to a misclassification as non-water terrain.Although prior research work has proposed many good solutions for terrain classification, there is still a gap regarding the study of dynamic terrain. The previously mentioned algorithms suffer from a high sensitivity to changes in the environment, mainly due to changes in brightness, color and texture. Recent works [17,18] ...

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Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

Cited by 28 publications

References 33 publications

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

A Review on UAV-Based Applications for Precision Agriculture

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

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