Land Use and Land Cover Classification Using Hyperspectral Imagery: Evaluating the Performance of Spectral Angle Mapper, Support Vector Machine and Random Forest

Christovam, Luiz E.; Pessoa, Guilherme Gomes; Shimabukuro, Milton Hirokazu; Galo, Maria de Lourdes Bueno Trindade

doi:10.5194/isprs-archives-xlii-2-w13-1841-2019

Cited by 35 publications

(8 citation statements)

References 28 publications

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“…Furthermore, Krishna et al (2018) argue that hyperspectral remote sensing is a solution to the problem of mixed pixels and spectral similarity associated with optically derived imagery, due to the numerous bands of hyperspectral data that provide spectral information per pixel, allowing for the discrimination of feature classes, yielding improved LULC classification accuracy. However, this imagery comes with high dimensionality and huge volumes of data resulting in the Hughes phenomenon (Christovam et al, 2019). In addition, hyperspectral imagery is relatively difficult to process compared to multispectral imagery, demanding bigger storage capacity (Zhang & Sriharan, 2005).…”

Section: Discussionmentioning

confidence: 99%

The use of remote sensing and GIS for land use and land cover mapping in Eswatini: A Review

Simelane¹,

Hansen²,

Munghemezulu³

2022

SA J of Geomatics

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Remote sensing and GIS are often used to assess spatiotemporal variations for land use/land cover (LULC) monitoring and classification. While LULC monitoring and classification has been undertaken in Eswatini, little attention has been given to ascertaining covered thematic areas, methods of image classification, and approaches and techniques for improving classification accuracy. This paper summarises and synthesizes the progress made in the Kingdom of Eswatini regarding the application of remote sensing and GIS in LULC monitoring and classification. Eight thematic areas (water resources mapping; land degradation; forestry; wildfire detection; urban expansion; crop production; disease surveillance; general mapping) dominate evaluated LULC studies, employing three LULC classification methods (classic; manual; advanced). While some studies include strengths and weaknesses of LULC classification techniques applied, others do not. This review shows that only two advanced classifiers (random forest; object-based) were identified from the reviewed articles. In addition, reviewed studies applied only two approaches (use of multi temporal data; fine spatial resolution data) and three techniques (use of ancillary data; post-classification procedure; the use of multisource data) for improving classification accuracy. Furthermore, the review finds that limited LULC investigations have been covered in Eswatini with a specific focus on the Sustainable Development Goals (SDGs). As such, this review recommends 1) the inclusion of higher resolution imagery for mapping purposes, 2) the adaptation of strengths and weaknesses for any image classification technique employed in future publications, 3) the use of more varied approaches and techniques for improving classification accuracy and area estimates, 4) inclusion of standard errors or confidence intervals for error-adjusted area estimates as part of accuracy assessment reporting, 5) the application of advanced image classifiers, and 6) the application of Earth Observation (EO) Analysis Ready Data (ARD) in the production of information for the support of the SDGs.

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Section: Discussionmentioning

confidence: 99%

The use of remote sensing and GIS for land use and land cover mapping in Eswatini: A Review

Simelane¹,

Hansen²,

Munghemezulu³

2022

SA J of Geomatics

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“…Data samples that were closest to the hyperplane were used to measure the margin, referred to as support vectors (SV) [7]. Since it considers only SVs, SVM can be useful with limited training sets, where collecting training data is costly in terms of both time and resources [9,10]. In most classification problems, such as remotely sensed images, classes cannot be separated by linear hyperplanes.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Classification of Hyperspectral Images with CNN in Agricultural Lands

Seyrek

Uysal

2021

The 1st International Electronic Conference on Agronomy

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Hyperspectral images (HSI) offer detailed spectral reflectance information about sensed objects through provision of information on hundreds of narrow spectral bands. HSI have a leading role in a broad range of applications, such as in forestry, agriculture, geology, and environmental sciences. The monitoring and management of agricultural lands is of great importance for meeting the nutritional and other needs of a rapidly and continuously increasing world population. In relation to this, classification of HSI is an effective way for creating land use and land cover maps quickly and accurately. In recent years, classification of HSI using convolutional neural networks (CNN), which is a sub-field of deep learning, has become a very popular research topic and several CNN architectures have been developed by researchers. The aim of this study was to investigate the classification performance of CNN model on agricultural HSI scenes. For this purpose, a 3D-2D CNN framework and a well-known support vector machine (SVM) model were compared using the Indian Pines and Salinas Scene datasets that contain crop and mixed vegetation classes. As a result of this study, it was confirmed that use of 3D-2D CNN offers superior performance for classifying agricultural HSI datasets.

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“…Supervised classification algorithms allow data classification after a prior learning process using training data. This approach has been thoroughly and successfully used in land-cover-classification-related studies [58][59][60]. We aimed to classify the study area into two classes, "chestnut" (chestnut plantations), and "other".…”

Section: Detection Of Chestnut Plantationsmentioning

confidence: 99%

Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases

et al. 2020

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Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest of Spain) is an area where property is extremely divided into small parcels. European chestnut (Castanea sativa) plantations are an important source of income there; however, it is often difficult to obtain information about them due to their small size and scattered distribution. Therefore, we selected a Galician region with a high presence of chestnut plantations as a case study area in order to locate and characterize small plantations using open-access data. First, we detected the location of chestnut plantations applying a supervised classification for a combination of: Sentinel-2 images and the open-access low-density Light Detection and Ranging (LiDAR) point clouds, obtained from the untapped open-access LiDAR Spanish national database. Three classification algorithms were used: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. We later characterized the plots at the tree-level using the LiDAR point-cloud. We detected individual trees and obtained their height applying a local maxima algorithm to a point-cloud-derived Canopy Height Model (CHM). We also calculated the crown surface of each tree by applying a method based on two-dimensional (2D) tree shape reconstruction and canopy segmentation to a projection of the LiDAR point cloud. Chestnut plantations were detected with an overall accuracy of 81.5%. Individual trees were identified with a detection rate of 96%. The coefficient of determination R2 value for tree height estimation was 0.83, while for the crown surface calculation it was 0.74. The accuracy achieved with these open-access databases makes the proposed procedure suitable for acquiring knowledge about the location and state of chestnut plantations as well as for monitoring their evolution.

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Land Use and Land Cover Classification Using Hyperspectral Imagery: Evaluating the Performance of Spectral Angle Mapper, Support Vector Machine and Random Forest

Cited by 35 publications

References 28 publications

The use of remote sensing and GIS for land use and land cover mapping in Eswatini: A Review

The use of remote sensing and GIS for land use and land cover mapping in Eswatini: A Review

Classification of Hyperspectral Images with CNN in Agricultural Lands

Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases

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