Multispectral Image Analysis using Decision Trees

Kulkarni, Arun D.; Shrestha, Anmol Purna

doi:10.14569/ijacsa.2017.080602

Cited by 13 publications

(8 citation statements)

References 25 publications

(19 reference statements)

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“…We then eliminated collinear predictors, resulting in another slight decrease of the accuracies and explained variances in the dry forest and moist forest compares with a substantial decrease of the rainforest, showing again higher possible effects of overfitting in the rainforest by the accumulation of predictors. The previous examinations have confirmed the high susceptibility of remote sensing analyses to overfit their training set data [100].…”

Section: Map Accuracy Among Predictor Setsmentioning

confidence: 58%

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

et al. 2019

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Developing accurate methods to map vegetation structure in tropical forests is essential to protect their biodiversity and improve their carbon stock estimation. We integrated LIDAR (Light Detection and Ranging), multispectral and SAR (Synthetic Aperture Radar) data to improve the prediction and mapping of canopy height (CH) at high spatial resolution (30 m) in tropical forests in South America. We modeled and mapped CH estimated from aircraft LiDAR surveys as a ground reference, using annual metrics derived from multispectral and SAR satellite imagery in a dry forest, a moist forest, and a rainforest of tropical South America. We examined the effect of the three forest types, five regression algorithms, and three predictor groups on the modelling and mapping of CH. Our CH models reached errors ranging from 1.2–3.4 m in the dry forest and 5.1–7.4 m in the rainforest and explained variances from 94–60% in the dry forest and 58–12% in the rainforest. Our best models show higher accuracies than previous works in tropical forests. The average accuracy of the five regression algorithms decreased from dry forests (2.6 m +/− 0.7) to moist (5.7 m +/− 0.4) and rainforests (6.6 m +/− 0.7). Random Forest regressions produced the most accurate models in the three forest types (1.2 m +/− 0.05 in the dry, 4.9 m +/− 0.14 in the moist, and 5.5 m +/− 0.3 the rainforest). Model performance varied considerably across the three predictor groups. Our results are useful for CH spatial prediction when GEDI (Global Ecosystem Dynamics Investigation lidar) data become available.

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Section: Map Accuracy Among Predictor Setsmentioning

confidence: 58%

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

et al. 2019

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“…Decision trees successfully find application in tasks related with classification using remote sensing data. For example, decision trees are applied to classify land covers using spectral images due to natural approach, when each pixel is analysed independently (Sharma, 2013), (Kulkarni and Shrestha, 2017), (Pooja et al, 2011), (Kulkarni and Lowe, 2016). However, pixel-based methods become ineffective with resolution increase (Veljanovski et al, 2011), but it does not reduce the significance of decision trees as classification method, which found renaissance in processing of shape or segment features.…”

Section: Decision Trees and Remote Sensingmentioning

confidence: 99%

Detection of Man-Made Constructions Using LiDAR Data and Decision Trees

Kodors¹

2019

BJMC

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Real estate monitoring is very important aspect of country economics, but old manual methods of land survey are time and resources consuming processes as geodata actualization tasks. Actual, precise, multidimensional and detailed information is the main instrument of geospatial intelligence to understand current economic situation and to make effective decision. Actualization of geoinformation using remote sensing is the modern approach of the computer age to complete Earth observation and human environment monitoring. This article describes multi-stage classification model, which detects man-made constructions in LiDAR point cloud. Proposed classification model applies decision tree and geometrical features of shape to remove noises. The goal of study is to experimentally compare decision trees with crisp and fuzzy logic (ID3 algorithms) to select the more suitable algorithm for noise reduction task. Algorithms are compared using total accuracy and Cohen's Kappa coefficient.

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“…Classification is the process of assigning a pixel to a particular type of land cover [24], [25], [26]. Classification uses data which are mathematically known as a measurement vector or feature vector from an acquisition system.…”

Section: Land Cover Classificationmentioning

confidence: 99%

Comparative Analysis of Support Vector Machine, Maximum Likelihood and Neural Network Classification on Multispectral Remote Sensing Data

Ahmad¹,

Hashim²,

Othman³

et al. 2018

ijacsa

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Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN.

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Multispectral Image Analysis using Decision Trees

Cited by 13 publications

References 25 publications

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

Detection of Man-Made Constructions Using LiDAR Data and Decision Trees

Comparative Analysis of Support Vector Machine, Maximum Likelihood and Neural Network Classification on Multispectral Remote Sensing Data

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