Land Cover Classification using Object-Based Image Analysis of SPOT-6 Imagery for Land Cover and Forest Monitoring in Nagan Raya, Aceh – Indonesia

Abstract

Abstract-Digital classification technique using Object-Based Image Analysis (OBIA) of SPOT-6 imagery could improve classification accuracy and provide detail type of land cover. This method is better applied for data with higher spatial resolution imagery which has high heterogeneity where the pixel size is smaller the actual size of the objects. The sequence steps were image preprocessing and pansharpening, identify the potential of landcover types using Jeffries-Matusita (JM) feature separability, determine a stratification boundary, and developing the Object-Based Orientation classification using Erdas Imagine Objective. Detail process of OBIA was image pixel segmentation with the parameters were consists of determining the minimum pixel segmentation ratio which was 1000 and applying the single feature probability and NDVI, and determine cue weight. The second segmentation was object vector classification which segmented the vector object using the empirical distribution analysis. The segmentation is based on region growing of the Multi-Bayesian network. The classification result is then assessed by comparing the classification result of Maximum Likelihood (MLC) using confusion matrix. The result shows that object-based classification technique could improve the classification by 83% compared to the MLC with only 67%. The method of segmentation used the stratification zone in order to make an optimum cue weight in detecting the object through texture, size, and shape while also applying the spectral-based method.

Leveraging Time-Series Imageries and Open Source Tools for Enhanced Land Cover Classification

“…Many studies have implemented image analysis methods to produce land-cover maps in Indonesia based on visual interpretation, supervised classification, object-based image classification, machine learning, and artificial intelligence [9], [10], [11], [12]. Despite these studies, a consistent, precise, and reproducible land-cover map remains a major obstacle to environmental conservation and land-use planning in Indonesia.…”

Object-Based on Land Cover Classification on LAPAN-A3 Satellite Imagery Using Tree Algorithm (Case Study: Rote Island)

“…Sari and Fildes [7] explored the utilization of OBIA of SPOT-6 symbolism with the plan of building up a land cover guide of Nagan Raya by expected to deliver data at scale 1:25,000 that will improve arrangement exactness than those created utilizing regular visual and ghostly based techniques to help of the Indonesian public timberland checking framework.…”