Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Tao Zhou, Zhaofu Li, Jianjun Pan

Sensors 2018 article cited by 46

Source Open-access PDF More in Climate & Environment

importance 4/10 ai assisted Synthetic Aperture Radar (SAR) Applications and Techniques remote sensing / urban planning stage: research

Problem

Improving urban land-cover classification accuracy using multi-sensor satellite imagery and determining which features contribute most.

Why it matters

Accurate urban land-cover maps inform city planning and environmental monitoring.

What the AI did

A random forest classifier used backscatter intensity, texture, coherence, and color features from Sentinel-1A, Landsat-8 OLI, and Hyperion imagery to classify urban land cover.

Results

Optimal combined-texture window size 9×9; texture features contributed most, then coherence and backscatter intensity; color features least.

Impact

Identified which satellite features most improve multi-sensor urban land-cover classification.

Record

Venue	Sensors
Published	2018
Work type	article
Authors	Tao Zhou, Zhaofu Li, Jianjun Pan
Citations	46 (OpenAlex, at import time)
References	105
Model or method	Random Forest (RF)
Organization	not stated in source
Primary source	https://doi.org/10.3390/s18020373
Open access	gold
Keywords	Sentinel-1A, random forest, land cover, multi-sensor, texture, remote sensing

Importance rating

4 / 10

real but narrower advance

Improves urban land-cover classification methods for city planning and environmental monitoring; a real but methodological, narrow advance with no stated scale of impact.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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