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| Klasifikasi Penginderaan Jauh Ruang-Waktu× | Klasifikasi Penginderaan Jauh× | |
|---|---|---|
| Bidang | Analisis Spasial | Analisis Spasial |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 1980s-2000s | 1970s–present |
| Pencetus≠ | Woodcock, Zhu, and remote sensing community | Swain & Davis (1978); Lillesand & Kiefer (classical textbook treatments) |
| Tipe≠ | Multi-temporal image classification | Supervised / unsupervised image classification |
| Sumber perintis≠ | Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370-384. DOI ↗ | Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation (7th ed.). Wiley. ISBN: 978-1118343289 |
| Alias | multi-temporal remote sensing classification, spatio-temporal image classification, temporal remote sensing analysis, STRSC | land cover classification, image classification, satellite image classification, spectral classification |
| Terkait | 4 | 4 |
| Ringkasan≠ | Space-Time Remote Sensing Classification extends standard image classification to multi-temporal satellite or aerial imagery, enabling analysts to track land cover change, phenological cycles, and environmental dynamics across both space and time. By incorporating the temporal dimension, classifiers achieve higher accuracy and can detect transitions that a single-date analysis would miss. | Remote sensing classification assigns discrete thematic labels — such as forest, urban, water, or cropland — to pixels in a satellite or aerial image based on their spectral, spatial, and temporal properties. It underpins land-use/land-cover mapping, change detection, environmental monitoring, and disaster response at local to global scales. |
| ScholarGateSet data ↗ |
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