Land Consumption Classification Using Sentinel 1 Data: A Systematic Review
Abstract
:1. Introduction
Goal of This Study
2. Background Analysis
2.1. Fundamentals on Land Consumption
“a phenomenon associated with the loss of an important environmental resource: agricultural, natural, or semi-natural land. The phenomenon refers to an increase of the artificial covering of the ground, due to settlement dynamics. It is defined as a change from a non-artificial covering (unconsumed land) to an artificial covering of the soil (consumed land)”
2.2. Fundamentals on SAR Images and Sentinel-1 Mission
3. Materials and Methods
- Type of analysis conducted (e.g., mapping, classification or change detection);
- Software used (e.g., SNAP, ENVI, etc.);
- Methodologies implemented (e.g., combining Radar and Geospatial Big Data, integrating SAR and Optical data, considering Sentinel-1 features, etc.);
- Algorithms applied (e.g., supervised/unsupervised Machine Learning (ML) algorithms, Bayesian Algorithms, Object/Pixel-Based learning algorithms, etc.);
- Study areas considered (e.g., local/city scale, national scale, global scale, etc.);
- Dataset considered (e.g., orbits, spatial and temporal resolution, number of images, etc.);
- Results obtained (e.g., overall accuracy, k-index, producer accuracy, visual analysis accuracy, etc.);
- Potential issues and gained benefits.
4. Analysis of Existing Approaches to Map Land Consumption with Sentinel-1 Images
4.1. Study Area, Dataset, and Software
4.2. Methodologies
- Type A—Land consumption mapping combining SAR and Geospatial Big Data;
- Type B—Land consumption change detection;
- Type C—Land consumption mapping using data fusion or data integration;
- Type D—Land consumption mapping using approaches considering only Sentinel-1 image features.
4.2.1. Type A—Land Consumption Mapping Combining SAR and Geospatial Big Data
4.2.2. Type B—Land Consumption Change Detection
4.2.3. Type C—Land Consumption Mapping Using Data Fusion or Data Integration
4.2.4. Type D—Land Consumption Mapping Using Approaches Considering Only Sentinel-1 Image Features
Paper | Type | Method | Mode | Algorithms/ Techniques | Accuracy Assessment | Advantages | Disadvantages |
---|---|---|---|---|---|---|---|
[63] | A | Object-based | Supervised | Segmentation algorithm Decision tree RF | OA: 91.55% K: 0.89 | Reduced confusion between greenhouses and vegetation thanks to the population density (in urban areas higher than in vegetation areas). | Availability of Social Media data. |
[17] | A–C | Pixel-based | Supervised | RF; K-means | OA: various | Possible use for global scale. | Several accuracies (different cities considered). Availability of global population data (GHS-POP). Low accuracy if considering cities different from trained ones. |
[1] | B | Pixel-based | Unsupervised | Bayesian algorithm | Visual analysis | This approach is simple, unsupervised, automatic, and feasible for users who are not experts in the field. | Changes are not detected whenever the new objects are characterized by low amplitude, such as a street, a square, or a set of solar panels. |
[56] | B | Pixel-based | Unsupervised | NDVI applicated to optical images; Median backscattering; Photointerpretation | OA: 97.7–99.66% | High accuracy. Global scale. | Arbitrariness of thresholds. |
[70] | B | Pixel-based | Unsupervised | MDADT | Visual analysis | Use only SAR data, consider large area, and long time period. | Only detected medium-term land cover changes. Small changes were not recorded due to the spatial resolution of Sentinel-1 images. |
[61] | B–C | Pixel-based | Supervised | SVM with radial bases | S1: OA = 96% K = 0.942 S1-S2: OA = 96% K = 0.942 | Data are integrated and harmonized, thus obtaining a very accurate result for a city with a complex and heterogeneous urban pattern. These new products offer support for conducting spatial and statistical analysis for changes that occurred in the urban landscape. | Limited number of classes that can be extracted due to the similar spectral signature of some classes. |
[55] | B | Pixel-based | Unsupervised | NDVI applicated to optical images; Median backscattering; Photointerpretation | OA: various | Global scale. No training areas. Time and cost-effective. | Omissions are mainly related to the smallest changes, which are less than one pixel in size. Could be detected false or not permanent changes. |
[64] | C | Pixel-based | Supervised | RF | OA: 85–98% K: 0.8–1 | The technique would mainly benefit urban areas with open spaces within the settlement due to higher spatial resolution of Sentinel data despite the urban density. | To replicate the process in tropical regions would be challenging, being S-2 data constantly contaminated with cloud covers. For a larger study area, the method can be computationally time consuming. |
[71] | C | Pixel-based | Supervised | SVM-CK | OA = 92.12% K = 0.89 | The fusion of Sentinel-2B MSI and Sentinel-1A SAR data efficiently improve land cover classification in cloud-prone regions. | Possible misclassification. |
[65] | C | Pixel-based | Supervised | MKSVR | RMSE: 0.2031 R2: 0.8321 | Improved accuracy compared with the same method using optical image alone. | Setting parameters. Computational efficiency. |
[66] | C | Pixel-based | Supervised | RF | S2:OA: 95% S1-S2:OA: 91% | - | Salt and pepper effect improved the mixed image classification, and overall accuracy and k coefficient have been reduced to respect multispectral S2 classification. |
[72] | C | Object-based | Supervised | S1: MLC, RF S2: SLIC | OA: 90% | Free and open access data. The ability to follow high-resolution details in a mixed environment. A low number of calibration parameters is required, reducing tuning sensitivity. | Urban pixels surrounded by many not-urban pixels will be misclassified. Inconsistencies of Sentinel 2. Quite complex approach. |
[78] | C | Pixel-based | Supervised | SML | Visual analysis | The capacity to handle different sets of input features, such as radiometric, textural, and morphological descriptors. The distinction between built-up areas and fluvial gravel, similar in terms of radiometric characteristics but having different surface roughness becomes possible. | Highways are not detected. Underestimate the built-up surfaces in high density areas where shadows hamper the automated classification. |
[57] | C | Object-based | Unsupervised | S2: Super-pixel segmentation; S1: Fuzzy C-Mean | OA: 88–95% K: 0.58–0.61 | The SAR features are tuned to detect high concentrations of permanent scatterers and stable targets. Unsupervised classification: no training dataset used, making the proposed solution applicable worldwide. Frequently updated. | Object-based approach limits the size of the smallest detail. Roads are narrow surfaces without any double-bounce scattering mechanisms (usually) and are surrounded by decorrelating targets, causing difficulties in their classification. |
[83] | C | Pixel-based | Supervised | RF | F1 score: 0.81–0.98; K-fold Cross Validation (CV): 0.90–0.95 | RF class probabilities were post-processed using a simple mean filter with a 3 × 3 window size. This allowed a partial removal of noise, illumination artifacts, and roads. | Availability of data since the long period considered. Lots of training samples. |
[68] | C | Pixel-based | Supervised | RF | OA: 99% K: 0.98 | High accuracy. | Impact of seasonality in urban land cover mapping. |
[60] | C | Pixel-based | Supervised | RF | OA: 92–95% K: 0.88–0.92 | High accuracys. | Slight underestimation of impervious surface for the city. |
[59] | C | Pixel-based | Supervised | UEXT | OA: 75-82% K: 0.5–0.65 | Overcome limits in mountainous regions, often erroneously identified as urban structures. Reduces number of false positives. Low computational demand. Higher repeatability over a long time. | Low accuracy. |
[69] | C | Pixel-based | Supervised | Multi-Attention Module Hybrid CNN (MAMHybridNet) | OA: 98.87 K: 0.98 | Good performance in high surface humidity, cloud cover, and foggy weather. High accuracy. | Quite complex model. |
[49] | D | Pixel-based\ | Unsupervised | MLC; ISODATA + Texture analyses | MLC: K < 0.80 ISO-TEX: K > 0.80 | Higher accuracy level compared to the supervised classification. | MLC: The confusions between the urban and non-urban were detected in the high backscatter areas. The disparities occur especially on more extended, excessively humid, or bare soil areas. ISO-TEX: the spectral response was explored on all 20 bands by land use classes that are narrow enough to capture a particular classification problem. |
[74] | D | Pixel-based | Unsupervised | Hierarchical Split Based Approach | Visual analysis | Reduces shadow and layover areas. Removes the permanent water bodies. Reduce false alarms. | Limited geometric resolution of the Sentinel satellites with respect to other commercial satellites. |
[85] | D | Pixel-based | Unsupervised | Hierarchical Spilt Based thresholding approach (HSBA) | OA: 91–97% | Reduces the speckle without losing spatial resolution. | Limited geometric resolution of the Sentinel satellites with respect to other commercial satellites. |
[58] | D | Pixel-based | Supervised | MLC; SVM | SVM: K = 0.72 ML: K = 0.61 | Good performance in separating water bodies. | Better accuracy only in semi-arid areas (due to low atmospheric turbulences). Backscattering coefficient may differ based on the geometric orientation of the objects. |
[62] | D | Pixel-based | Supervised; Unsupervised | MLC; RD; K-means | OA: 85–90% Visual analysis | Completely based on Open data. | Limited geometric resolution of the Sentinel satellites with respect to other commercial satellites. |
[48] | D | Pixel-based | Unsupervised | Hierarchical Spilt Based tresholding approach (HSBA) | OA: 91.55–97.93% K: 0.29–0.47 | Reduces shadow and layover areas. Reduces the speckle without losing spatial resolution. Reduces false alarms. | Variability of accuracies. Limited geometric resolution of the Sentinel satellites with respect to other commercial satellites. |
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Interferometric Wide-Swath Mode (IW) | Wave Mode | Strip Map Mode | Extra Wide-Swath Mode (EW) | ||
---|---|---|---|---|---|
Parameters | Polarization | Dual (HH + HV, VV + VH) | Single (HH, VV) | Dual (HH + HV, VV + VH) | Dual (HH+HV, VV + VH) |
Azimuth resolution | 20 m | 5 m | 5 m | 40 m | |
Ground range resolution | 5 m | 5 m | 5 m | 20 m | |
Azimuth and range looks | Single | Single | Single | Single | |
Products | Level 2 | Ocean | Ocean | Ocean | Ocean |
Level 1 | Single Look Complex | - | Single Look Complex | Single Look Complex | |
Level 0 | Raw data | - | Raw data | Raw data | |
Characteristic | Lifetime | 7 years (consumables for 12 years) | |||
Launch date | 1A—3 April 2014 | 1B—25 April 2016 timeframe | ||||
Launcher/Location | Soyuz, Kourou (both launches) | ||||
Orbit | Near-polar, Sun-synchronous, about 690 km, 12 days repeat cycle | ||||
Orbital period | 98.6 min |
Software | Developer | Application | Description | Related Paper |
---|---|---|---|---|
Copernicus Open Access | European Space Agency (ESA) | Downloading | It provides complete, free, and open access to Sentinel-1, Sentinel-2, Sentinel-3, and Sentinel-5P user products. The self-registration process is automatic and immediate. Registration grants access rights for searching and downloading Sentinels products. Search queries on the products stored on the archive and filtering of results are possible via a full-text search bar, using filters for the different acquisition modes, product types, product levels, and geographical areas. | [57,58,59,60] |
SNAP—Sentinel 1 Toolbox | European Space Agency (ESA) | Preprocessing; Classification | A graphical user interface (GUI) used for both polarimetric and interferometric processing of SAR data. Start to finish processing includes algorithms for calibration, speckle filtering, co-registration, orthorectification, mosaicking, and data conversion. | [17,49,59,60,61,62,63,64,65,66] |
ENVI | Inventory Optimization Solutions (IOS) | Preprocessing; Classification | Software built in IDL, a powerful programming language, allows for easy features and functionality customization to meet unique needs. It makes it easier than ever to read, explore, prepare, analyze, and share information from imagery. | [1,61,67,68,69] |
ERDAS Image | ERDAS, Inc. | Preprocessing; Classification | Used widely for processing remote sensing data since it provides a framework for integrating sensor data and imagery from many sources. It is based on a Hierarchical File Format (HFA) structure. It allows to apply algorithms and validate results (accuracy assessment). | [49] |
Google Earth Imagery | Validating | Google Earth includes many images collected from satellites orbiting the planet. These images come from various satellite companies and are grouped into a mosaic of photographs taken over many days, months and years. It allows to validate results in visual mode | [70,71] | |
Google Earth Engine | Downloading; Preprocessing; Classification | Google Earth Engine combines a multi-petabyte catalogue of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. It is used to detect changes, map trends, and quantify differences on the Earth’s surface. The client libraries provide Python and JavaScript wrappers around our web API. It is free. | [1,17,55,59,61,68,69,70,72] |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mastrorosa, S.; Crespi, M.; Congedo, L.; Munafò, M. Land Consumption Classification Using Sentinel 1 Data: A Systematic Review. Land 2023, 12, 932. https://doi.org/10.3390/land12040932
Mastrorosa S, Crespi M, Congedo L, Munafò M. Land Consumption Classification Using Sentinel 1 Data: A Systematic Review. Land. 2023; 12(4):932. https://doi.org/10.3390/land12040932
Chicago/Turabian StyleMastrorosa, Sara, Mattia Crespi, Luca Congedo, and Michele Munafò. 2023. "Land Consumption Classification Using Sentinel 1 Data: A Systematic Review" Land 12, no. 4: 932. https://doi.org/10.3390/land12040932
APA StyleMastrorosa, S., Crespi, M., Congedo, L., & Munafò, M. (2023). Land Consumption Classification Using Sentinel 1 Data: A Systematic Review. Land, 12(4), 932. https://doi.org/10.3390/land12040932