Pixel-based Change Detection Research Articles

An Object-Based Basic Farmland Change Detection Using High Spatial Resolution Image and GIS Data of Land Use Planning

With the rapid development of urbanization in China, an effective and quick approach usedto identify changes in basic farmland becomes more and more important in land use resource managementfield. While a variety of change detection approaches using multi-temporal satellite image havebeen reported, few approaches using GIS data of land use planning and single-temporal high spatialresolution satellite image have been reported. This paper proposes an object-based basic farmlandchange detection approach using single-temporal high spatial resolution satellite image and GIS dataof land use planning. Compared with the pixel-based change detection approach, the object-based approachmust be more suitable for in high spatial resolution images. To test the validity of the proposedapproach, we apply it to the actual data, and primary results reveal that the proposed approach is valid.

An Object-Based Basic Farmland Change Detection Using High Spatial Resolution Image and GIS Data of Land Use Planning

With the rapid development of urbanization in China, an effective and quick approach usedto identify changes in basic farmland becomes more and more important in land use resource managementfield. While a variety of change detection approaches using multi-temporal satellite image havebeen reported, few approaches using GIS data of land use planning and single-temporal high spatialresolution satellite image have been reported. This paper proposes an object-based basic farmlandchange detection approach using single-temporal high spatial resolution satellite image and GIS dataof land use planning. Compared with the pixel-based change detection approach, the object-based approachmust be more suitable for in high spatial resolution images. To test the validity of the proposedapproach, we apply it to the actual data, and primary results reveal that the proposed approach is valid.

Deforestation in the Miombo woodlands: a pixel-based semi-automated change detection method

Most methods of change detection require a considerable amount of effort and expertise. The procedures of change detection are visual-, classification-, object- or vector-based. The target of this research was to develop an automated and generally unsupervised combination of methods to quantify deforestation on a per pixel basis. The study area was the Gutu district in Zimbabwe. In the first step, Landsat Thematic Mapper (TM) scenes were spectrally unmixed by the Spectral Mixture Analysis (SMA). The calculation of the necessary endmembers was performed by means of the N-FINDR algorithm. After the unmixing process, the data were analysed with change vector analysis (CVA) utilizing spherical statistics. Thereafter, a combination of constraints, including a Bayesian threshold and spherical angles, was applied to identify deforestation. The combination of these methods provided an accurate idea of the state of deforestation and enabled attribution to ‘fire-induced’ and ‘non fire-induced’ classes.

Comparison of Geo-Object Based and Pixel-Based Change Detection of Riparian Environments using High Spatial Resolution Multi-Spectral Imagery

The objectives of this research were to (a) develop a geo-object-based classification system for accurately mapping riparian land-cover classes for two QuickBird images, and (b) compare change maps derived from geo-object-based and per-pixel inputs used in three change detection techniques. The change detection techniques included post-classification comparison, image differencing, and the tasseled cap transformation. Two QuickBird images, atmospherically corrected to at-surface reflectance, were captured in May and August 2007 for a savanna woodlands area along Mimosa Creek in Central Queensland, Australia. Concurrent in-situ land-cover identification and lidar data were used for calibration and validation. The geo-object-based classification results showed that the use of class-related features and membership functions could be standardized for classifying the two QuickBird images. The geo-object-based inputs provided more accurate change detection results than those derived from the pixel-based inputs, as the geo-object-based approach reduced mis-registration and shadowing effects and allowed inclusion of context relationships.

Structural Damage Assessments from Ikonos Data Using Change Detection, Object-Oriented Segmentation, and Classification Techniques

Recent improvements in the spatial resolution of commercial satellite imagery make it possible to apply very highresolution (VHR) satellite data for assessing structural damage in the aftermath of humanitarian crises, such as, armed conflicts. Visual interpretation of pre- and post-crisis very high-resolution satellite imagery is the most straightforward method for discriminating structural damage and assessing its extent. However, the feasibility of using visual interpretation alone diminishes in the cases of large and dense urban settlements and spatial resolutions in the range of 2 m to 3 meters and larger. Visual interpretation can be further complicated at spatial resolutions greater than 1 m if accompanied by shadow formation and differences in sensor and solar conditions between the pre- and post-conflict images. In this study, we address these problems through investigating the use of traditional change techniques, namely, image differencing and principle component analysis, with an object-oriented image classification software, e-Cognition. Pre-conflict Ikonos (2 m resolution) images of Jenin in the Palestinian territories and Brest (1 m resolution) in FYROM were classified using the e-Cognition software. Thereafter, the pre-conflict classification was used to guide the classification, using e-Cognition, of the pixel-based change detection analysis. The second part of the study examines the feasibility of using mathematical morphological operators to automatically identify likely structurally damaged zones in dense urban settings. The overall results are promising and show that object-oriented segmentation and classification systems facilitate the interpretation of change detection results derived from very high-resolution (1 m and 2 m) commercial satellite data. The results show that objectoriented classification techniques enhance quantitative analysis of traditional pixel-based change detection applied to very high-resolution satellite data and facilitate the interpretation of changes in urban features. Finally, the results suggest that mathematical morphological methods are a potential new avenue for automatically extracting likely damaged zones from very high-resolution satellite imagery in the aftermath of disasters.