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Multi - scale segmentation is a key step in the information extraction of high - resolution remote sensing images. However, the evaluation of segmentation quality and the quantification of segmentation errors are still challenging. Based on boundary strength information, this study developed an unsupervised segmentation evaluation method of selecting the optimal scale parameter and elevating the local segmentation quality for multi - scale remote sensing image segmentation. Segmentation errors include over - segmentation and under - segmentation. This study modeled the two types of emirs using normalized boundary gradient characteristics. The gradient information of patch edges was considered in the estimation of over - segmentation errors, while the intra - patch gradients were employed for the assessment of under - segmentation errors. To validate the proposed method, this study conducted an experiment on the evaluation of multi - scale segmentation results using two scenes of high - resolution remote sensing images. The segmentation evaluation results of the method coincided perfectly with the actual segmentation effects. The results indicate that the method proposed in this study can effectively reflect over - and under -segmentation errors. [ABSTRACT FROM AUTHOR]

Reliable information of croplands has useful implications for agriculture. Based on random forest classifier, a cropland extraction method was developed. Multi-temporal image data of Landsat 8 were used for classifying crop and non-crop vegetation, since these data contain useful information. However, there is also large redundancy. To solve this problem, a new feature selection method was proposed in this paper. The primary innovativeness resides in a temporal feature selection criterion. A classification experiment was carried out to validate the proposed technique. The results showed that the proposed method can decrease feature redundancy with little cost on classification performance. [ABSTRACT FROM AUTHOR]

With the increasing popularity of object-based image analysis (OBIA) since 2006, numerous classification and mapping tasks were reported to benefit from this evolving paradigm. In these studies, segments are firstly created, followed by classification based on segment-level information. However, the feature space formed by segment-level feature variables can be very large and complex, posing challenges to obtaining satisfactory classification performance. Accordingly, this work attempts to develop a new feature selection approach for segment-level features. Based on the principle of class-pair separability, the segment-level features are grouped according to their types. For each group, the contribution of each segment-level feature to the separation of a pair of classes is quantified. With the information of all feature groups and class pairs, the separability ranking and appearance frequency are considered to compute importance score for each feature. Higher importance score means larger appropriateness to select a feature. By using two Gaofen-2 multi-spectral images, the proposed method is validated. The experimental results show the advantages of the proposed technique over some state-of-the-art feature selection approaches: (1) it can better reduce the number of segment-level features and effectively avoid redundant information; (2) the feature subset obtained by the proposed scheme has good potential to improve classification accuracy. [ABSTRACT FROM AUTHOR]