Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images

Abstract

Land cover classification that uses very high resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, the accuracy and usability of classification systems can still be improved. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed to improve the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image, and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. (1) An adaptive region is generated by gradually extending the region around a central pixel based on two predefined parameters (T1 and T2) to utilize the spatial feature of ground targets in a VHR image. (2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting. Each initial classified map is refined in this manner pixel by pixel. (3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated with two remote sensing images with high spatial resolutions of 1.0 m and 1.3 m. Compared with the classical majority voting method and a relatively new post-processing method called the general post-classification framework, the proposed D-AMVS can achieve a land cover classification map with less noise and higher classification accuracies.

Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

“…Because the study site images are large in size (i.e., 4717 × 4508 pixels and 3879 × 3344 pixels for the first and the second datasets, respectively), it was difficult to digitize all the changes occurring in the sites. The study sites were larger (up to 10 times) than those in other related studies [28][29][30][31][32]. Although the conditions for conducting the CD were limited, we could demonstrate the effectiveness of the proposed approach by achieving the improvement in accuracy as compared to other existing methods.…”

“…To evaluate the performance of the proposed OBCD method, three PBCD results (i.e., CVA [15], IRMAD [40], and PCA [41]) prior to fusion and their extended OBCD results using the majority voting technique [28] were generated for a comparison purpose. Additionally, three PBCD results were combined with the dual majority voting technique to generate the OBCD result [29]. When generating the OBCD results, the same segmentation image was employed for the proposed method (i.e., scale, shape, and compactness parameter values set to 500, 0.1, and 0.5, respectively).…”

“…Instead of using a pixel as the basic unit for CD, using an object, which is a group of pixels that are spatially adjacent and spectrally similar to each other [23], as a basic unit can be a solution for minimizing problems with the VHR image CD [24]. Object-based change detection (OBCD) extracts meaningful objects by segmenting input images and, thus, is consistent with the original idea of using CD to identify differences in the state of an observed object or phenomenon [1,25,26].Based on our review of the literature, the OBCD methods can be categorized into two groups: (1) fusing spatial features, which takes into consideration of the texture, shape, and topology features of the objects, in the process of change analysis [23,24,27]; and (2) utilizing the object as the process unit to improve the completeness and accuracy of the final result [28][29][30]. Many studies have focused on using the spatial features of the objects.…”

“…Therefore, no single method can achieve satisfactory performance. Several PBCD results were thus adaptively combined with the object-level operations using the majority voting strategy [19,29,30]. However, when fusing multiple CD results, uncertainty will remain the primary problem.…”

“…Based on our review of the literature, the OBCD methods can be categorized into two groups: (1) fusing spatial features, which takes into consideration of the texture, shape, and topology features of the objects, in the process of change analysis [23,24,27]; and (2) utilizing the object as the process unit to improve the completeness and accuracy of the final result [28][29][30]. Many studies have focused on using the spatial features of the objects.…”