Building extraction from RGB VHR images is an important and popular topic for mapping, disaster emergency responding, and city management. The automation of most methodologies cannot meet the need for applications. In this paper, based on classification and optimization, we propose a novel methodology using shadows to automatically extract building samples and verify buildings accurately to improve automation and accuracy. On one hand, in order to acquire various and reliable building samples automatically for classification, detected shadows first are shifted opposite to the direction of illumination to extract building shadows. Furthermore, each building shadow will be shifted again in the same way. Then according to the distribution of classes in these customized shifted regions, building samples can be filtered out by removing those recognized objects. On the other hand, besides the common measures to optimize the initial building during post-processing; a new, original, and an efficient shadow-based index for building verification is also designed. Shadow rate on the intersect boundary between the expanding edge of candidate regions and their shifted regions following the illumination direction can efficiently recognize buildings. When the proposed method is compared to other sample acquisition methods based on shadow, experimental results show that the approach for building samples acquisition is helpful to get accurate initial building results. Moreover, in comparison with other building extraction methods, the proposed building verification method can distinguish buildings from non-buildings. This significantly improves the accuracy of the final results. Numerical assessments performed on a series of test images indicate that our proposed approach for building extraction is efficient and feasible, especially in suburban areas.

Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.

Current automatic shadow compensation methods often suffer because their contrast improvement processes are not self-adaptive and, consequently, the results they produce do not adequately represent the real objects. The study presented in this paper designed a new automatic shadow compensation framework based on improvements to the Wallis principle, which included an intensity coefficient and a stretching coefficient to enhance contrast and brightness more efficiently. An automatic parameter calculation strategy also is a part of this framework, which is based on searching for and matching similar feature points around shadow boundaries. Finally, a final compensation combination strategy combines the regional compensation with the local window compensation of the pixels in each shadow to improve the shaded information in a balanced way. All these strategies in our method work together to provide a better measurement for customizing suitable compensation depending on the condition of each region and pixel. The intensity component I also is automatically strengthened through the customized compensation model. Color correction is executed in a way that avoids the color bias caused by over-compensated component values, thereby better reflecting shaded information. Images with clouds shadows and ground objects shadows were utilized to test our method and six other state-of-the-art methods. The comparison results indicate that our method compensated for shaded information more effectively, accurately, and evenly than the other methods for customizing suitable models for each shadow and pixel with reasonable time-cost. Its brightness, contrast, and object color in shaded areas were approximately equalized with non-shaded regions to present a shadow-free image.