Many ecological applications (e.g., human activity management in protected areas, conservation status assignment) require actual forest inventory data. However, frequent field research of forest areas is very expensive. Therefore, in practice, forest inventory data are slowly updated, approximately one time per decade. From the other hand, modern remote sensing systems combine high-quality imagery with short revisiting time and can be used for the forest inventory data clarification. Our paper presents an investigation of tree species classification based on seasonal Sentinel-2 data (2018) and the latest forest inventory information (2013–2014). The main advantages of Sentinel-2 satellites are a short revisiting time and a large field of view that is important in large area analysis. Our classification model was based on support vector machines method combined with specific spatial processing methods. We used the known inventory data for training and validation the classifier. Misclassified regions were further analyzed in ground surveys to produce the inventory data clarification. The paper addresses the optimal image dates selection, image preprocessing and classification procedure evaluation issues. The study was carried out for the territory of the Krasnosamarskoe forestry in Samara region, Russia. The experiments have shown that the proper Sentinel-2 data selection and classification procedure configuration allow reaching the classification accuracy of about 0.82 for the control sample. The ground survey confirmed that classification errors are mainly caused by the dominant tree species changes. Thus, we concluded that Sentinel-2 data can be effectively used for the forest inventory data clarification.
In this paper we propose an automatic technology for extraction of latent images from printed media such as documents, banknotes, financial securities, etc. This technology includes image processing by adaptively constructed Gabor filter bank for obtaining feature images, as well as subsequent stages of feature selection, grouping and multicomponent segmentation. The main advantage of the proposed technique is versatility: it allows to extract latent images made by different texture variations. Experimental results showing performance of the method over another known system for latent image extraction are given.
This paper presents a new method for high-capacity information hiding in digital video and algorithms of embedding and extraction of hidden information based on this method. These algorithms do not require temporal synchronization to provide robustness against both malicious and non-malicious frame dropping (temporal desynchronization). At the same time, due to randomized distribution of hidden information bits across the video frames, the proposed method allows to increase the hiding capacity proportionally to the number of frames used for information embedding. The proposed method is also robust against “watermark estimation” attack aimed at estimation of hidden information without knowing the embedding key or non-watermarked video. Presented experimental results demonstrate declared features of this method.
This paper describes a study to find the best error diffusion kernel for digital halftoning under various restrictions on the number of non-zero kernel coefficients and their set of values. As an objective measure of quality, WSNR was used. The problem of multidimensional optimization was solved numerically using several well-known algorithms: Nelder– Mead, BFGS, and others. The study found a kernel function that provides a quality gain of about 5% in comparison with the best of the commonly used kernel introduced by Floyd and Steinberg. Other kernels obtained allow to significantly reduce the computational complexity of the halftoning process without reducing its quality.