Synthetic aperture radar (SAR) images have found numerous applications. However, further analysis of SAR images including interpretation, classification, segmentation, etc. is an extremely challenging task due to the presence of highly intensive speckle noise. Therefore, image despeckling is one of the main stages in preliminary SAR data processing. Over the past decades, a large number of different image despeckling techniques have been proposed ranging from local statistics filters to deep learning based ones. In this study, we analyze one of the most known and widely used local statistics Frost filter. Despeckling efficiency of the Frost filter significantly depends on the sliding window size and tuning (also called damping) factor. Here, we present a method for optimal parameters selection of the Frost filter for a given image based on despeckling efficiency prediction. Despeckling efficiency prediction for the Frost filter is carried out using a set of statistical and spectral input parameters and multilayer neural network. It is shown that such a prediction can be performed before applying image despeckling with a high accuracy and it is faster than despeckling itself. Both simulated speckled images and real-life Sentinel-1 SAR images have been used for extensive evaluation of the proposed method.
Visual quality is important for remote sensing data presented as grayscale, color or pseudo-color images. Although several visual quality metrics (VQMs) have been used to characterize such data, only a limited analysis of their applicability in remote sensing applications has been done so far. In this paper, we study correlation factors for a wide set of VQMs for color images with distortion types typical for remote sensing. It is demonstrated that there are many metrics that have very high Spearman rank order correlation, e.g. PSNR-based and SSIM-based metrics. Meanwhile, there are also metrics that are practically uncorrelated with others. A detailed analysis of VQMs that have the largest SROCC values and belong to different groups is presented in this paper.
Given a suitable dataset, transfer learning using deep convolutional neural networks is an effective method to develop a system to detect and classify objects. Despite having models pretrained on large general-purpose datasets, the requirement to manually label an application-specific dataset remains a limiting factor in system development. We consider this wider problem in the context of the purity analysis of canola seeds, where end users wish to distinguish species of interest from contaminants in images taken with optical microscopes. We use a Detector network, trained only to detect seeds, to help label the dataset used to train an Analyzer network, capable of both seed detection and classification. We present results, over three experiments that involve 25 contaminant species, including Primary and Secondary Noxious Weed Seeds (as per the Canadian Weed Seeds Order), to validate our incremental approach. We also compare the proposed system to competing ones in a literature review.