Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach

Hybrid-Input Convolutional Neural Network-Based Underwater Image Quality Assessment

Since precisely sensing the underwater environment is a challenging prerequisite for safe and reliable underwater operation, interest in underwater image processing is growing at a rapid pace. In engineering applications, there are redundant underwater images addressed in real-time on the remotely operated vehicle (ROV). It puts the equipment or operators under great pressure. To relieve this pressure by transmitting images selectively according to the degradation degree, we propose an end-to-end hybrid-input convolutional neural network (HI-CNN) to predict the degradation of underwater images. First, we propose a feature extraction module to extract the features of original underwater images and saliency maps concurrently, which is composed of two branches with the same structure and shared parameters. Second, we design an end-to-end model to predict the quality scores of original images, which consists of a feature extraction module and a prediction module. Finally, we establish a real-world dataset to make the proposed model be duplicated in the practical underwater environment. Through several experiments, we demonstrate that the proposed model outperforms existing models in predicting underwater image quality.

Impact of land use land cover changes on ecosystem service values in the Dire and Legedadi watersheds, central highlands of Ethiopia: Implication for landscape management decision making

Land use land cover change in a landscape is the main driver of degradation in ecosystem goods and services. This study was aimed at analysing the dynamics of the LULC change in the catchments of the water supply reservoirs as well as the impact on the Ecosystem Service Values (ESVs) between 1985 and 2022. The benefit transfer method was used to evaluate ecosystem service value (ESV) changes in response to LULC. The watersheds experienced substantial LULC changes. As a result, the natural vegetation, grasslands, and eucalyptus plantations declined dramatically, while settlements and cultivated lands considerably increased. The global and local ESV estimates show a dramatic decline in ESVs between 1985 and 2022. According to global and local ESV estimates, total ESV in the Legedadi watershed has decreased from approximately US$ 65.8 million in 1985 to approximately US$ 11.9 million in 2022 and from approximately US$ 42.7 million in 1985 to approximately US$ 9.66 million in 2022. According to global and local ESV estimates, total ESV in the Dire watershed decreased from approximately US$ 437 thousand in 1985 to approximately US$ 59 thousand in 2022 and from approximately US$ 225 thousand in 1985 to approximately US$ 36 thousand in 2022. The overall decline in ESV demonstrates that the natural environment is deteriorating as a result of replacement of the natural land cover by other economic land uses. Hence, it is highly recommended that implementing sustainable watershed management practices to halt the dramatic loss of natural ecosystems must be a high priority.

Quality Control for the BPG Lossy Compression of Three-Channel Remote Sensing Images

This paper deals with providing the desired quality in the Better Portable Graphics (BPG)-based lossy compression of color and three-channel remote sensing (RS) images. Quality is described by the Mean Deviation Similarity Index (MDSI), which is proven to be one of the best metrics for characterizing compressed image quality due to its high conventional and rank-order correlation with the Mean Opinion Score (MOS) values. The MDSI properties are studied and three main areas of interest are determined. It is shown that quite different quality and compression ratios (CR) can be observed for the same values of the quality parameter Q that controls compression, depending on the compressed image complexity. To provide the desired quality, a modified two-step procedure is proposed and tested. It has a preliminary stage carried out offline (in advance). At this stage, an average rate-distortion curve (MDSI on Q) is obtained and it is available until the moment when a given image has to be compressed. Then, in the first step, an image is compressed using the starting Q determined from the average rate-distortion curve for the desired MDSI. After this, the image is decompressed and the produced MDSI is calculated. In the second step, if necessary, the parameter Q is corrected using the average rate-distortion curve, and the image is compressed with the corrected Q. Such a procedure allows a decrease in the MDSI variance by around one order after two steps compared to variance after the first step. This is important for the MDSI of approximately 0.2–0.25 corresponding to the distortion invisibility threshold. The BPG performance comparison to some other coders is performed and examples of its application to real-life RS images are presented.

Combined No-Reference Image Quality Metrics for Visual Quality Assessment Optimized for Remote Sensing Images

No-reference image quality assessment is one of the most demanding areas of image analysis for many applications where the results of the analysis should be strongly correlated with the quality of an input image and the corresponding reference image is unavailable. One of the examples might be remote sensing since the transmission of such obtained images often requires the use of lossy compression and they are often distorted, e.g., by the presence of noise and blur. Since the practical usefulness of acquired and/or preprocessed images is directly related to their quality, there is a need for the development of reliable and adequate no-reference metrics that do not need any reference images. As the performance and universality of many existing metrics are quite limited, one of the possible solutions is the design and application of combined metrics. Several possible approaches to their composition have been previously proposed and successfully used for full-reference metrics. In the paper, three possible approaches to the development and optimization of no-reference combined metrics are investigated and verified for the dataset of images containing distortions typical for remote sensing. The proposed approach leads to good results, significantly improving the correlation of the obtained results with subjective quality scores.

Qualitative Rating of Lossy Compression for Aerial Imagery by Neutrosophic WASPAS Method

The monitoring and management of consistently changing landscape patterns are accomplished through a large amount of remote sensing data using satellite images and aerial photography that requires lossy compression for effective storage and transmission. Lossy compression brings the necessity to evaluate the image quality to preserve the important and detailed visual features of the data. We proposed and verified a weighted combination of qualitative parameters for the multi-criteria decision-making (MCDM) framework to evaluate the quality of the compressed aerial images. The aerial imagery of different contents and resolutions was tested using the transform-based lossy compression algorithms. We formulated an MCDM problem dedicated to the rating of lossy compression algorithms, governed by the set of qualitative parameters of the images and visually acceptable lossy compression ratios. We performed the lossy compression algorithms’ ranking with different compression ratios by their suitability for the aerial images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The novelty of our methodology is the use of a weighted combination of different qualitative parameters for lossy compression estimation to get a more precise evaluation of the effect of lossy compression on the image content. Our methodology includes means of solving different subtasks, either by altering the weights or the set of aspects.