spectral decomposition of time series has a significant role in seismic data processing and interpretation. since the earth acts as a low-pass filter, it changes frequency content of passing seismic waves. conventional representing methods of signals in time domain and frequency domain cannot show time and frequency information simultaneously. time-frequency transforms upgraded spectral decompo...

Spectral unmixing is focused in the identification of spectrally pure signatures, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Mainly focused on the spectral information contained in the hyperspectral images, endmember extraction techniques have recently included spatial information to achieve more accurate results. Several algorithms have been d...

The detection of camouflaged objects is important for industrial inspection, medical diagnoses, and military applications. Conventional supervised learning methods for hyperspectral images can be a feasible solution. Such approaches, however, require a priori information of a camouflaged object and background. This letter proposes a fully autonomous feature selection and camouflaged object dete...

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investi...

Variational pansharpening can enhance the spatial resolution of a hyperspectral (HS) image using a high-resolution panchromatic (PAN) image. However, this technology may lead to spectral distortion that obviously affect the accuracy of data analysis. In this article, we propose an improved variational method for HS image pansharpening with the constraint of spectral difference minimization. We ...

Semi–natural areas are characterized by cultivated and natural areas with continuous transition zones. Especially in the alpine regions, this type of land-cover is predominant. The paper presents a method to classify semi –natural alpine areas by combining the spatial detail of orthophotos with the spectral information of SPOT satellite images. As case study, an alpine region in South Tyrol (It...

Hyperspectral images (HSIs) are characterized by high spatial resolution and rich in spectral information. In the process of HSI classification, extraction spectral-spatial features directly influences classification results. recent years, hyperspectral method based on convolutional neural networks has demonstrated excellent performance. However, as network structure deepens, degradation occurs...

Remote Sensing systems, particularly those deployed on satellites, provide a repetitive and consistent view of the Earth (Schowengerdt, 2007). To meet the needs of different remote sensing applications the systems offer a wide range of spatial, spectral, radiometric and temporal resolutions. Satellites usually take several images from frequency bands in the visual and non-visual range. Each mon...