Mathematical morphology coupled with creation of a time stack image and Principal Oscillation Pattern analysis are used to determine the water depths over a known sloping bottom from synthetic remotely sensed images. The data consisted of 60 images, each 256x256 pixels, separated by 1 second in time. These images are produced by a simulator which creates a noise-free time series of images showi...

Decision fusion is one of hot research topics in classification area, which aims to achieve the best possible performance for the task at hand. In this paper, we investigate the usefulness of this concept to improve change detection accuracy in remote sensing. Thereby, outputs of two fuzzy change detectors based respectively on simultaneous and comparative analysis of multitemporal data are fus...

Supervised classification techniques are commonly used to assign pixels of multispectral satellite imagery to a predefined set of classes in order to generate or update land use or land cover maps from remote sensed data. These techniques have a limited ability in expressing spatial relationships among pixels. We propose a new contextual approach to address this issue. In particular, we present...

The article is devoted to the development of mathematical models based on combining images obtained by remote sensing means with different spatial and radiometric resolutions. An analysis modern sensing, which form that are fixed under same positional conditions projection, in spectral ranges radiation, was carried out. Images formed a wide range have higher linear resolution than narrower rang...

Recently, many deep learning-based methods have been developed for solving remote sensing (RS) scene classification or retrieval tasks. Most of the adopted loss functions training these models require accurate annotations. However, presence noise in such annotations (also known as label noise) cannot be avoided large-scale RS benchmark archives, resulting from geo-location/registration errors, ...

Association Rule Mining, originally proposed for market basket data, has potential applications in many areas. Remote Sensed Imagery (RSI) data is one of the promising application areas. Extracting interesting patterns and rules from datasets composed of images and associated ground data, can be of importance in precision agriculture, community planning, resource discovery and other areas. Howe...

Image inpainting is the process of reconstructing an image or to fill the missed region by using the surrounding pixels so that it looks reasonable to human eye. Sometimes, while capturing the image dead pixels will exists in the image which results in degraded image. In this paper, various algorithms are discussed by using which we can get a smoothed and undegraded image. Keywords—Inpainting, ...

Remote sensing plays a vital role in overseeing the transformations on the earth surface. Unsupervised clustering has a indispensable role in an immense range of applications like remote sensing, motion detection, environmental monitoring, medical diagnosis, damage assessment, agricultural surveys, surveillance etc In this paper, a novel method for unsupervised classification in multitemporal o...

In this article a texture feature extraction scheme based on M-band wavelet packet frames is investigated. The features so extracted are used for segmentation of satellite images which usually have complex and overlapping boundaries. The underlying principle is based on the fact that different image regions exhibit different textures. Since most signifcant information of a texture often lies in...

We propose a novel approach using airborne image sequences for detecting dense crowds and individuals. Although airborne images of this resolution range are not enough to see each person in detail, we can still notice a change of color and intensity components of the acquired image in the location where a person exists. Therefore, we propose a local feature detection-based probabilistic framewo...