An Application of Combined Neural Networks to Remotely Sensed Images

Studies in the area of pattern recognition have indicated that in most cases a classifier performs differently from one pattern class to another. This observation gave birth to the idea of combining the individual results from different classifiers to derive a consensus decision. This work investigates the potential of combining neural networks to remotely sensed images. A classifier system is built by integrating the results of a plurarity of feed-forward neural networks, each of them designed to have the best performance for one class. Fuzzy Integrals are used as the combining strategy. Experiments carried out to evaluate the system, using a satellite image of an area undergoing a rapid degradation process, have shown that the combination may yield a better performance than that of a single neural network

SAR TO OPTICAL IMAGE SYNTHESIS FOR CLOUD REMOVAL WITH GENERATIVE ADVERSARIAL NETWORKS

Optical imagery is often affected by the presence of clouds. Aiming to reduce their effects, different reconstruction techniques have been proposed in the last years. A common alternative is to extract data from active sensors, like Synthetic Aperture Radar (SAR), because they are almost independent on the atmospheric conditions and solar illumination. On the other hand, SAR images are more complex to interpret than optical images requiring particular handling. Recently, Conditional Generative Adversarial Networks (cGANs) have been widely used in different image generation tasks presenting state-of-the-art results. One application of cGANs is learning a nonlinear mapping function from two images of different domains. In this work, we combine the fact that SAR images are hardly affected by clouds with the ability of cGANS for image translation in order to map optical images from SAR ones so as to recover regions that are covered by clouds. Experimental results indicate that the proposed solution achieves better classification accuracy than SAR based classification

Lem benchmark database for tropical agricultural remote sensing application.

Abstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data

Detecção automática de estradas não pavimentadas em imagens de média resolução.

Este trabalho propõe um novo método de detecção automática de estradas não pavimentadas a partir de imagens multiespectrais de sensores remotos, de baixa e média resolução. A área de estudo localiza-se no município de Alcinópolis, nordeste do estado de Mato Grosso do Sul, região com alta susceptibilidade a erosão, que é parte da sub-bacia do Rio Taquari, contribuinte do Pantanal e afluente do rio Paraguai. Foi utilizada uma cena do satélite LANDSAT TM da área de estudo e a validação dos resultados foi realizada com dados de campo colhidos com GPS um mês após a captura das imagens

Evaluating different water-land-boundary approximations to improve sar-derived digital elevation models

The coastline of the German Wadden Sea is constantly subjected to the tides and the tidal-induced environmental changes like erosion and accumulation of sediments need to be monitored constantly. This task requires digital elevation models (DEMs), which are derived from remote sensing data. To model those DEMs, a separation of data collected over landmasses and water bodies is required. In the GeoWAM project the potential of airborne SAR-data (F-SAR) is investigated for monitoring purposes in the Wadden Sea. As part of the project, this paper focuses on the suitability of F-SAR data regarding the derivation of water-land-boundaries (WLBs). Therefore, water-land-boundaries based on independent data sets are compared and evaluated. Analyzed data sets include data collected via F-SAR, airborne laserscanning (ALS), on site GNSS measured WLB points and sea-level data from two acoustic gauges. The algorithms were tested on a study site on Spiekeroog island. Our results show, that the accuracies of the derived WLBs mostly depend on the on-site topography and sediments. The spatial deviation between the reference data and the approximated WLBs is mostly less than 2 m horizontally and 0.15 m vertically. Identified challenges to overcome are mostly related to processing of F-SAR data in areas with highly water saturated sediments. Our results suggest, that F-SAR data in tidal flats is not necessarily dependent on further supplementing surveys, as one of the main advantages of the F-SAR data is the potential to derive DEMs and WLBs from the same data set

Crop type recognition based on Hidden Markov Models of plant phenology

Abstract This work introduces a Hidden Markov Model (HMM) base

Um método para modelagem de conhecimento multitemporal no processo de classificação automática de imagens de sensores remotos.

O presente trabalho propõe um procedimento para a modelagem de conhecimento multitemporal e sua integração com outras formas de conhecimento num ambiente integrado para a interpretação automática de imagens de sensores remotos. A proposta consiste em utilizar diagramas de transição de estado para expressar a relação entre a classe de um objeto num dado momento e a classe do mesmo objeto num instante anterior