Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.

Abstract-Synthetic aperture radar (SAR) images are disturbed by a multiplicative noise depending on the signal (the ground reflectivity) due to the radar wave coherence. Images have a strong variability from one pixel to another reducing essentially the efficiency of the algorithms of detection and classification. In this study, we propose to filter this noise with a multiresolution analysis of the image. The wavelet coefficient of the reflectivity is estimated with a Bayesian model, maximizing the a posteriori probability density function. The different probability density function are modeled with the Pearson system of distributions. The resulting filter combines the classical adaptive approach with wavelet decomposition where the local variance of high-frequency images is used in order to segment and filter wavelet coefficients.Index Terms-Adaptive filtering, synthetic aperture radar (SAR), speckle, wavelet.

Speckle noise filtering on polarimetric SAR (PolSAR) images remains a challenging task due to the difficulty to reduce a scatterer-dependent noise while preserving the polarimetric information and the spatial information. This challenge is particularly acute on single look complex images, where little information about the scattering process can be derived from a rank-1 covariance matrix. This paper proposes to analyze and to evaluate the performances of a set of PolSAR speckle filters. The filter performances are measured by a set of ten different indicators, including relative errors on incoherent target decomposition parameters, coherences, polarimetric signatures, point target, and edge preservation. The result is a performance profile for each individual filter. The methodology consists of simulating a set of artificial PolSAR images on which the various filters will be evaluated. The image morphology is stochastic and determined by a Markov random field and the number of scattering classes is allowed to vary so that we can explore a large range of image configurations. Evaluation on real PolSAR images is also considered. Results show that filters performances need to be assessed using a complete set of indicators, including distributed scatterer parameters, radiometric parameters, and spatial information preservation.

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We describe a video indexing system that aims at indexing large video files in relation to the presence of similar faces.