The deep learning revolution in computer vision has enabled a potential for creating new value chains for Earth observation that significantly enhances the analysis of satellite data for tasks like land cover mapping, change analysis, and object detection. We demonstrate a deep learning based value chain for the task of mapping vegetation height in the Liwale region in Tanzania using Sentinel-1 and -2 data. As ground truth data we use lidar measurements, which are processed to provide the average vegetation height per Sentinel-2 pixel grid (10 m). We apply the UNet, which is a widely used neural network for segmentation tasks in computer vision, to estimate average vegetation height from the Sentinel data. Preliminary results show that we are able to map the forest extent with high accuracy, with an RMSE of 3.5 m for Sentinel-2 data and 4.6 m for the Sentinel-1 data.

Remote sensing plays a key role in monitoring the quality and coverage of the tropical forests, and for early warning of illegal logging and forest degradation. We propose a hidden Markov model based framework for analyzing multi-source time series of remote sensing images of tropical forests with the aim of detecting changes in the spatial coverage of the forest. Multi-source is supported by the hidden Markov model by applying specific data distributions for each source. The proposed methodology is demonstrated on a time series of Landsat TM and Radarsat-2 quad-pol images covering tropical forest in Tanzania. The results are evaluated by visual inspection of Landsat 5 TM images.

Remote sensing plays a key role in monitoring the quality and coverage of the tropical forests, and for early warning of il legal logging and forest degradation. We propose a hidden Markov model based methodology for analyzing time series of remote sensing images of tropical forests with the aim of detecting changes in the spatial coverage of the forest. Two different methods are investigated; the most likely state sequence and the minimum probability of state error. The pro posed methodology is demonstrated on a time series of Land sat TM images covering tropical forest in Brazil. The results are evaluated by visual inspection, and show that for change detection the most likely state sequence method is recommended.

Compares the performance of topographic analysis and binary analysis for recognition of digits in hydrographic maps. The performance of each method was measured by the correct classification rate of the final symbol recognition step when processing a complete hydrographic map of size 0.45/spl times/0.6 m/sup 2/ with about 35000 digits. The experimental results indicated that binary analysis had a better performance than topographic analysis. Overall, the performance of the binary analysis was acceptable.

This paper investigates how gray scale information can be used in a hydrographic map understanding system to improve the system performance. To process gray scale scanned map images, we have implemented a topographic analysis method and a binary analysis method. In addition, deconvolution of the gray scale map image was used as an optional preprocessing step for both the methods. Both the methods process the input image by extracting binary print components, recognizing long lines, splitting touching digits and recognizing the digits. The topographic analysis extracts the information by computing topographic labels for each pixel, while the binary analysis is based on locally adaptive thresholding of the gray scale image. The performance of each method was evaluated by measuring the recognition performance of the digit recognition module. Experimental results indicate that the computationally intensive deconvolution and topographic analysis does not improve system performance. The same high performance is achieved by binary analysis, provided a high quality locally adaptive binary method is used.

There is a large number of documents, including hand-printed maps, where useful information is lost if binarization is performed on the scanned image before further processing. For such documents, methods which utilize the gray scale values must be used in order to extract as much of the available information as possible. Topographic analysis has been used in the literature to recognize characters directly in the gray scale images. The authors extend the topographic analysis method, so that characters and lines can be extracted from gray scale map images where methods using only the information in the binary image fail.

This paper presents a methodology for evaluation of low-level image analysis methods, using binarization (two-level thresholding) as an example. Binarization of scanned gray scale images is the first step in most document image analysis systems. Selection of an appropriate binarization method for an input image domain is a difficult problem. Typically, a human expert evaluates the binarized images according to his/her visual criteria. However, to conduct an objective evaluation, one needs to investigate how well the subsequent image analysis steps will perform on the binarized image. We call this approach goal-directed evaluation, and it can be used to evaluate other low-level image processing methods as well. Our evaluation of binarization methods is in the context of digit recognition, so we define the performance of the character recognition module as the objective measure. Eleven different locally adaptive binarization methods were evaluated, and Niblack's method gave the best performance.

This paper presents an evaluation of eleven locally adaptive binarization methods for gray scale images with low contrast, variable background intensity and noise. Niblack's method (1986) with the addition of the postprocessing step of Yanowitz and Bruckstein's method (1989) added performed the best and was also one of the fastest binarization methods.>

This paper presents an evaluation of locally adaptive binarization methods for gray scale images with low contrast, variable background intensity and noise. Such low quality occurs frequently in utility maps and excludes the use of global binarization methods. Only robust locally adaptive binarization methods with no need for on-line tuning of the parameters were considered since the gray scale images of utility maps often consist of a billion (10/sup 9/) pixels or more. Eight locally adaptive binarization methods were tested on five different images. The postprocessing step (PS) of Yanowitz and Bruckstein's (1989) method improved all the other best binarization methods. Niblack's (1986) method with PS gave the best performance. Eikvil, Taxt and Moen's (1991) method with PS, and Yanowitz and Bruckstein's method did almost as well. Comparison was also made on the CPU requirement.>