IMAGE FUSION TECHNOLOGIES IN
COMMERCIAL REMOTE SENSING
PACKAGES
Firouz Abdullah Al-Wassai
Research Student, Computer Science Dept., (SRTMU), Nanded, India
and N.V. Kalyankar 2
Principal, Yeshwant Mahavidyala College, Nanded, India
Abstract:
Several remote sensing software packages are used to the explicit purpose of analyzing and visualizing remotely sensed data, with the developing of remote sensing sensor technologies from last ten years. According to literature, the remote sensing is still the lack of software tools for effective information extraction from remote sensing data. So, this paper provides a state-ofart of multi-sensor image fusion technologies as well as review on the quality evaluation of the single image or fused images in the commercial remote sensing packages. It also introduces program (ALwassaiProcess) developed for image fusion and classification.
INTRODUCTION
Automatic recognition, description, classification, and grouping of patterns are important problems in a variety of engineering and scientific disciplines such as statistics, computer-aided diagnosis, marketing, computer vision, bio-medicine, and remote sensing. This topic has been extensively studied and applied to several tasks in various areas, with continual evolution of computers and sensors, it has become increasingly important to understand the interactions and associations between data from different sensors and deduce meaningful inferences. Remote sensors offer a wide variety of image data with different characteristics in terms of temporal, spatial, radiometric and spectral resolutions. Very high spatial resolution enables an accurate description of shapes, features and structures while high spectral resolution enables better identification and discrimination of the features based on their spectral response in each of the narrow bands. A current remote sensor offers multispectral sensors and advanced multi-spectral sensors called hyper-spectral sensors, it detects hundreds of very narrow spectral bands throughout the visible, nearinfrared, and mid-infrared portions of the electromagnetic spectrum. also, high spatial ranges from (0.5m10m) in remote sensing commercial domain such as IKONOS, QuickBird, OrbView-3 and SPOT-5…etc. despite that more than 80% of the modern earth observation satellite sensors and many airborne digital cameras simultaneously, offers a tradeoffs between high spatial and high spectral resolution, and no single system offers both (i.e. collects high-resolution panchromatic (Pan) and low-resolution multispectral (MS) images or the opposite). For example TM Sensors on board the Landsat-series of satellites imagery has significant advantage in 6 spectral wavebands but is very poor in spatial resolution (30m) for certain applications, whereas The HRV sensors on board the SPOT-series of satellites performed very well in spatial resolution 10m but with low-spectral resolution, as a single-channel PAN image and three multispectral images at spatial resolution 20m. In order to automate the processing of these satellite images the concepts for image fusion are needed.
The term “image fusion” covers multiple techniques used to combine the geometric detail of a high-resolution panchromatic image and the colour information of a lowresolution multispectral image to produce a final image with the highest possible spatial information content, while still preserving good spectral information quality. Multi-sensor image fusion is widely recognized as an efficient tool for improving overall performance in image based application. Some of the common applications of image fusion includes; visual display enhancement, pattern recognition, machine learning, Object separation[1], texture information[2] image categorization and retrieval [3], Classification of Forest [4], and vehicle detection [5.], targets tracking [6], ….etc. The applications themselves usually used to drive the choice of associated with fusion algorithms. Although advancements in image fusion have been made, the research has established that no single fusion algorithm can work for every application; because the sensors of images themselves have varying responses under different operating conditions such as merging images of multi-spectral differ with merging hyper-spectral satellite sensor data. As a result, a lot of variations of image fusion techniques appeared in the lecturer. Thus, associated algorithms are generally tuned toward specific tasks and leading to custom solutions as well as depends upon the user’s experience. Image fusion is only an introductory stage to another task Therefore; the performance of the fusion algorithm must be measured in terms of improvement or image quality to evaluate the possible benefits of fusion. With increasingly sophisticated algorithms, the need for a cost-effective and standardized on designing software for image Fusion and quality evaluate the possible benefits of fusion.
Recently, quite a few survey papers have been published, providing overviews of the history, developments, Evaluation, and the current state of the art of image fusion in the image-based application fields [7- 11]. But recent development of multi-sensor data fusion in remote sensing software packages has not been discussed in detail. The objectives of this paper are to preIMAGE FUSION TECHNOLOGIES IN COMMERCIAL REMOTE SENSING PACKAGES Firouz Abdullah Al-Wassai1 and N.V. Kalyankar2 1Research Student, Computer Science Dept., (SRTMU), Nanded, India 2Principal, Yeshwant Mahavidyala College, Nanded, India fairozwaseai@yahoo.com , drkalyankarnv@yahoo.com sent an overview of the image fusion tools in the software packages are available for the explicit purpose for visualizing of remote sensing images. Focuses mostly on image fusion and quality evaluation of the single image or fused images components of these packages. The subsequent sections of this paper are organized as follows: section II gives the brief overview of the textbooks with their software implementation. III covers the review on Image Processing Systems; IV This section gives a quick overview the program ALwassaiProcess for image fusion and classification and is subsequently followed by the conclusion
Conclusion
In this way the paper presenting the review on the some textbooks that are implementations the software and some commercial remote sensing packages which are working for image processing and analysis. The results of this review in the textbooks implementation software the image fusion was absent mostly there and the most of commercial remote sensing packages have been widely offer the popularity of fusion algorithms (i.e. PCA, IHS, HPF, BT and WV), despite a lot of variations of image fusion techniques. Are these methods in these products better performance than can be achieved by other techniques to make image fusion practitioners suitable or accurate enough to use remote sensing specialists communicated efficiently?
However, with the popularity of commercial remote sensing packages, it is easy to obtain processed remote sensing products based on fusion algorithms or Classification. But, it is noteworthy that these products may not be suitable or accurate corresponding to the conditions of image fusion to use. Where the result of fusion process is depends on its many applications, so we should to evaluate the benefits of fusion. Therefore, it is still urgent needed to make image fusion practitioners efficiently. Also, the result of review on commercial remote sensing packages for image evolution is absent there. So, we introduce our program which that it covers 15 techniques of image fusion to answer the previous ques- Fig. 6 Snapshots Illustrated Some Functions for Classification. Fig. 7a Fig. 7b Fig. 7(a, b) Snapshots Illustrated Automatic quality assessment for Some Functions. Fig. 8a Fig. 8b Fig. 8 Snapshots Illustrated Some Functions such as Filtering, Transformation and Batch Convert file. tion. Also, introduces maximums tools in the program which are needed in image fusion and classification as well as provide tools for automatic quality assessment implemented in [23-25, 31-33]
Finally, we hope that this program will be helpful in the future to them who want to study in this field and further investigations are necessary to develops image fusion algorithms.
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