A novel mathematical morphology based algorithm for shoreline extraction from satellite images

Figures & data

Table 1. Specification of data-set.

Sl. No.	Satellite and sensor	Spatial resolution /m	Source agency	Area of interest	Date of procurement (Date of pass)
1.	Cartosat-2, PAN	0.8	NRSC	Mangalore, India	12 NOV 2008
2.	Cartosat-1, PAN (Tile-532-338)	2.5	NRSC	Mangalore southern coast, India	16 FEB 2012
3.	Cartosat-1, PAN (Tile-530-334)	2.5	NRSC	Mangalore northern coast, India	14 NOV 2014
4.	Resource sat-2, LISS IV	5.8	NRSC	Mangalore, India	06 FEB 2016
5.	Landsat 8, ETM+	30.0	USGS	Mangalore northern coast, India	11 JAN 2015

Table

Input: An N×N matrix sized satellite input image (f1).

Output: An N×N matrix sized extracted output image (X).

1. Start

2. Procurement and registration of the satellite image.

3. Pre-processing operations to suppress nonlinear objects and noises such as salt and pepper.

4. SEs (SE1 to SE3), pixel size (P, to remove noises /non shore objects) and threshold (th1 to th5) declarations and initializations for processing activities.

5. Prompt for spatial resolution of input satellite images: input 1 for very high (<1 m), 2 for high (1−5 m), 3 if moderate (5−25 m), 4 if sparse (>25 m).

6. Computation of matching top-hat and bot-hat algebra; (f2 = f1+tophat(f1, SE1)) − bothat(f1, SE1).

7. Opening by reconstruction; f3=Rfo(f2, SE2).

8. Thresholding

If (auto threshold) then

switch Res

case ‘1’: X1 = im2bw(f3, th1);

case ‘2’: X1 = im2bw(f3, th2);

case ‘3’: X1 = im2bw(f3, th3);

case ‘4’: X1 = im2bw(f3, th4);

otherwise

X1 = im2bw(f3, th5);

End

Else (manual threshold)

prompt = 'Please enter threshold value’

X1 = input(prompt);

X1 = im2bw(f3, X1);

9. Closing by reconstruction; X = Rfc(X1, SE3, mmsebox).

10. Binary area open; bwareaopen(X, P), noise removal (non-shores)

11. Display extracted outcome and save resultant imagery.

12. End.

Figure 1. Flowchart of the proposed approach for shoreline extraction.

Figure 2. Illustration of results with Cartosat-1, PAN (Tile-532-338) imagery. (a) Original image, (b) Ground truth, (c) MM processed image (intermediate stage), (d) Classification outcome, (e) Proposed MM algorithm outcome.

Figure 3. Illustration of results with Cartosat-2 imagery (a) Original image, (b) Ground truth, (c) MM processed image (intermediate stage), (d) Classification outcome, (e) Proposed MM algorithm outcome.

Figure 4. Illustration of results with Resource sat-2, LISS IV imagery. (a) Original image, (b) Ground truth, (c) MM processed image (intermediate stage), (d) Classification outcome, (e) Proposed MM algorithm outcome.

Figure 5. Illustration of results with Cartosat 1, PAN (Tile-530-334) imagery. (a) Original image, (b) Ground truth, (c) MM processed image (intermediate stage), (d) Classification outcome, (e) Proposed MM algorithm outcome.

Figure 6. Illustration of results with Landsat ETM + sensor imagery. (a) Original image, (b) Ground truth, (c) MM processed image (intermediate stage), (d) Classification outcome, (e) Proposed MM algorithm outcome.

Table 2. Accuracy assessment of the proposed MM approach.

Sensor and ROI description	TP (feature)	FP	TN (background)	FN	F-score	MCC	Accuracy
Cartosat-2, PAN	470,275	47,498	52,904,280	60,929	0.896 63	0.895 68	0.997 97
Cartosat-1, PAN (Tile-532-338)	285,895	17,426	36,154,604	14,335	0.947 37	0.946 95	0.999 12
Cartosat-1, PAN (Tile-530-334)	240,917	71,419	86,907,650	10,794	0.854 24	0.858 78	0.999 05
Resource sat-2, LISS IV	74,394	6292	19,993,870	16,324	0.868 05	0.868 99	0.998 87
Landsat 8, ETM+	36,497	48,011	27,884,335	3613	0.585 74	0.626 19	0.998 15

Table 3. Accuracy assessment of existing shoreline classification approach.

Sensor and ROI description	TP (feature)	FP	TN (background)	FN	F-score	MCC	Accuracy
Cartosat-2, PAN	509,278	966,121	51,985,657	21,926	0.507 60	0.569 41	0.981 520
Cartosat-1, PAN (Tile-532-338)	256,139	328,471	35,843,559	44,091	0.578 94	0.607 27	0.989 780
Cartosat-1, PAN (Tile-530-334)	216,435	893,029	86,086,040	35,276	0.859 85	0.406 68	0.989 358
Resource sat-2, LISS IV	68,414	207,940	19,792,222	22,304	0.372 75	0.428 11	0.988 539
Landsat 8, ETM+	37,263	246,138	27,686,208	2847	0.230 36	0.215 75	0.974 895